Deep Learning Benchmarks on L1000 Gene Expression Data

Gene expression data can offer deep, physiological insights beyond the static coding of the genome alone. We believe that realizing this potential requires specialized, high-capacity machine learning methods capable of using underlying biological structure, but the development of such models is hamp...

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Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics 2020-11, Vol.17 (6), p.1846-1857
Hauptverfasser:	McDermott, Matthew B.A., Wang, Jennifer, Zhao, Wen-Ning, Sheridan, Steven D., Szolovits, Peter, Kohane, Isaac, Haggarty, Stephen J., Perlis, Roy H.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Benchmark testing Benchmarks Biological system modeling Classifiers Data models Datasets Decision trees Deep learning Gene expression gene expression data Genomes Learning algorithms Machine learning model development Neural networks
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