New Results
GERV: A Statistical Method for Generative Evaluation of Regulatory Variants for Transcription Factor Binding
Haoyang Zeng, Tatsunori Hashimoto, Daniel D Kang, David K Gifford
doi: https://doi.org/10.1101/017392
Haoyang Zeng
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Tatsunori Hashimoto
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Daniel D Kang
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
David K Gifford
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
2Department of Stem Cell and Regenerative Biology, Harvard University and Harvard Medical School, Cambridge, MA 02138, USA
Article usage
Posted July 04, 2015.
GERV: A Statistical Method for Generative Evaluation of Regulatory Variants for Transcription Factor Binding
Haoyang Zeng, Tatsunori Hashimoto, Daniel D Kang, David K Gifford
bioRxiv 017392; doi: https://doi.org/10.1101/017392
Subject Area
Subject Areas
- Biochemistry (11755)
- Bioengineering (8757)
- Bioinformatics (29209)
- Biophysics (14980)
- Cancer Biology (12103)
- Cell Biology (17416)
- Clinical Trials (138)
- Developmental Biology (9425)
- Ecology (14187)
- Epidemiology (2067)
- Evolutionary Biology (18314)
- Genetics (12246)
- Genomics (16807)
- Immunology (11870)
- Microbiology (28101)
- Molecular Biology (11599)
- Neuroscience (60995)
- Paleontology (452)
- Pathology (1872)
- Pharmacology and Toxicology (3238)
- Physiology (4961)
- Plant Biology (10429)
- Synthetic Biology (2887)
- Systems Biology (7341)
- Zoology (1651)