Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light grad...Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.展开更多
Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has prov...Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.展开更多
Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to...Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to propose a fast and accurate model for predicting the longevity of landslide dams while also addressing the issue of missing data.Given the wide variation in the survival times of landslide dams—from mere minutes to several thousand years—predicting their longevity presents a considerable challenge.The study develops predictive models by considering key factors such as dam geometry,hydrodynamic conditions,materials,and triggering parameters.A dataset of 1045 landslide dam cases is analyzed,categorizing their longevity into three distinct groups:C1(<1 month),C2(1 month to 1 year),and C3(>1 year).Multiple imputation and knearest neighbor algorithms are used to handle missing data on geometric size,hydrodynamic conditions,materials,and triggers.Based on the imputed data,two predictive models are developed:a classification model for dam longevity categories and a regression model for precise longevity predictions.The classification model achieves an accuracy of 88.38%while the regression model outperforms existing models with an R^(2) value of 0.966.Two real-life landslide dam cases are used to validate the models,which show correct classification and small prediction errors.The longevity of landslide dams is jointly influenced by factors such as geometric size,hydrodynamic conditions,materials,and triggering events.Among these,geometric size has the greatest impact,followed by hydrodynamic conditions,materials,and triggers,as confirmed by variable importance in the model development.展开更多
Accurate lithofacies classification in low-permeability sandstone reservoirs remains challenging due to class imbalance in well-log data and the difficulty of the modeling vertical lithological dependencies.Traditiona...Accurate lithofacies classification in low-permeability sandstone reservoirs remains challenging due to class imbalance in well-log data and the difficulty of the modeling vertical lithological dependencies.Traditional core-based interpretation introduces subjectivity,while conventional deep learning models often fail to capture stratigraphic sequences effectively.To address these limitations,we propose a hybrid CNN–GRU framework that integrates spatial feature extraction and sequential modeling.Heat Kernel Imputation is applied to reconstruct missing log data,and Borderline SMOTE(BSMOTE)improves class balance by augmenting boundary-case minority samples.The CNN component extracts localized petrophysical features,and the GRU component captures depth-wise lithological transitions,to enable spatial-sequential feature fusion.Experiments on real-well datasets from tight sandstone reservoirs show that the proposed model achieves an average accuracy of 93.3%and a Macro F1-score of 0.934.It outperforms baseline models,including RF(87.8%),GBDT(81.8%),CNN-only(87.5%),and GRU-only(86.1%).Leave-one-well-out validation further confirms strong generalization ability.These results demonstrate that the proposed approach effectively addresses data imbalance and enhances classification robustness,offering a scalable and automated solution for lithofacies interpretation under complex geological conditions.展开更多
The accurate prediction and analysis of emergencies in Urban Rail Transit Systems(URTS)are essential for the development of effective early warning and prevention mechanisms.This study presents an integrated perceptio...The accurate prediction and analysis of emergencies in Urban Rail Transit Systems(URTS)are essential for the development of effective early warning and prevention mechanisms.This study presents an integrated perception model designed to predict emergencies and analyze their causes based on historical unstructured emergency data.To address issues related to data structuredness and missing values,we employed label encoding and an Elastic Net Regularization-based Generative Adversarial Interpolation Network(ER-GAIN)for data structuring and imputation.Additionally,to mitigate the impact of imbalanced data on the predictive performance of emergencies,we introduced an Adaptive Boosting Ensemble Model(AdaBoost)to forecast the key features of emergencies,including event types and levels.We also utilized Information Gain(IG)to analyze and rank the causes of various significant emergencies.Experimental results indicate that,compared to baseline data imputation models,ER-GAIN improved the prediction accuracy of key emergency features by 3.67%and 3.78%,respectively.Furthermore,AdaBoost enhanced the accuracy by over 4.34%and 3.25%compared to baseline predictivemodels.Through causation analysis,we identified the critical causes of train operation and fire incidents.The findings of this research will contribute to the establishment of early warning and prevention mechanisms for emergencies in URTS,potentially leading to safer and more reliable URTS operations.展开更多
Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attentio...Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.展开更多
Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a sign...Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.展开更多
Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This s...Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This study aims to investigate the issue of missing data in extensive TBM datasets.Through a comprehensive literature review,we analyze the mechanism of missing TBM data and compare different imputation methods,including statistical analysis and machine learning algorithms.We also examine the impact of various missing patterns and rates on the efficacy of these methods.Finally,we propose a dynamic interpolation strategy tailored for TBM engineering sites.The research results show that K-Nearest Neighbors(KNN)and Random Forest(RF)algorithms can achieve good interpolation results;As the missing rate increases,the interpolation effect of different methods will decrease;The interpolation effect of block missing is poor,followed by mixed missing,and the interpolation effect of sporadic missing is the best.On-site application results validate the proposed interpolation strategy's capability to achieve robust missing value interpolation effects,applicable in ML scenarios such as parameter optimization,attitude warning,and pressure prediction.These findings contribute to enhancing the efficiency of TBM missing data processing,offering more effective support for large-scale TBM monitoring datasets.展开更多
Background: Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence(WGS) data. However, sequencing thousands of individuals of interest is expensive.Imputatio...Background: Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence(WGS) data. However, sequencing thousands of individuals of interest is expensive.Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation.Results: We genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24 X to 144 X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth(12 X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for resequencing. With fixed reference population size(24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1 X to 12 X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study.Conclusions: In conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.展开更多
Background: Improving the feed efficiency would increase profitability for producers while also reducing the environmental footprint of livestock production. This study was conducted to investigate the relationships a...Background: Improving the feed efficiency would increase profitability for producers while also reducing the environmental footprint of livestock production. This study was conducted to investigate the relationships among feed efficiency traits and metabolizable efficiency traits in 180 male broilers. Significant loci and genes affecting the metabolizable efficiency traits were explored with an imputation-based genome-wide association study. The traits measured or calculated comprised three growth traits, five feed efficiency related traits, and nine metabolizable efficiency traits.Results: The residual feed intake(RFI) showed moderate to high and positive phenotypic correlations with eight other traits measured, including average daily feed intake(ADFI), dry excreta weight(DEW), gross energy excretion(GEE), crude protein excretion(CPE), metabolizable dry matter(MDM), nitrogen corrected apparent metabolizable energy(AMEn), abdominal fat weight(Ab F), and percentage of abdominal fat(Ab P). Greater correlations were observed between growth traits and the feed conversion ratio(FCR) than RFI. In addition, the RFI, FCR, ADFI, DEW,GEE, CPE, MDM, AMEn, Ab F, and Ab P were lower in low-RFI birds than high-RFI birds(P < 0.01 or P < 0.05), whereas the coefficients of MDM and MCP of low-RFI birds were greater than those of high-RFI birds(P < 0.01). Five narrow QTLs for metabolizable efficiency traits were detected, including one 82.46-kb region for DEW and GEE on Gallus gallus chromosome(GGA) 26, one 120.13-kb region for MDM and AMEn on GGA1, one 691.25-kb region for the coefficients of MDM and AMEn on GGA5, one region for the coefficients of MDM and MCP on GGA2(103.45–103.53 Mb), and one 690.50-kb region for the coefficient of MCP on GGA14. Linkage disequilibrium(LD) analysis indicated that the five regions contained high LD blocks, as well as the genes chromosome 26 C6 orf106 homolog(C26 H6 orf106), LOC396098, SH3 and multiple ankyrin repeat domains 2(SHANK2), ETS homologous factor(EHF), and histamine receptor H3-like(HRH3 L), which are known to be involved in the regulation of neurodevelopment, cell proliferation and differentiation, and food intake.Conclusions: Selection for low RFI significantly decreased chicken feed intake, excreta output, and abdominal fat deposition, and increased nutrient digestibility without changing the weight gain. Five novel QTL regions involved in the control of metabolizable efficiency in chickens were identified. These results, combined through nutritional and genetic approaches, should facilitate novel insights into improving feed efficiency in poultry and other species.展开更多
