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

Identifying high-priority proteins across the human diseasome using semantic similarity

Edward Lau, Vidya Venkatraman, Cody T Thomas, Jennifer E Van Eyk, Maggie PY Lam
doi: https://doi.org/10.1101/309203
Edward Lau
1Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vidya Venkatraman
2Advanced Clinical Biosystems Research Institute, Department of Medicine and The Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cody T Thomas
3Department of Medicine, Division of Cardiology, Consortium for Fibrosis Research and Translation, Anschutz Medical Campus, University of Colorado Denver, CO.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jennifer E Van Eyk
2Advanced Clinical Biosystems Research Institute, Department of Medicine and The Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Maggie PY Lam
3Department of Medicine, Division of Cardiology, Consortium for Fibrosis Research and Translation, Anschutz Medical Campus, University of Colorado Denver, CO.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: maggie.lam@ucdenver.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Knowledge of “popular proteins” has been a focus of multiple Human Proteome Organization (HUPO) initiatives and can guide the development of proteomics assays targeting important disease pathways. We report here an updated method to identify prioritized protein lists from the research literature, and apply it to catalog lists of important proteins across multiple cell types, sub-anatomical regions, and disease phenotypes of interest. We provide a systematic collection of popular proteins across 10,129 human diseases as defined by the Disease Ontology, 10,642 disease phenotypes defined by Human Phenotype Ontology, and 2,370 cellular pathways defined by Pathway Ontology. This strategy allows instant retrieval of popular proteins across the human “diseasome”, and further allows reverse queries from protein to disease, enabling functional analysis of experimental protein lists using bibliometric annotations.

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 4.0 International license.
Back to top
PreviousNext
Posted April 29, 2018.
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.
Identifying high-priority proteins across the human diseasome using semantic similarity
(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
Identifying high-priority proteins across the human diseasome using semantic similarity
Edward Lau, Vidya Venkatraman, Cody T Thomas, Jennifer E Van Eyk, Maggie PY Lam
bioRxiv 309203; doi: https://doi.org/10.1101/309203
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Identifying high-priority proteins across the human diseasome using semantic similarity
Edward Lau, Vidya Venkatraman, Cody T Thomas, Jennifer E Van Eyk, Maggie PY Lam
bioRxiv 309203; doi: https://doi.org/10.1101/309203

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 (4230)
  • Biochemistry (9123)
  • Bioengineering (6766)
  • Bioinformatics (23968)
  • Biophysics (12109)
  • Cancer Biology (9509)
  • Cell Biology (13753)
  • Clinical Trials (138)
  • Developmental Biology (7622)
  • Ecology (11674)
  • Epidemiology (2066)
  • Evolutionary Biology (15490)
  • Genetics (10630)
  • Genomics (14310)
  • Immunology (9473)
  • Microbiology (22821)
  • Molecular Biology (9086)
  • Neuroscience (48914)
  • Paleontology (355)
  • Pathology (1480)
  • Pharmacology and Toxicology (2566)
  • Physiology (3839)
  • Plant Biology (8322)
  • Scientific Communication and Education (1468)
  • Synthetic Biology (2295)
  • Systems Biology (6180)
  • Zoology (1299)