Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classificatio...Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.展开更多
In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and classification. The multi-expre...In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and classification. The multi-expression classification algorithm which is based on Adaboost and mutual independent feature is proposed. In order to effectively and quickly train threshold values of weak classifiers of features, Sample of training is carried out simple improvement. We obtain a good classification results through experiments.展开更多
Differences between healthy subjects and associated disease risks are of substantial interest in clinical medicine. Based on clinical presentations, Traditional Chinese Medicine （TCM） classifies healthy people into ...Differences between healthy subjects and associated disease risks are of substantial interest in clinical medicine. Based on clinical presentations, Traditional Chinese Medicine （TCM） classifies healthy people into nine constitutions： Balanced, Qi, Yang or Yin deficiency, Phlegm-dampness, Damp-heat, Blood stasis, Qi stagnation, and Inherited special constitutions. In particular, Yang and Yin deficiency constitutions exhibit cold and heat aversion, respectively. However, the intrinsic molecular characteristics of unbal- anced phenotypes remain unclear. To determine whether gene expression-based clustering can reca- pitulate TCM-based classification, peripheral blood mononudear cells （PBMCs） were collected from Chinese Han individuals with Yang/Yin deficiency （n = 12 each） and Balanced （n = 8） constitutions, and global gene expression profiles were determined using the Affymetrix HC-UI33A Plus 2.0 array. Notably, we found that gene expression-based classifications reflected distinct TCM-based subtypes. Consistent with the clinical observation that subjects with Yang deficiency tend toward obesity, series-clustering analysis detected several key lipid metabolic genes （diacylglycerol acyltransferase （DGAT2）, acyl-CoA synthetase （ACSL1）, and ATP-hinding cassette subfamily A member 1 （ABCAI）） to be down- and up- regulated in Yin and Yang deficiency constitutions, respectively. Our findings suggest that Yin]Yang deficiency and Balanced constitutions are unique entities in their mRNA expression profiles. Moreover, the distinct physical and clinical characteristics of each unbalanced constitution can be explained, in part, by specific gene expression signatures.展开更多
Light-harvesting chlorophyll a/b-binding （LHC） proteins are a group of nuclear-encoded thylakoid proteins that play a key role in plant photosynthesis and are widely involved in light harvesting, energy transfer to ...Light-harvesting chlorophyll a/b-binding （LHC） proteins are a group of nuclear-encoded thylakoid proteins that play a key role in plant photosynthesis and are widely involved in light harvesting, energy transfer to the reaction center, maintenance of thylakoid membrane structure, photoprotection and response to en- vironmental conditions, etc. Although/dw supergene family is well characterized in model plants such as Arabidopsis, rice and poplar, little information is available in castor bean （Ricinus communis L. ）. In this study, a genome-wide search was carried out for the first time to identify castor bean L/w genes and analyze the gene structures, biochemical properties, evolutionary relationships and expression characteristics based on the published data of castor bean genome and ESTs. According to the results, a total of 28 Rclhcs genes representing 13 gene families （ l_hca , l_hcb , Elip , Ohpl , Ohp2 , SEP1, SEP2 , SEP3 , SEP4 , SEP5 , PsbS , Rieske and FCII） and 25 subgene families were identified in castor bean genome; to be specific, 25 and 5 genes were found to have corresponding ESTs in NCBI and have al- ternative splicing isoforlns, respectively. These RcLhcs contain 0 to 9 introns and distribute on 26 of the 25 878 released scaffolds. All RcLhcs genes were found to be expressed in all examined tissues, i.e. leaf, flower, II/III stage endosperm, V/VI stage endosperm and seed, with the highest expression level in leaf tissue.展开更多
In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary cha...In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.展开更多
Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists...Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.展开更多
DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we presen...DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we present Fs-LSA (F-score based Learning Search Algorithm), a novel gene selection algorithm designed to enhance the precision and efficiency of target gene identification from microarray data for cancer classification. This algorithm is divided into two phases: the first leverages F-score values to prioritize and select feature genes with the most significant differential expression;the second phase introduces our Learning Search Algorithm (LSA), which harnesses swarm intelligence to identify the optimal subset among the remaining genes. Inspired by human social learning, LSA integrates historical data and collective intelligence for a thorough search, with a dynamic control mechanism that balances exploration and refinement, thereby