For optimal results,retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection(FS)techniques.The aim of this review is to provide a thorou...For optimal results,retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection(FS)techniques.The aim of this review is to provide a thorough description of various,recent FS techniques.This review also focuses on the techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms.We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray datasets.An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques.A case study is provided to demonstrate the process of implementation,in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.展开更多
Objective To use high-throughput transcriptome sequencing data to screen for a set of non-invasive diagnostic biomarkers for assessing the risk of pulmonary tuberculosis(PTB)and predicting disease progression stages.M...Objective To use high-throughput transcriptome sequencing data to screen for a set of non-invasive diagnostic biomarkers for assessing the risk of pulmonary tuberculosis(PTB)and predicting disease progression stages.Methods A total of 37 effective microarray transcriptome datasets were collected from the National Center for Biotechnology Information(NCBI)from January 2015 to December 2024,covering five groups:control group,PTB group,extrapulmonary TB(EPTB)group,latent TB infection(LTBI)group,and other diseases(OD)group.展开更多
基金the Department of Science and Technology under the Interdisciplinary Cyber-Physical Systems Scheme(No.T-54)。
文摘For optimal results,retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection(FS)techniques.The aim of this review is to provide a thorough description of various,recent FS techniques.This review also focuses on the techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms.We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray datasets.An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques.A case study is provided to demonstrate the process of implementation,in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.
文摘Objective To use high-throughput transcriptome sequencing data to screen for a set of non-invasive diagnostic biomarkers for assessing the risk of pulmonary tuberculosis(PTB)and predicting disease progression stages.Methods A total of 37 effective microarray transcriptome datasets were collected from the National Center for Biotechnology Information(NCBI)from January 2015 to December 2024,covering five groups:control group,PTB group,extrapulmonary TB(EPTB)group,latent TB infection(LTBI)group,and other diseases(OD)group.