Some studies have suggested that early surgical treatment can effectively improve the prognosis of cervical spinal cord injury without radiological abnormality, but no research has focused on the development of a prog...Some studies have suggested that early surgical treatment can effectively improve the prognosis of cervical spinal cord injury without radiological abnormality, but no research has focused on the development of a prognostic model of cervical spinal cord injury without radiological abnormality. This retrospective analysis included 43 patients with cervical spinal cord injury without radiological abnormality. Seven potential factors were assessed: age, sex, external force strength causing damage, duration of disease, degree of cervical spinal stenosis, Japanese Orthopaedic Association score, and physiological cervical curvature. A model was established using multiple binary logistic regression analysis. The model was evaluated by concordant profiling and the area under the receiver operating characteristic curve. Bootstrapping was used for internal validation. The prognostic model was as follows: logit(P) =-25.4545 + 21.2576 VALUE + 1.2160SCORE-3.4224 TIME, where VALUE refers to the Pavlov ratio indicating the extent of cervical spinal stenosis, SCORE refers to the Japanese Orthopaedic Association score(0–17) after the operation, and TIME refers to the disease duration(from injury to operation). The area under the receiver operating characteristic curve for all patients was 0.8941(95% confidence interval, 0.7930–0.9952). Three factors assessed in the predictive model were associated with patient outcomes: a great extent of cervical stenosis, a poor preoperative neurological status, and a long disease duration. These three factors could worsen patient outcomes. Moreover, the disease prognosis was considered good when logit(P) ≥-2.5105. Overall, the model displayed a certain clinical value. This study was approved by the Biomedical Ethics Committee of the Second Affiliated Hospital of Xi'an Jiaotong University, China(approval number: 2018063) on May 8, 2018.展开更多
Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction m...Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.展开更多
Background:Genotyping by sequencing(GBS)still has problems with missing genotypes.Imputation is important for using GBS for genomic predictions,especially for low depths,due to the large number of missing genotypes.Mi...Background:Genotyping by sequencing(GBS)still has problems with missing genotypes.Imputation is important for using GBS for genomic predictions,especially for low depths,due to the large number of missing genotypes.Minor allele frequency(MAF)is widely used as a marker data editing criteria for genomic predictions.In this study,three imputation methods(Beagle,IMPUTE2 and FImpute software)based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions,based on simulated data of livestock population.Results:Four MAFs(no MAF limit,MAF≥0.001,MAF≥0.01 and MAF≥0.03)were used for editing marker data before imputation.Beagle,IMPUTE2 and FImpute software were applied to impute the original GBS.Additionally,IMPUTE2 also imputed the expected genotype dosage after genotype correction(GcIM).The reliability of genomic predictions was calculated using GBS and imputed GBS data.The results showed that imputation accuracies were the same for the three imputation methods,except for the data of sequencing read depth(depth)=2,where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2.GcIM was observed to be the best for all of the imputations at depth=4,5 and 10,but the worst for depth=2.For genomic prediction,retaining more SNPs with no MAF limit resulted in higher reliability.As the depth increased to 10,the prediction reliabilities approached those using true genotypes in the GBS loci.Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points,and FImpute gained 3 percentage points at depth=2.The best prediction was observed at depth=4,5 and 10 using GcIM,but the worst prediction was also observed using GcIM at depth=2.Conclusions:The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths.Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths.These results suggest that the application of IMPUTE2,based on a corrected GBS(GcIM)to improve genomic predictions for higher depths,and FImpute software could be a good alternative for routine imputation.展开更多
