期刊
METHODS
卷 166, 期 -, 页码 40-47出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2019.03.020
关键词
Deep learning; Transcription factors; ENCODE; DREAM
资金
- National Institute of Biomedical Imaging and Bioengineering, National Research Service Award from the University of California, Irvine, Center for Complex Biological Systems [EB009418]
- National Science Foundation Graduate Research Fellowship [DGE-1321846]
- NSF [IIS-1715017]
- NSF-Simons grant [DMS-1763272]
- NIH [U54-CA217378]
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, called FactorNet, to computationally impute the missing binding data. FactorNet trains on binding data from reference cell types to make predictions on testing cell types by leveraging a variety of features, including genomic sequences, genome annotations, gene expression, and signal data, such as DNase I cleavage. FactorNet implements several convenient strategies to reduce runtime and memory consumption. By visualizing the neural network models, we can interpret how the model predicts binding. We also investigate the variables that affect cross-cell type accuracy, and offer suggestions to improve upon this field. Our method ranked among the top teams in the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge, achieving first place on six of the 13 final round evaluation TF/cell type pairs, the most of any competing team. The FactorNet source code is publicly available, allowing users to reproduce our methodology from the ENCODE-DREAM Challenge.
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