Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks

Ranjani Murali, James Hemp, Victoria Orphan, Yonatan Bisk
doi: https://doi.org/10.1101/592808
Ranjani Murali
1Division of Geological and Planetary Sciences, California Institute of Technology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: m.ranjani@gmail.com
James Hemp
2School of Medicine, University of Utah
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: m.ranjani@gmail.com
Victoria Orphan
1Division of Geological and Planetary Sciences, California Institute of Technology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yonatan Bisk
3Paul G. Allen School of Computer Science & Engineering, University of Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Article Information

doi 
https://doi.org/10.1101/592808
History 
  • March 30, 2019.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Ranjani Murali1,*,
  2. James Hemp2,*,
  3. Victoria Orphan1 and
  4. Yonatan Bisk3
  1. 1Division of Geological and Planetary Sciences, California Institute of Technology
  2. 2School of Medicine, University of Utah
  3. 3Paul G. Allen School of Computer Science & Engineering, University of Washington
  1. ↵*Corresponding author; email: m.ranjani{at}gmail.com
Back to top
PreviousNext
Posted March 30, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks
Ranjani Murali, James Hemp, Victoria Orphan, Yonatan Bisk
bioRxiv 592808; doi: https://doi.org/10.1101/592808
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks
Ranjani Murali, James Hemp, Victoria Orphan, Yonatan Bisk
bioRxiv 592808; doi: https://doi.org/10.1101/592808

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4085)
  • Biochemistry (8755)
  • Bioengineering (6477)
  • Bioinformatics (23331)
  • Biophysics (11743)
  • Cancer Biology (9144)
  • Cell Biology (13242)
  • Clinical Trials (138)
  • Developmental Biology (7412)
  • Ecology (11364)
  • Epidemiology (2066)
  • Evolutionary Biology (15084)
  • Genetics (10397)
  • Genomics (14006)
  • Immunology (9115)
  • Microbiology (22036)
  • Molecular Biology (8777)
  • Neuroscience (47346)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2480)
  • Physiology (3703)
  • Plant Biology (8045)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2207)
  • Systems Biology (6014)
  • Zoology (1249)