Background:Transrectal(TR)and transperineal(TP)biopsies are commonly used methods for diagnosing prostate cancer.However,their comparative effectiveness in conjunction with machine learning(ML)techniques remains under...Background:Transrectal(TR)and transperineal(TP)biopsies are commonly used methods for diagnosing prostate cancer.However,their comparative effectiveness in conjunction with machine learning(ML)techniques remains underexplored.This study aimed to evaluate the predictive accuracy of ML algorithms in detecting prostate cancer using data derived from TR and TP biopsies.Methods:The clinical records of patients who underwent prostate biopsy at King Saud University Medical City and King Faisal Specialist Hospital and Research Centerin Riyadh,Saudi Arabia,between 2018 and 2025 were analyzed.Data were used to train and testMLmodels,including eXtreme Gradient Boosting(XGBoost),Decision Tree,Random Forest,and Extra Trees.Results:The two datasets are comparable.The models demonstrated exceptional performance,achieving accuracies of up to 96.49%and 95.56%on TP and TR biopsy datasets,respectively.The area under the curve(AUC)values were also high,reaching 0.9988 for TP and 0.9903 for TR biopsy predictions.Conclusion:These findings highlight the potential of MLto enhance the diagnostic accuracy of prostate cancer detection irrespective of the biopsy method.However,TP biopsy data showed marginally higher accuracy,possibly because of the lower risk of contamination.While ML holds great promise for transforming prostate cancer care,further research is needed to address limitations.Collaboration between clinicians,data scientists,and researchers is crucial to ensure the clinical relevance and interpretability of ML models.展开更多
The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damag...The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.展开更多
BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of lif...BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of life.However,the detection of TDs relies primarily on postoperative pathological examination,which may have a low detection rate due to sampling limitations.AIM To evaluate the spectral computed tomography(CT)parameters of primary tu-mors and the largest regional lymph nodes(LNs),to determine their value in predicting TDs in CRC.METHODS A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available.Patients were divided into the TDs+group and the TDs-group on the basis of their pathological results.Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured,including the normalized iodine concentration(NIC)in both the arte-rial and venous phases,and the LN-to-primary tumor ratio was calculated.Stati-stical methods were used to evaluate the diagnostic efficacy of each spectral para-meter.RESULTS Among the 121 CRC patients,33(27.2%)were confirmed to be TDs+.The risk of TDs positivity was greater in patients with positive LN metastasis,higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels.The NIC(LNs in both the arterial and venous phases),NIC(primary tumors in the venous phase),and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs(P<0.05).In mul-tivariate logistic regression analysis,the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs,demonstrating the highest diagnostic performance(area under the curve:0.812,sensitivity:0.879,specificity:0.648,cutoff value:1.145).CONCLUSION The spectral CT parameters of the primary colorectal tumor and the largest regional LNs,especially the LN-to-primary tumor ratio,have significant clinical value in predicting TDs in CRC.展开更多
To the Editor:We read with great interest the recent article by Shi et al.pub-lished in Hepatobiliary Pancreatic Diseases International[1].Shi’s study was based on radiological features and clinical factors to constr...To the Editor:We read with great interest the recent article by Shi et al.pub-lished in Hepatobiliary Pancreatic Diseases International[1].Shi’s study was based on radiological features and clinical factors to construct a model to predict the effectiveness of first transarterial chemoembolization(TACE)treatment for hepatocellular carcinoma(HCC)in prolonging patient survival.The results showed that area under the receiver operating characteristic curve was 0.964 for the training cohort and 0.949 for the validation cohort.展开更多
BACKGROUND Early symptoms of hepatocellular carcinoma(HCC)are not obvious,and more than 70%of which does not receive radical hepatectomy,when first diagnosed.In recent years,molecular-targeted drugs combined with immu...BACKGROUND Early symptoms of hepatocellular carcinoma(HCC)are not obvious,and more than 70%of which does not receive radical hepatectomy,when first diagnosed.In recent years,molecular-targeted drugs combined with immunotherapy and other therapeutic methods have provided new treatment options for middle and advanced HCC(aHCC).Predicting the effect of targeted combined immunotherapy has become a hot topic in current research.AIM To explore the relationship between nodule enhancement in hepatobiliary phase and the efficacy of combined targeted immunotherapy for aHCC.METHODS Data from 56 patients with aHCC for magnetic resonance imaging with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid were retrospectively collected.Signal intensity of intrahepatic nodules was measured,and the hepatobiliary relative enhancement ratio(RER)was calculated.Progression-free survival(PFS)of patients with high and low reinforcement of HCC nodules was compared.The model was validated using receiver operating characteristic curves.Univariate and multivariate logistic regression and Kaplan-Meier analysis were performed to explore factors influencing the efficacy of targeted immunization and PFS.RESULTS Univariate and multivariate analyses revealed that the RER,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,and prognostic nutritional index were significantly associated with the efficacy of tyrosine kinase inhibitors combined with immunotherapy(P<0.05).The area under the curve of the RER for predicting the efficacy of tyrosine kinase inhibitors combined with anti-programmed death 1 antibody in patients with aHCC was 0.876(95%confidence interval:0.781-0.971,P<0.05),the optimal cutoff value was 0.904,diagnostic sensitivity was 87.5%,and specificity was 79.2%.Kaplan-Meier analysis showed that neutrophil-to-lymphocyte ratio<5,plateletto-lymphocyte ratio<300,prognostic nutritional index<45,and RER<0.9 significantly improved PFS.CONCLUSION AHCC nodules enhancement in the hepatobiliary stage was significantly correlated with PFS.Imaging information and immunological indicators had high predictive efficacy for targeted combined immunotherapy and were associated with PFS.展开更多
Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy o...Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.展开更多
The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years ...The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years from the time of patenting are required to make a new drug available for general prescription. Every new drug needs to be charac-展开更多
Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis...Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.展开更多
In protein engineering,while computational models are increasingly used to predict mutation effects,their evaluations primarily rely on high-throughput deep mutational scanning(DMS)experiments that use surrogate reado...In protein engineering,while computational models are increasingly used to predict mutation effects,their evaluations primarily rely on high-throughput deep mutational scanning(DMS)experiments that use surrogate readouts,which may not adequately capture the complex biochemical properties of interest.Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties,and this is particularly true for the real industrial application scenario.Therefore,the desired testing datasets,will be small-size(∼10–100)experimental data for each protein,and involve as many proteins as possible and as many properties as possible,which is,however,lacking.Here,we present VenusMutHub,a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases,spanning 527 proteins across diverse functional properties including stability,activity,binding affinity,and selectivity.These datasets feature direct biochemical measurements rather than surrogate readouts,providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions.We evaluate 23 computational models across various methodological paradigms,such as sequence-based,structure-informed and evolutionary approaches.This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.展开更多
Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction metho...Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction methods for engineering applications.In this work,PFC2D software was used to simulate coal seam hydraulic fracturing.The results were used in a coupled mathematical model of the interaction between coal seam deformation and gas flow.The results show that the displacement and velocity of particles increase in the direction of minimum principal stress,and the cracks propagate in the direction of maximum principal stress.The gas pressure drop rate and permeability increase rate of the fracture model are higher than that of the non-fracture model.Both parameters decrease rapidly with an increase in the drainage time and approach 0.The longer the hydraulic fracturing time,the more complex the fracture network is,and the faster the gas pressure drops.However,the impact of fracturing on the gas drainage effect declines over time.As the fracturing time increases,the difference between the horizontal and vertical permeability increases.However,this difference decreases as the gas drainage time increases.The higher the initial void pressure,the faster the gas pressure drops,and the greater the permeability increase is.However,the influence of the initial void pressure on the permeability declines over time.The research results provide guidance for predicting the anti-reflection effect of hydraulic fracturing in underground coal mines.展开更多
Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw...Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.展开更多
This paper analyzes challenges encountered during the scale-up production of small molecule inhibitors,focusing on synthesis efficiency,solubility/bioavailability,quality control,stability/storage,and side effect pred...This paper analyzes challenges encountered during the scale-up production of small molecule inhibitors,focusing on synthesis efficiency,solubility/bioavailability,quality control,stability/storage,and side effect prediction/control.To address these issues,targeted solutions leveraging modern technologies are proposed and implemented:synthesis efficiency and purity were significantly enhanced through process optimization,green chemistry principles,and efficient catalysts;solubility and bioavailability were improved utilizing solid dispersion and nano-crystal technologies;process scale-up was optimized with online monitoring systems and continuous flow chemistry,ensuring product quality consistency;computer-aided drug design(CADD)was employed to predict and mitigate potential side effects.These integrated approaches effectively addressed key bottlenecks in the industrial-scale manufacturing of small molecule inhibitors.展开更多
文摘Background:Transrectal(TR)and transperineal(TP)biopsies are commonly used methods for diagnosing prostate cancer.However,their comparative effectiveness in conjunction with machine learning(ML)techniques remains underexplored.This study aimed to evaluate the predictive accuracy of ML algorithms in detecting prostate cancer using data derived from TR and TP biopsies.Methods:The clinical records of patients who underwent prostate biopsy at King Saud University Medical City and King Faisal Specialist Hospital and Research Centerin Riyadh,Saudi Arabia,between 2018 and 2025 were analyzed.Data were used to train and testMLmodels,including eXtreme Gradient Boosting(XGBoost),Decision Tree,Random Forest,and Extra Trees.Results:The two datasets are comparable.The models demonstrated exceptional performance,achieving accuracies of up to 96.49%and 95.56%on TP and TR biopsy datasets,respectively.The area under the curve(AUC)values were also high,reaching 0.9988 for TP and 0.9903 for TR biopsy predictions.Conclusion:These findings highlight the potential of MLto enhance the diagnostic accuracy of prostate cancer detection irrespective of the biopsy method.However,TP biopsy data showed marginally higher accuracy,possibly because of the lower risk of contamination.While ML holds great promise for transforming prostate cancer care,further research is needed to address limitations.Collaboration between clinicians,data scientists,and researchers is crucial to ensure the clinical relevance and interpretability of ML models.