enhancing the gene selection process. We conducted a rigorous validation of Fs-LSA’s performance using eight publicly available cancer microarray expression datasets. Fs-LSA achieved accuracy, precision, sensitivity, and F1-score values of 0.9932, 0.9923, 0.9962, and 0.994, respectively. Comparative analyses with state-of-the-art algorithms revealed Fs-LSA’s superior performance in terms of simplicity and efficiency. Additionally, we validated the algorithm’s efficacy independently using glioblastoma data from GEO and TCGA databases. It was significantly superior to those of the comparison algorithms. Importantly, the driver genes identified by Fs-LSA were instrumental in developing a predictive model as an independent prognostic indicator for glioblastoma, underscoring Fs-LSA’s transformative potential in genomics and personalized medicine.展开更多
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati...In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.展开更多
Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a chal...Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishin...Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.展开更多
Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype cl...Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.展开更多
The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median...The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted.展开更多
Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse...Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse facial features of individual frames.In this paper, a frame-level attention module is integrated into an improved VGG-based frame work and a lightweight facial expression recognition method is proposed.The proposed network takes a sub video cut from an experimental video sequence as its input and generates a fixed-dimension representation.The VGG-based network with an enhanced branch embeds face images into feature vectors.The frame-level attention module learns weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation.Finally, a regression module outputs the classification results.The experimental results on CK+and AFEW databases show that the recognition rates of the proposed method can achieve the state-of-the-art performance.展开更多
Background:Gastric adenocarcinoma(GA)is a heterogeneous tumor,and the accurate classification of GA is important.Previous classifications are based on molecular analysis and have not focused on GA with the primitive e...Background:Gastric adenocarcinoma(GA)is a heterogeneous tumor,and the accurate classification of GA is important.Previous classifications are based on molecular analysis and have not focused on GA with the primitive enterocyte phenotype(GAPEP),a unique subtype with a poor prognosis and frequent liver metastases.New substituted molecular(SM)classifications based on immunohistochemistry(IHC)are needed.Methods:According to the IHC staining results,we divided 582 cases into six types:mismatch repair deficient(dMMR),Epstein-Barr virus associated(EBVa),the primitive enterocyte phenotype(PEP),the epithelial mes-enchymal transition(EMT)phenotype,not otherwise specified/P53 mutated(NOS/P53m)and not otherwise specified/P53 wild-type(NOS/P53w).We analyzed the clinicopathological features,the immune microenviron-ment(PD-L1,CD8)and expression of HER2 and VEGFR2 of those types.Results:There were 31(5.3%)cases of the dMMR type,13(2.2%)cases of the EBVa type,44(7.6%)cases of the PEP type,122(21.0%)cases of the EMT type,127(21.8%)cases of the NOS/P53m type and 245(42.1%)cases of the NOS/P53w type.Patients with the dMMR type had the best survival(P<0.001).Patients with the EBVa type were younger(P<0.001)and had higher PD-L1 and CD8 expression(P<0.001)than other patients.Patients with the EMT type exhibited poor differentiation and a higher rate of abdominal metastasis.Patients with the NOS/P53m and PEP types had the worst survival rates and the highest PD-L1/HER2/VEGFR2 expression levels among all patients(P<0.001).Conclusion:Different SM classifications have different clinicopathological features and expression patterns,which indicate the probable clinical treatment strategies for these subtypes.展开更多
The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Altho...The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.展开更多
针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了...针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了改进的SE Attention模块,以期提高模型的收敛速度和对面部细节的关注程度.在保持VGG19深层特征提取能力的基础上,通过特定设计的卷积层和跳跃连接,实现了对特征的高效融合与优化.经过改进的VGG19模型,不仅能更好地提取面部特征,还能在保证准确率的前提下,降低模型参数,提高运算效率.为了验证改进模型的效果,利用FER2013数据集和CK+两个数据集对本文提出的模型进行了测试.实验结果显示,改进后的VGG19网络在表情识别的准确率上分别取得了1.58%和4.04%的提升.这一结果充分证明了本文提出的方法在解决传统面部识别问题方面的优越性,也为面部识别技术的进一步发展提供了新的思路.展开更多
基金supported by a grant from the National High-tech R&D Program (863 Program, No. 2006AA02Z331) to Liangbiao Chen
文摘Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.
文摘In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and classification. The multi-expression classification algorithm which is based on Adaboost and mutual independent feature is proposed. In order to effectively and quickly train threshold values of weak classifiers of features, Sample of training is carried out simple improvement. We obtain a good classification results through experiments.