Numbers of vertebrae is an important economic trait associated with body size and meat productivity in animals.However,the genetic basis of vertebrae number in donkey remains to be well understood.The aim of this stud...Numbers of vertebrae is an important economic trait associated with body size and meat productivity in animals.However,the genetic basis of vertebrae number in donkey remains to be well understood.The aim of this study was to identify candidate genes affecting the number of thoracic(TVn)and the number of lumbar vertebrae(LVn)in Dezhou donkey.A genome-wide association study was conducted using whole genome sequence data imputed from low-coverage genome sequencing.For TVn,we identified 38 genome-wide significant and 64 suggestive SNPs,which relate to 7 genes(NLGN1,DCC,SLC26A7,TOX,WNT7A,LOC123286078,and LOC123280142).For LVn,we identified 9 genome-wide significant and 38 suggestive SNPs,which relate to 8 genes(GABBR2,FBXO4,LOC123277146,LOC123277359,BMP7,B3GAT1,EML2,and LRP5).The genes involve in the Wnt and TGF-βsignaling pathways and may play an important role in embryonic development or bone formation and could be good candidate genes for TVn and LVn.展开更多
The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics,especially the analysis of gene expression data that facilitates an early detection of cancer.Many...The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics,especially the analysis of gene expression data that facilitates an early detection of cancer.Many attempts show improvements made by excluding samples with missing information from the analysis process,while others have tried to fill the gaps with possible values.While the former is simple,the latter safeguards information loss.For that,a neighbour-based(KNN)approach has proven more effective than other global estimators.The paper extends this further by introducing a new summarizationmethod to theKNNmodel.It is the first study that applies the concept of ordered weighted averaging(OWA)operator to such a problem context.In particular,two variations of OWA aggregation are proposed and evaluated against their baseline and other neighbor-based models.Using different ratios of missing values from 1%-20%and a set of six published gene expression datasets,the experimental results suggest that newmethods usually provide more accurate estimates than those compared methods.Specific to the missing rates of 5%and 20%,the best NRMSE scores as averages across datasets is 0.65 and 0.69,while the highest measures obtained by existing techniques included in this study are 0.80 and 0.84,respectively.展开更多
Genotype imputation has become an indispensable part of genomic data analysis. In recent years, imputation based on a multi-breed reference population has received more attention, but the relevant studies are scarce i...Genotype imputation has become an indispensable part of genomic data analysis. In recent years, imputation based on a multi-breed reference population has received more attention, but the relevant studies are scarce in pigs. In this study, we used the Illumina Porcine SNP50 Bead Chip to investigate the variations of imputation accuracy with various influencing factors and compared the imputation performance of four commonly used imputation software programs. The results indicated that imputation accuracy increased as either the validation population marker density, reference population sample size, or minor allele frequency(MAF) increased. However, the imputation accuracy would have a certain extent of decrease when the pig reference population was a mixed group of multiple breeds or lines. Considering both imputation accuracy and running time, Beagle 4.1 and FImpute are excellent choices among the four software packages tested. This work visually presents the impacts of these influencing factors on imputation and provides a reference for formulating reasonable imputation strategies in actual pig breeding.展开更多
Time series forecasting has become an important aspect of data analysis and has many real-world applications.However,undesirable missing values are often encountered,which may adversely affect many forecasting tasks.I...Time series forecasting has become an important aspect of data analysis and has many real-world applications.However,undesirable missing values are often encountered,which may adversely affect many forecasting tasks.In this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time series.Our approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom.In an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation method.Subsequently,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models.The results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.展开更多
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
基金supported by the National Natural Science Foundation of China(No.12475340 and 12375350)Special Branch project of South Taihu Lakethe Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202456326).
文摘Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.
基金partially supported by the National Natural Science Foundation of China(62271485)the SDHS Science and Technology Project(HS2023B044)
文摘Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.
基金support of the National Natural Science Foundation of China(U42107189,20A20111).
