Work

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

Year: 2019

Type: article

Source: Methods

DOI: 10.1016/j.ymeth.2019.03.020

Authors: Daniel Quang, Xiaohui Xie

Institution: University of California, Irvine

Language: English

Topic: Genomics and Chromatin Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, National Sleep Foundation, Israel National Road Safety Authority, National Institutes of Health, University of California, Irvine, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, Center for Complex Biological Systems, University of California, Irvine

Awards: Graduate Research Fellowship Program (GRFP), Complexity, Cooperation and Community in Cancer, G7587780446, Mathematical, Computational and Systems Biology, III: Small: Integrating and Interpreting Heterogeneous Genomic Data Through Deep Learning

OpenAlex ID: W2951410692

FWCI: 8.424

Cites: 67

Cited by: 196

Related to: 10