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10.36255/exonpublications.bioinformatics.2021.ch4 http://scihub22266oqcxt.onion/10.36255/exonpublications.bioinformatics.2021.ch4 33877765!ä!33877765 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics-/-ä 2021 ; ä (ä): ä Nephropedia Template TP {{tp|p=33877765|t=2021. Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data |pdf=|usr=}}{{33877765}} gab.com Text Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data JO: Bioinformatics-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=33877765 #FOAvip Biomarkers are of great importance in many fields, such as cancer research, toxicology, diagnosis and treatment of diseases, and to better understand biological response mechanisms to internal or external intervention. High-throughput gene expression profiling technologies, such as DNA microarrays and RNA sequencing, provide large gene expression data sets which enable data-driven biomarker discovery. Traditional statistical tests have been the mainstream for identifying differentially expressed genes as biomarkers. In recent years, machine learning techniques such as feature selection have gained more popularity. Given many options, picking the most appropriate method for a particular data becomes essential. Different evaluation metrics have therefore been proposed. Being evaluated on different aspects, a method's varied performance across different datasets leads to the idea of integrating multiple methods. Many integration strategies are proposed and have shown great potential. This chapter gives an overview of the current research advances and existing issues in biomarker discovery using machine learning approaches on gene expression data. Twit Text FOAVip Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data ????Bioinformatics-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=33877765 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33877765 #FOAvip English Wikipedia <ref>{{Cite pmid|33877765}}</ref> Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data #MMPMID33877765 Zhang X; Jonassen I; Goksoyr A Bioinformatics-/-ä 2021[Mar]; ä (ä): ä PMID33877765show ga Biomarkers are of great importance in many fields, such as cancer research, toxicology, diagnosis and treatment of diseases, and to better understand biological response mechanisms to internal or external intervention. High-throughput gene expression profiling technologies, such as DNA microarrays and RNA sequencing, provide large gene expression data sets which enable data-driven biomarker discovery. Traditional statistical tests have been the mainstream for identifying differentially expressed genes as biomarkers. In recent years, machine learning techniques such as feature selection have gained more popularity. Given many options, picking the most appropriate method for a particular data becomes essential. Different evaluation metrics have therefore been proposed. Being evaluated on different aspects, a method's varied performance across different datasets leads to the idea of integrating multiple methods. Many integration strategies are proposed and have shown great potential. This chapter gives an overview of the current research advances and existing issues in biomarker discovery using machine learning approaches on gene expression data. äMachine Learning Approaches for Biomarker Discovery Using Gene Express(epmc).pdf DeepDyve Pubget Overpricing