文摘Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to propose a fast and accurate model for predicting the longevity of landslide dams while also addressing the issue of missing data.Given the wide variation in the survival times of landslide dams—from mere minutes to several thousand years—predicting their longevity presents a considerable challenge.The study develops predictive models by considering key factors such as dam geometry,hydrodynamic conditions,materials,and triggering parameters.A dataset of 1045 landslide dam cases is analyzed,categorizing their longevity into three distinct groups:C1(<1 month),C2(1 month to 1 year),and C3(>1 year).Multiple imputation and knearest neighbor algorithms are used to handle missing data on geometric size,hydrodynamic conditions,materials,and triggers.Based on the imputed data,two predictive models are developed:a classification model for dam longevity categories and a regression model for precise longevity predictions.The classification model achieves an accuracy of 88.38%while the regression model outperforms existing models with an R^(2) value of 0.966.Two real-life landslide dam cases are used to validate the models,which show correct classification and small prediction errors.The longevity of landslide dams is jointly influenced by factors such as geometric size,hydrodynamic conditions,materials,and triggering events.Among these,geometric size has the greatest impact,followed by hydrodynamic conditions,materials,and triggers,as confirmed by variable importance in the model development.
基金supported by the Langfang Science and Technology Program with self-raised funds under the project“Application of Deep Learning-Based Joint Well-Seismic Analysis in Lithology Prediction”(Project No.2024011013)the Science and Technology Innovation Program for Postgraduate students in IDP subsidized by Fundamental Research Funds for the Central Universities,under the project“Research on CNN Algorithm Enhanced by Physical Information for Lithofacies Prediction in Tight Sandstone Reservoirs”(Project No.ZY20250328).
文摘Accurate lithofacies classification in low-permeability sandstone reservoirs remains challenging due to class imbalance in well-log data and the difficulty of the modeling vertical lithological dependencies.Traditional core-based interpretation introduces subjectivity,while conventional deep learning models often fail to capture stratigraphic sequences effectively.To address these limitations,we propose a hybrid CNN–GRU framework that integrates spatial feature extraction and sequential modeling.Heat Kernel Imputation is applied to reconstruct missing log data,and Borderline SMOTE(BSMOTE)improves class balance by augmenting boundary-case minority samples.The CNN component extracts localized petrophysical features,and the GRU component captures depth-wise lithological transitions,to enable spatial-sequential feature fusion.Experiments on real-well datasets from tight sandstone reservoirs show that the proposed model achieves an average accuracy of 93.3%and a Macro F1-score of 0.934.It outperforms baseline models,including RF(87.8%),GBDT(81.8%),CNN-only(87.5%),and GRU-only(86.1%).Leave-one-well-out validation further confirms strong generalization ability.These results demonstrate that the proposed approach effectively addresses data imbalance and enhances classification robustness,offering a scalable and automated solution for lithofacies interpretation under complex geological conditions.
基金supported by the Fundamental Research Funds for the Central Universities(grant number 2024YJS096)National Natural Science Foundation of China(grant numbers 62433005,62272036,62173167).
文摘The accurate prediction and analysis of emergencies in Urban Rail Transit Systems(URTS)are essential for the development of effective early warning and prevention mechanisms.This study presents an integrated perception model designed to predict emergencies and analyze their causes based on historical unstructured emergency data.To address issues related to data structuredness and missing values,we employed label encoding and an Elastic Net Regularization-based Generative Adversarial Interpolation Network(ER-GAIN)for data structuring and imputation.Additionally,to mitigate the impact of imbalanced data on the predictive performance of emergencies,we introduced an Adaptive Boosting Ensemble Model(AdaBoost)to forecast the key features of emergencies,including event types and levels.We also utilized Information Gain(IG)to analyze and rank the causes of various significant emergencies.Experimental results indicate that,compared to baseline data imputation models,ER-GAIN improved the prediction accuracy of key emergency features by 3.67%and 3.78%,respectively.Furthermore,AdaBoost enhanced the accuracy by over 4.34%and 3.25%compared to baseline predictivemodels.Through causation analysis,we identified the critical causes of train operation and fire incidents.The findings of this research will contribute to the establishment of early warning and prevention mechanisms for emergencies in URTS,potentially leading to safer and more reliable URTS operations.
基金supported by the Intelligent System Research Group(ISysRG)supported by Universitas Sriwijaya funded by the Competitive Research 2024.
文摘Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.
文摘Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.