基金financially supported by the General Program of National Natural Science Foundation of China(No.62373069)the Major Projects for Technological Transformation(No.H20201555)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project (No.CQYC202203091061)。
文摘The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.
文摘BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of life.However,the detection of TDs relies primarily on postoperative pathological examination,which may have a low detection rate due to sampling limitations.AIM To evaluate the spectral computed tomography(CT)parameters of primary tu-mors and the largest regional lymph nodes(LNs),to determine their value in predicting TDs in CRC.METHODS A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available.Patients were divided into the TDs+group and the TDs-group on the basis of their pathological results.Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured,including the normalized iodine concentration(NIC)in both the arte-rial and venous phases,and the LN-to-primary tumor ratio was calculated.Stati-stical methods were used to evaluate the diagnostic efficacy of each spectral para-meter.RESULTS Among the 121 CRC patients,33(27.2%)were confirmed to be TDs+.The risk of TDs positivity was greater in patients with positive LN metastasis,higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels.The NIC(LNs in both the arterial and venous phases),NIC(primary tumors in the venous phase),and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs(P<0.05).In mul-tivariate logistic regression analysis,the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs,demonstrating the highest diagnostic performance(area under the curve:0.812,sensitivity:0.879,specificity:0.648,cutoff value:1.145).CONCLUSION The spectral CT parameters of the primary colorectal tumor and the largest regional LNs,especially the LN-to-primary tumor ratio,have significant clinical value in predicting TDs in CRC.
基金supported by a grant from the Nursing Re-search Program of the First Affiliated Hospital of Zhejiang Univer-sity School of Medicine(No.2022ZYHL045).
文摘To the Editor:We read with great interest the recent article by Shi et al.pub-lished in Hepatobiliary Pancreatic Diseases International[1].Shi’s study was based on radiological features and clinical factors to construct a model to predict the effectiveness of first transarterial chemoembolization(TACE)treatment for hepatocellular carcinoma(HCC)in prolonging patient survival.The results showed that area under the receiver operating characteristic curve was 0.964 for the training cohort and 0.949 for the validation cohort.
基金Supported by Natural Science Foundation of Henan Province,No.242300421286the research and practice project of higher education reform in Henan Province,No.2023SJGLX124Ythe research and practice project of higher education reform of Zhengzhou University,No.2023ZZUJGXM114.
