Deep learning for regulatory genomics

By Yongjin P. Park, Manolis Kellis. 2015

166 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Deep learning for computational biology (2016, neutral): “The applications we cover are deliberately broad to illustrate differences and commonalities between approaches; reviews focusing on specific domains can be found elsewhere (Park & Kellis, 2015; Gawehn et al , 2016; Leung et al , 2016; Mamoshina et al , 2016).”
Predicting enhancer-promoter interaction from genomic sequence with deep neural networks (2016, background): “In the past year, there have been many deep learning applications to regulatory genomics [1, 15, 21, 24, 25, 35].”
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017, methodology): “Deep learning has been relatively widely used by the bioinformatics (Alipanahi et al., 2015; Jiménez et al., 2017; Jurtz et al., 2017; Leung et al., 2014; Park and Kellis, 2015) and computational biology community (Angermueller et al.”
Machine Learning in Enzyme Engineering (2019, background): “Recent advances in the analysis of human genetic variation data in biomedicine and healthcare further increase the appeal of this approach for the design of beneficial mutations (12-14).”
Deep learning in bioinformatics (2016, background): “The depth of CNNs enables learning more complex patterns and can capture longer motifs, integrate cumulative effects of observed motifs, and eventually learn sophisticated regulatory codes [114].”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “DL is quickly becoming a crucial tool in unlocking the secretes of genomic codes in Computational Biology [Park and Kellis, 2015], with a sizable and rapidly growing body of literature summarized in reviews [Angermueller et al.”
Wind Turbine Gearbox Failure Identification With Deep Neural Networks (2017, methodology): “To accurately model the lubricant pressure of healthy gearboxes with SCADA data, the deep neural network (DNN), which has been considered as a powerful approach for modeling higher complexities [21]–[25], is applied.”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “Deep learning applications for detection of regulatory elements within the non-coding genome are beginning to emerge [7, 8, 72].”