基金supported by the National Natural Science Foundation of China(Grant No.52409151)the Programme of Shenzhen Key Laboratory of Green,Efficient and Intelligent Construction of Underground Metro Station(Programme No.ZDSYS20200923105200001)the Science and Technology Major Project of Xizang Autonomous Region of China(XZ202201ZD0003G).
文摘Substantial advancements have been achieved in Tunnel Boring Machine(TBM)technology and monitoring systems,yet the presence of missing data impedes accurate analysis and interpretation of TBM monitoring results.This study aims to investigate the issue of missing data in extensive TBM datasets.Through a comprehensive literature review,we analyze the mechanism of missing TBM data and compare different imputation methods,including statistical analysis and machine learning algorithms.We also examine the impact of various missing patterns and rates on the efficacy of these methods.Finally,we propose a dynamic interpolation strategy tailored for TBM engineering sites.The research results show that K-Nearest Neighbors(KNN)and Random Forest(RF)algorithms can achieve good interpolation results;As the missing rate increases,the interpolation effect of different methods will decrease;The interpolation effect of block missing is poor,followed by mixed missing,and the interpolation effect of sporadic missing is the best.On-site application results validate the proposed interpolation strategy's capability to achieve robust missing value interpolation effects,applicable in ML scenarios such as parameter optimization,attitude warning,and pressure prediction.These findings contribute to enhancing the efficiency of TBM missing data processing,offering more effective support for large-scale TBM monitoring datasets.
基金supported by the National Natural Science Foundation of China(31772556)the China Agricultural Research System(CARS-41-G03)+2 种基金the Science Innovation Project of Guangdong(2015A020209159)the Special Program for Applied Research on Super Computation of the NSFC Guangdong Joint Fund(the second phase)under Grant No.U1501501technical support from the National Supercomputer Center in Guangzhou
文摘Background: Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence(WGS) data. However, sequencing thousands of individuals of interest is expensive.Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation.Results: We genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24 X to 144 X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth(12 X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for resequencing. With fixed reference population size(24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1 X to 12 X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study.Conclusions: In conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.
基金supported by grants from the National Nonprofit Institute Research Grant (Y2020PT02)the earmarked fund for the modern agroindustry technology research system (CARS-41)+1 种基金Agricultural Science and Technology Innovation Program (ASTIP-IAS04ASTIP-IAS-TS-15)。
文摘Background: Improving the feed efficiency would increase profitability for producers while also reducing the environmental footprint of livestock production. This study was conducted to investigate the relationships among feed efficiency traits and metabolizable efficiency traits in 180 male broilers. Significant loci and genes affecting the metabolizable efficiency traits were explored with an imputation-based genome-wide association study. The traits measured or calculated comprised three growth traits, five feed efficiency related traits, and nine metabolizable efficiency traits.Results: The residual feed intake(RFI) showed moderate to high and positive phenotypic correlations with eight other traits measured, including average daily feed intake(ADFI), dry excreta weight(DEW), gross energy excretion(GEE), crude protein excretion(CPE), metabolizable dry matter(MDM), nitrogen corrected apparent metabolizable energy(AMEn), abdominal fat weight(Ab F), and percentage of abdominal fat(Ab P). Greater correlations were observed between growth traits and the feed conversion ratio(FCR) than RFI. In addition, the RFI, FCR, ADFI, DEW,GEE, CPE, MDM, AMEn, Ab F, and Ab P were lower in low-RFI birds than high-RFI birds(P < 0.01 or P < 0.05), whereas the coefficients of MDM and MCP of low-RFI birds were greater than those of high-RFI birds(P < 0.01). Five narrow QTLs for metabolizable efficiency traits were detected, including one 82.46-kb region for DEW and GEE on Gallus gallus chromosome(GGA) 26, one 120.13-kb region for MDM and AMEn on GGA1, one 691.25-kb region for the coefficients of MDM and AMEn on GGA5, one region for the coefficients of MDM and MCP on GGA2(103.45–103.53 Mb), and one 690.50-kb region for the coefficient of MCP on GGA14. Linkage disequilibrium(LD) analysis indicated that the five regions contained high LD blocks, as well as the genes chromosome 26 C6 orf106 homolog(C26 H6 orf106), LOC396098, SH3 and multiple ankyrin repeat domains 2(SHANK2), ETS homologous factor(EHF), and histamine receptor H3-like(HRH3 L), which are known to be involved in the regulation of neurodevelopment, cell proliferation and differentiation, and food intake.Conclusions: Selection for low RFI significantly decreased chicken feed intake, excreta output, and abdominal fat deposition, and increased nutrient digestibility without changing the weight gain. Five novel QTL regions involved in the control of metabolizable efficiency in chickens were identified. These results, combined through nutritional and genetic approaches, should facilitate novel insights into improving feed efficiency in poultry and other species.
