Improved prediction of smoking status via isoform-aware RNA-seq deep learning models
暂无分享,去创建一个
Russell Bowler | Michael H. Cho | Edwin K. Silverman | Peter J. Castaldi | Craig P. Hersh | Adel Boueiz | Aria Masoomi | Jennifer G. Dy | Tingting Zhao | Zifeng Wang | Zhonghui Xu | Sool Lee
[1] Thomas R. Gingeras,et al. STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..
[2] D. Botstein,et al. Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[3] Thomas Lengauer,et al. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure , 2006, Bioinform..
[4] Anne E Carpenter,et al. Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.
[5] M. Peters,et al. A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking. , 2016, Human molecular genetics.
[6] Wolfgang Huber,et al. Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues , 2017, Nucleic acids research.
[7] Eric T. Wang,et al. Alternative Isoform Regulation in Human Tissue Transcriptomes , 2008, Nature.
[8] Aristotelis Tsirigos,et al. A Deep Learning Framework for Predicting Response to Therapy in Cancer. , 2019, Cell reports.
[9] Chris Williams,et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization , 2012, Bioinform..
[10] May D. Wang,et al. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction , 2015, Genome Biology.
[11] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[12] Shuifang Zhu,et al. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads , 2014, BMC Bioinformatics.
[13] Mateusz Maciejewski,et al. Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data , 2020, BMC Bioinformatics.
[14] Andrew M. Gross,et al. Network-based stratification of tumor mutations , 2013, Nature Methods.
[15] Y. Shoenfeld,et al. Effects of tobacco smoke on immunity, inflammation and autoimmunity. , 2010, Journal of autoimmunity.
[16] M. Swanson,et al. RNA mis-splicing in disease , 2015, Nature Reviews Genetics.
[17] E. Regan,et al. Genetic Epidemiology of COPD (COPDGene) Study Design , 2011, COPD.
[18] Sandrine Dudoit,et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.
[19] Philip Beineke,et al. A whole blood gene expression-based signature for smoking status , 2012, BMC Medical Genomics.
[20] D. Botstein,et al. Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[21] Sungroul Kim. Overview of Cotinine Cutoff Values for Smoking Status Classification , 2016, International journal of environmental research and public health.
[22] David A. Knowles,et al. RNA splicing is a primary link between genetic variation and disease , 2016, Science.
[23] Jennifer G. Dy,et al. COPD subtypes identified by network-based clustering of blood gene expression. , 2016, Genomics.
[24] Bonnie Berger,et al. Making sense out of massive data by going beyond differential expression , 2012, Proceedings of the National Academy of Sciences.
[25] E. Silverman,et al. RNA sequencing identifies novel non-coding RNA and exon-specific effects associated with cigarette smoking , 2017, BMC Medical Genomics.
[26] M. Cronin,et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.
[27] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..