基金supported by the National Key Basic Research Program of China (973 Program No. 2011CB505400)
文摘Differences between healthy subjects and associated disease risks are of substantial interest in clinical medicine. Based on clinical presentations, Traditional Chinese Medicine （TCM） classifies healthy people into nine constitutions： Balanced, Qi, Yang or Yin deficiency, Phlegm-dampness, Damp-heat, Blood stasis, Qi stagnation, and Inherited special constitutions. In particular, Yang and Yin deficiency constitutions exhibit cold and heat aversion, respectively. However, the intrinsic molecular characteristics of unbal- anced phenotypes remain unclear. To determine whether gene expression-based clustering can reca- pitulate TCM-based classification, peripheral blood mononudear cells （PBMCs） were collected from Chinese Han individuals with Yang/Yin deficiency （n = 12 each） and Balanced （n = 8） constitutions, and global gene expression profiles were determined using the Affymetrix HC-UI33A Plus 2.0 array. Notably, we found that gene expression-based classifications reflected distinct TCM-based subtypes. Consistent with the clinical observation that subjects with Yang deficiency tend toward obesity, series-clustering analysis detected several key lipid metabolic genes （diacylglycerol acyltransferase （DGAT2）, acyl-CoA synthetase （ACSL1）, and ATP-hinding cassette subfamily A member 1 （ABCAI）） to be down- and up- regulated in Yin and Yang deficiency constitutions, respectively. Our findings suggest that Yin]Yang deficiency and Balanced constitutions are unique entities in their mRNA expression profiles. Moreover, the distinct physical and clinical characteristics of each unbalanced constitution can be explained, in part, by specific gene expression signatures.
基金Supported by National Natural Science Foundation of China(31100460)Natural Science Foundation of Hainan Province(312026)Fundamental Research Fund for the Rubber Research Institute in Chinese Academy of Tropical Agricultural Sciences(1630022011014)
文摘Light-harvesting chlorophyll a/b-binding （LHC） proteins are a group of nuclear-encoded thylakoid proteins that play a key role in plant photosynthesis and are widely involved in light harvesting, energy transfer to the reaction center, maintenance of thylakoid membrane structure, photoprotection and response to en- vironmental conditions, etc. Although/dw supergene family is well characterized in model plants such as Arabidopsis, rice and poplar, little information is available in castor bean （Ricinus communis L. ）. In this study, a genome-wide search was carried out for the first time to identify castor bean L/w genes and analyze the gene structures, biochemical properties, evolutionary relationships and expression characteristics based on the published data of castor bean genome and ESTs. According to the results, a total of 28 Rclhcs genes representing 13 gene families （ l_hca , l_hcb , Elip , Ohpl , Ohp2 , SEP1, SEP2 , SEP3 , SEP4 , SEP5 , PsbS , Rieske and FCII） and 25 subgene families were identified in castor bean genome; to be specific, 25 and 5 genes were found to have corresponding ESTs in NCBI and have al- ternative splicing isoforlns, respectively. These RcLhcs contain 0 to 9 introns and distribute on 26 of the 25 878 released scaffolds. All RcLhcs genes were found to be expressed in all examined tissues, i.e. leaf, flower, II/III stage endosperm, V/VI stage endosperm and seed, with the highest expression level in leaf tissue.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/42/43)This work was supported by Taif University Researchers Supporting Program(project number:TURSP-2020/200),Taif University,Saudi Arabia.
文摘In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.
文摘Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.
基金supported by the National Natural Science Foundation of China(Grant Number 62341210)Natural Science Foundation of Guangxi Province(Grant Number:2025GXNSFHA069267)Science and Technology Development Plan for Baise City(Grant Number 20233654).
文摘DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we present Fs-LSA (F-score based Learning Search Algorithm), a novel gene selection algorithm designed to enhance the precision and efficiency of target gene identification from microarray data for cancer classification. This algorithm is divided into two phases: the first leverages F-score values to prioritize and select feature genes with the most significant differential expression;the second phase introduces our Learning Search Algorithm (LSA), which harnesses swarm intelligence to identify the optimal subset among the remaining genes. Inspired by human social learning, LSA integrates historical data and collective intelligence for a thorough search, with a dynamic control mechanism that balances exploration and refinement, thereby enhancing the gene selection process. We conducted a rigorous validation of Fs-LSA’s performance using eight publicly available cancer microarray expression datasets. Fs-LSA achieved accuracy, precision, sensitivity, and F1-score values of 0.9932, 0.9923, 0.9962, and 0.994, respectively. Comparative analyses with state-of-the-art algorithms revealed Fs-LSA’s superior performance in terms of simplicity and efficiency. Additionally, we validated the algorithm’s efficacy independently using glioblastoma data from GEO and TCGA databases. It was significantly superior to those of the comparison algorithms. Importantly, the driver genes identified by Fs-LSA were instrumental in developing a predictive model as an independent prognostic indicator for glioblastoma, underscoring Fs-LSA’s transformative potential in genomics and personalized medicine.
基金funded by the Ministry of Higher Education of Malaysia,grant number FRGS/1/2022/ICT02/UPSI/02/1.