基金supported by the National Natural Science Foundation of China,No.30672136(to HPL)
文摘Some studies have suggested that early surgical treatment can effectively improve the prognosis of cervical spinal cord injury without radiological abnormality, but no research has focused on the development of a prognostic model of cervical spinal cord injury without radiological abnormality. This retrospective analysis included 43 patients with cervical spinal cord injury without radiological abnormality. Seven potential factors were assessed: age, sex, external force strength causing damage, duration of disease, degree of cervical spinal stenosis, Japanese Orthopaedic Association score, and physiological cervical curvature. A model was established using multiple binary logistic regression analysis. The model was evaluated by concordant profiling and the area under the receiver operating characteristic curve. Bootstrapping was used for internal validation. The prognostic model was as follows: logit(P) =-25.4545 + 21.2576 VALUE + 1.2160SCORE-3.4224 TIME, where VALUE refers to the Pavlov ratio indicating the extent of cervical spinal stenosis, SCORE refers to the Japanese Orthopaedic Association score(0–17) after the operation, and TIME refers to the disease duration(from injury to operation). The area under the receiver operating characteristic curve for all patients was 0.8941(95% confidence interval, 0.7930–0.9952). Three factors assessed in the predictive model were associated with patient outcomes: a great extent of cervical stenosis, a poor preoperative neurological status, and a long disease duration. These three factors could worsen patient outcomes. Moreover, the disease prognosis was considered good when logit(P) ≥-2.5105. Overall, the model displayed a certain clinical value. This study was approved by the Biomedical Ethics Committee of the Second Affiliated Hospital of Xi'an Jiaotong University, China(approval number: 2018063) on May 8, 2018.
基金supported in part by the National Natural Science Foundation of China(51975075)Chongqing Technology Innovation and Application Program(cstc2018jszx-cyzd X0183)。
文摘Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.
基金This study was funded by the Genomic Selection in Animals and Plants(GenSAP)research project financed by the Danish Council of Strategic Research(Aarhus,Denmark).Xiao Wang received Ph.D.stipends from the Technical University of Denmark(DTU Bioinformatics and DTU Compute),Denmark,and the China Scholarship Council,China.
文摘Background:Genotyping by sequencing(GBS)still has problems with missing genotypes.Imputation is important for using GBS for genomic predictions,especially for low depths,due to the large number of missing genotypes.Minor allele frequency(MAF)is widely used as a marker data editing criteria for genomic predictions.In this study,three imputation methods(Beagle,IMPUTE2 and FImpute software)based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions,based on simulated data of livestock population.Results:Four MAFs(no MAF limit,MAF≥0.001,MAF≥0.01 and MAF≥0.03)were used for editing marker data before imputation.Beagle,IMPUTE2 and FImpute software were applied to impute the original GBS.Additionally,IMPUTE2 also imputed the expected genotype dosage after genotype correction(GcIM).The reliability of genomic predictions was calculated using GBS and imputed GBS data.The results showed that imputation accuracies were the same for the three imputation methods,except for the data of sequencing read depth(depth)=2,where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2.GcIM was observed to be the best for all of the imputations at depth=4,5 and 10,but the worst for depth=2.For genomic prediction,retaining more SNPs with no MAF limit resulted in higher reliability.As the depth increased to 10,the prediction reliabilities approached those using true genotypes in the GBS loci.Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points,and FImpute gained 3 percentage points at depth=2.The best prediction was observed at depth=4,5 and 10 using GcIM,but the worst prediction was also observed using GcIM at depth=2.Conclusions:The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths.Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths.These results suggest that the application of IMPUTE2,based on a corrected GBS(GcIM)to improve genomic predictions for higher depths,and FImpute software could be a good alternative for routine imputation.