文摘BACKGROUND Early symptoms of hepatocellular carcinoma(HCC)are not obvious,and more than 70%of which does not receive radical hepatectomy,when first diagnosed.In recent years,molecular-targeted drugs combined with immunotherapy and other therapeutic methods have provided new treatment options for middle and advanced HCC(aHCC).Predicting the effect of targeted combined immunotherapy has become a hot topic in current research.AIM To explore the relationship between nodule enhancement in hepatobiliary phase and the efficacy of combined targeted immunotherapy for aHCC.METHODS Data from 56 patients with aHCC for magnetic resonance imaging with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid were retrospectively collected.Signal intensity of intrahepatic nodules was measured,and the hepatobiliary relative enhancement ratio(RER)was calculated.Progression-free survival(PFS)of patients with high and low reinforcement of HCC nodules was compared.The model was validated using receiver operating characteristic curves.Univariate and multivariate logistic regression and Kaplan-Meier analysis were performed to explore factors influencing the efficacy of targeted immunization and PFS.RESULTS Univariate and multivariate analyses revealed that the RER,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,and prognostic nutritional index were significantly associated with the efficacy of tyrosine kinase inhibitors combined with immunotherapy(P<0.05).The area under the curve of the RER for predicting the efficacy of tyrosine kinase inhibitors combined with anti-programmed death 1 antibody in patients with aHCC was 0.876(95%confidence interval:0.781-0.971,P<0.05),the optimal cutoff value was 0.904,diagnostic sensitivity was 87.5%,and specificity was 79.2%.Kaplan-Meier analysis showed that neutrophil-to-lymphocyte ratio<5,plateletto-lymphocyte ratio<300,prognostic nutritional index<45,and RER<0.9 significantly improved PFS.CONCLUSION AHCC nodules enhancement in the hepatobiliary stage was significantly correlated with PFS.Imaging information and immunological indicators had high predictive efficacy for targeted combined immunotherapy and were associated with PFS.
文摘Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.
文摘The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years from the time of patenting are required to make a new drug available for general prescription. Every new drug needs to be charac-
文摘Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.
基金supported by Science and Technology Innovation Key R&D Program of Chongqing(CSTB2024TIAD-STX0032,China)the Computational Biology Key Program of Shanghai Science and Technology Commission(23JS1400600,China)+3 种基金Shanghai Jiao Tong University Scientific and Technological Innovation Funds(21X010200843,China)and Science and Technology Innovation Key R&D Program of Chongqing(CSTB2022TIAD-STX0017,China)the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20241010the Student Innovation Center at Shanghai Jiao Tong University,and Shanghai Artificial Intelligence Laboratory.
文摘In protein engineering,while computational models are increasingly used to predict mutation effects,their evaluations primarily rely on high-throughput deep mutational scanning(DMS)experiments that use surrogate readouts,which may not adequately capture the complex biochemical properties of interest.Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties,and this is particularly true for the real industrial application scenario.Therefore,the desired testing datasets,will be small-size(∼10–100)experimental data for each protein,and involve as many proteins as possible and as many properties as possible,which is,however,lacking.Here,we present VenusMutHub,a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases,spanning 527 proteins across diverse functional properties including stability,activity,binding affinity,and selectivity.These datasets feature direct biochemical measurements rather than surrogate readouts,providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions.We evaluate 23 computational models across various methodological paradigms,such as sequence-based,structure-informed and evolutionary approaches.This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.
基金This work was supported by National Natural Science Foundation of China(52130409,52121003,52004291,51874314).
文摘Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction methods for engineering applications.In this work,PFC2D software was used to simulate coal seam hydraulic fracturing.The results were used in a coupled mathematical model of the interaction between coal seam deformation and gas flow.The results show that the displacement and velocity of particles increase in the direction of minimum principal stress,and the cracks propagate in the direction of maximum principal stress.The gas pressure drop rate and permeability increase rate of the fracture model are higher than that of the non-fracture model.Both parameters decrease rapidly with an increase in the drainage time and approach 0.The longer the hydraulic fracturing time,the more complex the fracture network is,and the faster the gas pressure drops.However,the impact of fracturing on the gas drainage effect declines over time.As the fracturing time increases,the difference between the horizontal and vertical permeability increases.However,this difference decreases as the gas drainage time increases.The higher the initial void pressure,the faster the gas pressure drops,and the greater the permeability increase is.However,the influence of the initial void pressure on the permeability declines over time.The research results provide guidance for predicting the anti-reflection effect of hydraulic fracturing in underground coal mines.
基金This research was partially supported by the National Natural Science Foundation of China(No.30470916).
文摘Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.
文摘This paper analyzes challenges encountered during the scale-up production of small molecule inhibitors,focusing on synthesis efficiency,solubility/bioavailability,quality control,stability/storage,and side effect prediction/control.To address these issues,targeted solutions leveraging modern technologies are proposed and implemented:synthesis efficiency and purity were significantly enhanced through process optimization,green chemistry principles,and efficient catalysts;solubility and bioavailability were improved utilizing solid dispersion and nano-crystal technologies;process scale-up was optimized with online monitoring systems and continuous flow chemistry,ensuring product quality consistency;computer-aided drug design(CADD)was employed to predict and mitigate potential side effects.These integrated approaches effectively addressed key bottlenecks in the industrial-scale manufacturing of small molecule inhibitors.
