DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning

[1] Dembélé D(2014)Fold change rank ordering statistics: a new method for detecting differentially expressed genes BMC Bioinform 15 14-140

[2] Kastner P(2010)Differential expression analysis for sequence count data Genome Biol 11 106-21

[3] Anders S(2014)Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol 15 550-1951

[4] Huber W(2010)EdgeR: a bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 26 139-1132

[5] Love MI(2014)Voom: precision weights unlock linear model analysis tools for RNA-seq read counts Genome Biol 15 29-90629

[6] Huber W(2015)Gene expression data classification using support vector machine and mutual information-based gene selection Procedia Comput Sci 47 13-377

[7] Anders S(2007)Logistic regression for disease classification using microarray data: model selection in a large p and small n case Bioinformatics 23 1945-13

[8] Robinson MD(2006)Gene selection and classification of microarray data using random forest BMC Bioinform 7 3-900

[9] McCarthy DJ(2018)Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers? RNA 24 1119-7

[10] Smyth GK(2020)Deep learning for multi-tissue cancer classification of gene expressions (GeneXNet) IEEE Access 8 90615-2100

[1] Dembélé D(2014)Fold change rank ordering statistics: a new method for detecting differentially expressed genes BMC Bioinform 15 14-140

[2] Kastner P(2010)Differential expression analysis for sequence count data Genome Biol 11 106-21

[3] Anders S(2014)Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol 15 550-1951

[4] Huber W(2010)EdgeR: a bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 26 139-1132

[5] Love MI(2014)Voom: precision weights unlock linear model analysis tools for RNA-seq read counts Genome Biol 15 29-90629

[6] Huber W(2015)Gene expression data classification using support vector machine and mutual information-based gene selection Procedia Comput Sci 47 13-377

[7] Anders S(2007)Logistic regression for disease classification using microarray data: model selection in a large p and small n case Bioinformatics 23 1945-13

[8] Robinson MD(2006)Gene selection and classification of microarray data using random forest BMC Bioinform 7 3-900

[9] McCarthy DJ(2018)Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers? RNA 24 1119-7

[10] Smyth GK(2020)Deep learning for multi-tissue cancer classification of gene expressions (GeneXNet) IEEE Access 8 90615-2100