基金the Natural Science Foundation of Shandong Province,China(ZR2020MC168)。
文摘Numbers of vertebrae is an important economic trait associated with body size and meat productivity in animals.However,the genetic basis of vertebrae number in donkey remains to be well understood.The aim of this study was to identify candidate genes affecting the number of thoracic(TVn)and the number of lumbar vertebrae(LVn)in Dezhou donkey.A genome-wide association study was conducted using whole genome sequence data imputed from low-coverage genome sequencing.For TVn,we identified 38 genome-wide significant and 64 suggestive SNPs,which relate to 7 genes(NLGN1,DCC,SLC26A7,TOX,WNT7A,LOC123286078,and LOC123280142).For LVn,we identified 9 genome-wide significant and 38 suggestive SNPs,which relate to 8 genes(GABBR2,FBXO4,LOC123277146,LOC123277359,BMP7,B3GAT1,EML2,and LRP5).The genes involve in the Wnt and TGF-βsignaling pathways and may play an important role in embryonic development or bone formation and could be good candidate genes for TVn and LVn.
基金This work is funded by Newton Institutional Links 2020-21 project:623718881,jointly by British Council and National Research Council of Thailand(www.britishcouncil.org).The corresponding author is the project PI.
文摘The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics,especially the analysis of gene expression data that facilitates an early detection of cancer.Many attempts show improvements made by excluding samples with missing information from the analysis process,while others have tried to fill the gaps with possible values.While the former is simple,the latter safeguards information loss.For that,a neighbour-based(KNN)approach has proven more effective than other global estimators.The paper extends this further by introducing a new summarizationmethod to theKNNmodel.It is the first study that applies the concept of ordered weighted averaging(OWA)operator to such a problem context.In particular,two variations of OWA aggregation are proposed and evaluated against their baseline and other neighbor-based models.Using different ratios of missing values from 1%-20%and a set of six published gene expression datasets,the experimental results suggest that newmethods usually provide more accurate estimates than those compared methods.Specific to the missing rates of 5%and 20%,the best NRMSE scores as averages across datasets is 0.65 and 0.69,while the highest measures obtained by existing techniques included in this study are 0.80 and 0.84,respectively.
基金supported by the China Agriculture Research System of MOF and MARA(CARS-35)the National Natural Science Foundation of China(32072696,31790414 and 31601916)the Fundamental Research Funds for the Central Universities(2662019PY011)。
文摘Genotype imputation has become an indispensable part of genomic data analysis. In recent years, imputation based on a multi-breed reference population has received more attention, but the relevant studies are scarce in pigs. In this study, we used the Illumina Porcine SNP50 Bead Chip to investigate the variations of imputation accuracy with various influencing factors and compared the imputation performance of four commonly used imputation software programs. The results indicated that imputation accuracy increased as either the validation population marker density, reference population sample size, or minor allele frequency(MAF) increased. However, the imputation accuracy would have a certain extent of decrease when the pig reference population was a mixed group of multiple breeds or lines. Considering both imputation accuracy and running time, Beagle 4.1 and FImpute are excellent choices among the four software packages tested. This work visually presents the impacts of these influencing factors on imputation and provides a reference for formulating reasonable imputation strategies in actual pig breeding.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(Grant Number 2020R1A6A1A03040583).
文摘Time series forecasting has become an important aspect of data analysis and has many real-world applications.However,undesirable missing values are often encountered,which may adversely affect many forecasting tasks.In this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time series.Our approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom.In an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation method.Subsequently,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models.The results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.
