Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data
Authors
Keywords
-
Journal
BMC BIOINFORMATICS
Volume 21, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-03-20
DOI
10.1186/s12859-020-3427-8
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
- (2018) Jianfang Liu et al. CELL
- Opportunities and obstacles for deep learning in biology and medicine
- (2018) Travers Ching et al. Journal of the Royal Society Interface
- Improving the value of public RNA-seq expression data by phenotype prediction
- (2018) Shannon E Ellis et al. NUCLEIC ACIDS RESEARCH
- Massive mining of publicly available RNA-seq data from human and mouse
- (2018) Alexander Lachmann et al. Nature Communications
- Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
- (2018) Kourosh Zarringhalam et al. Scientific Reports
- Deep generative modeling for single-cell transcriptomics
- (2018) Romain Lopez et al. NATURE METHODS
- Reproducible RNA-seq analysis using recount2
- (2017) Leonardo Collado-Torres et al. NATURE BIOTECHNOLOGY
- The Reactome Pathway Knowledgebase
- (2017) Antonio Fabregat et al. NUCLEIC ACIDS RESEARCH
- Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation
- (2016) Mehmet Gönen BMC BIOINFORMATICS
- Association Between Response to Etrolizumab and Expression of Integrin αE and Granzyme A in Colon Biopsies of Patients With Ulcerative Colitis
- (2016) Gaik W. Tew et al. GASTROENTEROLOGY
- Applications of Deep Learning in Biomedicine
- (2016) Polina Mamoshina et al. MOLECULAR PHARMACEUTICS
- In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
- (2016) Ivan V. Ozerov et al. Nature Communications
- MicroRNAs Classify Different Disease Behavior Phenotypes of Crohnʼs Disease and May Have Prognostic Utility
- (2015) Bailey C. E. Peck et al. INFLAMMATORY BOWEL DISEASES
- RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications
- (2015) Mayte Suárez-Fariñas et al. JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY
- Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance
- (2015) William R. Swindell et al. Genome Medicine
- Proportionality: A Valid Alternative to Correlation for Relative Data
- (2015) David Lovell et al. PLoS Computational Biology
- Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks
- (2014) Kourosh Zarringhalam et al. BIOINFORMATICS
- Activation of the Aryl Hydrocarbon Receptor Dampens the Severity of Inflammatory Skin Conditions
- (2014) Paola Di Meglio et al. IMMUNITY
- Detecting and correcting systematic variation in large-scale RNA sequencing data
- (2014) Sheng Li et al. NATURE BIOTECHNOLOGY
- Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism
- (2014) J. Fadista et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis
- (2014) Andrew D Fernandes et al. Microbiome
- CellMix: a comprehensive toolbox for gene expression deconvolution
- (2013) R. Gaujoux et al. BIOINFORMATICS
- Causal analysis approaches in Ingenuity Pathway Analysis
- (2013) Andreas Krämer et al. BIOINFORMATICS
- TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors
- (2013) Konika Chawla et al. BIOINFORMATICS
- Representation Learning: A Review and New Perspectives
- (2013) Y. Bengio et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories
- (2013) Peter A C 't Hoen et al. NATURE BIOTECHNOLOGY
- The Genotype-Tissue Expression (GTEx) project
- (2013) John Lonsdale et al. NATURE GENETICS
- NCBI GEO: archive for functional genomics data sets—update
- (2012) Tanya Barrett et al. NUCLEIC ACIDS RESEARCH
- Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples
- (2012) Günter P. Wagner et al. THEORY IN BIOSCIENCES
- ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets
- (2011) Alyssa C Frazee et al. BMC BIOINFORMATICS
- RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
- (2011) Bo Li et al. BMC BIOINFORMATICS
- Gene expression deconvolution in clinical samples
- (2011) Yingdong Zhao et al. Genome Medicine
- Cell type–specific gene expression differences in complex tissues
- (2010) Shai S Shen-Orr et al. NATURE METHODS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started