Biological interpretation of deep neural network for phenotype prediction based on gene expression

[1] Libb MWNWS(2015)recht: Machine learning applications in genetics and genomics Nat Rev Genetics 16 321-32

[2] Daoud M(2019)A survey of neural network-based cancer prediction models from microarray data Artif Intell Med 97 204-214

[3] Mayo M(2020)Deep learning approach for microarray cancer data classification CAAI Trans Intell Technol 5 22-131

[4] Basavegowda H(2018)Phenotypes prediction from gene expression data with deep multilayer perceptron and unsupervised pre-training Int J Biosci Biochem Bioinform 8 125-19342

[5] Dagnew G(2018)A survey of methods for explaining black box models ACM Comput Surv 51 93-229

[6] Hanczar B(2017)A deep learning approach for cancer detection and relevant gene identification Pac Symp Biocomput 22 219-15

[7] Henriette M(2017)Methods for interpreting and understanding deep neural networks Digital Signal Process 73 1-46

[8] Ratovomanana T(2015)On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation PLOS ONE 10 1-214

[9] Zehraoui F(2016)Identification of cancer related genes using a comprehensive map of human gene expression PLoS One 11 6-275

[10] Guidotti R(2017)Cell matrix adhesions in cancer: the proteins that form the glue Oncotarget 8 48471-774

[1] Libb MWNWS(2015)recht: Machine learning applications in genetics and genomics Nat Rev Genetics 16 321-32

[2] Daoud M(2019)A survey of neural network-based cancer prediction models from microarray data Artif Intell Med 97 204-214

[3] Mayo M(2020)Deep learning approach for microarray cancer data classification CAAI Trans Intell Technol 5 22-131

[4] Basavegowda H(2018)Phenotypes prediction from gene expression data with deep multilayer perceptron and unsupervised pre-training Int J Biosci Biochem Bioinform 8 125-19342

[5] Dagnew G(2018)A survey of methods for explaining black box models ACM Comput Surv 51 93-229

[6] Hanczar B(2017)A deep learning approach for cancer detection and relevant gene identification Pac Symp Biocomput 22 219-15

[7] Henriette M(2017)Methods for interpreting and understanding deep neural networks Digital Signal Process 73 1-46

[8] Ratovomanana T(2015)On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation PLOS ONE 10 1-214

[9] Zehraoui F(2016)Identification of cancer related genes using a comprehensive map of human gene expression PLoS One 11 6-275

[10] Guidotti R(2017)Cell matrix adhesions in cancer: the proteins that form the glue Oncotarget 8 48471-774