文摘In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.
基金supported by the Academy of Finland(267581)the D2I SHOK Project from Digile Oy as well as Nokia Technologies(Tampere,Finland)
文摘Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
文摘Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.
基金supported by the Langfang Science and Technology Plan Project(No.2018013151)from Hebei Petro China Central Hospital.
文摘Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.
基金King Saud University for funding this work through Researchers Supporting Project Number(RSP2022R426),King Saud University,Riyadh,Saudi Arabia.
文摘The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted.
基金Supported by the Future Network Scientific Research Fund Project of Jiangsu Province (No. FNSRFP2021YB26)the Jiangsu Key R&D Fund on Social Development (No. BE2022789)the Science Foundation of Nanjing Institute of Technology (No. ZKJ202003)。
文摘Facial expression recognition(FER) in video has attracted the increasing interest and many approaches have been made.The crucial problem of classifying a given video sequence into several basic emotions is how to fuse facial features of individual frames.In this paper, a frame-level attention module is integrated into an improved VGG-based frame work and a lightweight facial expression recognition method is proposed.The proposed network takes a sub video cut from an experimental video sequence as its input and generates a fixed-dimension representation.The VGG-based network with an enhanced branch embeds face images into feature vectors.The frame-level attention module learns weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation.Finally, a regression module outputs the classification results.The experimental results on CK+and AFEW databases show that the recognition rates of the proposed method can achieve the state-of-the-art performance.
基金supported by the Peking Union Medical College Youth Fund(2017320030)the Beijing Hope Run Special Fund(No.LC2018A12),the CAMS Initiative for Innovative Medicine(CIFMS)(No.2016-I2M-3-005)+1 种基金the Medical and Health Science and Tech-nology Innovation Project of the Chinese Academy of Medical Sci-ences(2016-12M-1-007)the China International Medical Exchange Foundation Xiansheng Anti-Tumor Therapy Special Research Fund(cimf-f-h001-314).
文摘Background:Gastric adenocarcinoma(GA)is a heterogeneous tumor,and the accurate classification of GA is important.Previous classifications are based on molecular analysis and have not focused on GA with the primitive enterocyte phenotype(GAPEP),a unique subtype with a poor prognosis and frequent liver metastases.New substituted molecular(SM)classifications based on immunohistochemistry(IHC)are needed.Methods:According to the IHC staining results,we divided 582 cases into six types:mismatch repair deficient(dMMR),Epstein-Barr virus associated(EBVa),the primitive enterocyte phenotype(PEP),the epithelial mes-enchymal transition(EMT)phenotype,not otherwise specified/P53 mutated(NOS/P53m)and not otherwise specified/P53 wild-type(NOS/P53w).We analyzed the clinicopathological features,the immune microenviron-ment(PD-L1,CD8)and expression of HER2 and VEGFR2 of those types.Results:There were 31(5.3%)cases of the dMMR type,13(2.2%)cases of the EBVa type,44(7.6%)cases of the PEP type,122(21.0%)cases of the EMT type,127(21.8%)cases of the NOS/P53m type and 245(42.1%)cases of the NOS/P53w type.Patients with the dMMR type had the best survival(P<0.001).Patients with the EBVa type were younger(P<0.001)and had higher PD-L1 and CD8 expression(P<0.001)than other patients.Patients with the EMT type exhibited poor differentiation and a higher rate of abdominal metastasis.Patients with the NOS/P53m and PEP types had the worst survival rates and the highest PD-L1/HER2/VEGFR2 expression levels among all patients(P<0.001).Conclusion:Different SM classifications have different clinicopathological features and expression patterns,which indicate the probable clinical treatment strategies for these subtypes.
基金supported by the National Natural Science Foundation of China (20835002)International Science and Technology Cooperation Program of the Ministry of Science and Technology (MOST) of China (2008DFA32250)
文摘The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.
文摘针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了改进的SE Attention模块,以期提高模型的收敛速度和对面部细节的关注程度.在保持VGG19深层特征提取能力的基础上,通过特定设计的卷积层和跳跃连接,实现了对特征的高效融合与优化.经过改进的VGG19模型,不仅能更好地提取面部特征,还能在保证准确率的前提下,降低模型参数,提高运算效率.为了验证改进模型的效果,利用FER2013数据集和CK+两个数据集对本文提出的模型进行了测试.实验结果显示,改进后的VGG19网络在表情识别的准确率上分别取得了1.58%和4.04%的提升.这一结果充分证明了本文提出的方法在解决传统面部识别问题方面的优越性,也为面部识别技术的进一步发展提供了新的思路.
