Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis...

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Vydáno v:	PLoS computational biology Ročník 14; číslo 4; s. e1006076
Hlavní autoři:	Ching, Travers, Zhu, Xun, Garmire, Lana X.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 10.04.2018 Public Library of Science (PLoS)
Témata:	Angiogenesis Artificial neural networks Bioengineering Biology and Life Sciences Brain research Cancer Computation Computational Biology Computer and Information Sciences Data analysis Databases, Nucleic Acid - statistics & numerical data Epidemiology Female Gene expression Gene Expression Profiling - statistics & numerical data Gene Regulatory Networks Generalized linear models Genomes Genomics Hazards High-Throughput Nucleotide Sequencing - statistics & numerical data Humans Kaplan-Meier Estimate Kidney cancer Kinases Male Medical prognosis Medical research Medicine and Health Sciences Metabolic Networks and Pathways - genetics Methods Neoplasms - genetics Neoplasms - metabolism Neoplasms - mortality Neural networks Neural Networks, Computer Oncology Patients Physical Sciences Predictions Prognosis Proportional Hazards Models Proteins Regression analysis Research and Analysis Methods Ribonucleic acid RNA Sequence Analysis, RNA - statistics & numerical data Software Survival Survival Analysis Writing United States > US
ISSN:	1553-7358, 1553-734X, 1553-7358
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Popis
Shrnutí:	Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.
Bibliografie:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-3 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1006076