Skip to main content
Duke University Libraries
DukeSpace Scholarship by Duke Authors
  • Login
  • Ask
  • Menu
  • Login
  • Ask a Librarian
  • Search & Find
  • Using the Library
  • Research Support
  • Course Support
  • Libraries
  • About
View Item 
  •   DukeSpace
  • Duke Scholarly Works
  • Scholarly Articles
  • View Item
  •   DukeSpace
  • Duke Scholarly Works
  • Scholarly Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.

Thumbnail
View / Download
543.0 Kb
Date
2011-07
Authors
Xing, Chuanhua
Dunson, David B
Repository Usage Stats
124
views
82
downloads
Abstract
Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.
Type
Journal article
Subject
Algorithms
Bayes Theorem
Computational Biology
Databases, Protein
Humans
Logistic Models
Protein Interaction Mapping
Proteins
ROC Curve
Reproducibility of Results
Permalink
https://hdl.handle.net/10161/15602
Published Version (Please cite this version)
10.1371/journal.pcbi.1002110
Publication Info
Xing, Chuanhua; & Dunson, David B (2011). Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions. PLoS Comput Biol, 7(7). pp. e1002110. 10.1371/journal.pcbi.1002110. Retrieved from https://hdl.handle.net/10161/15602.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
Collections
  • Scholarly Articles
More Info
Show full item record

Scholars@Duke

Dunson

David B. Dunson

Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more.  We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers.  We are also interested in new inference framework and in studying theoretical properties
Open Access

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy

Rights for Collection: Scholarly Articles


Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info

Related items

Showing items related by title, author, creator, and subject.

  • Thumbnail

    LKB1 Loss induces characteristic patterns of gene expression in human tumors associated with NRF2 activation and attenuation of PI3K-AKT. 

    Kaufman, Jacob M; Amann, Joseph M; Park, Kyungho; Arasada, Rajeswara Rao; Li, Haotian; Shyr, Yu; Carbone, David P (Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2014-06)
    Inactivation of serine/threonine kinase 11 (STK11 or LKB1) is common in lung cancer, and understanding the pathways and phenotypes altered as a consequence will aid the development of targeted therapeutic strategies. Gene ...
  • Thumbnail

    Amino acid permeases require COPII components and the ER resident membrane protein Shr3p for packaging into transport vesicles in vitro. 

    Kuehn, MJ; Schekman, R; Ljungdahl, PO (J Cell Biol, 1996-11)
    In S. cerevisiae lacking SHR3, amino acid permeases specifically accumulate in membranes of the endoplasmic reticulum (ER) and fail to be transported to the plasma membrane. We examined the requirements of transport of the ...
  • Thumbnail

    G protein beta gamma subunits stimulate phosphorylation of Shc adapter protein. 

    Touhara, K; Hawes, BE; van Biesen, T; Lefkowitz, RJ (Proc Natl Acad Sci U S A, 1995-09-26)
    The mechanism of mitogen-activated protein (MAP) kinase activation by pertussis toxin-sensitive Gi-coupled receptors is known to involve the beta gamma subunits of heterotrimeric G proteins (G beta gamma), p21ras activation, ...

Make Your Work Available Here

How to Deposit

Browse

All of DukeSpaceCommunities & CollectionsAuthorsTitlesTypesBy Issue DateDepartmentsAffiliations of Duke Author(s)SubjectsBy Submit DateThis CollectionAuthorsTitlesTypesBy Issue DateDepartmentsAffiliations of Duke Author(s)SubjectsBy Submit Date

My Account

LoginRegister

Statistics

View Usage Statistics
Duke University Libraries

Contact Us

411 Chapel Drive
Durham, NC 27708
(919) 660-5870
Perkins Library Service Desk

Digital Repositories at Duke

  • Report a problem with the repositories
  • About digital repositories at Duke
  • Accessibility Policy
  • Deaccession and DMCA Takedown Policy

TwitterFacebookYouTubeFlickrInstagramBlogs

Sign Up for Our Newsletter
  • Re-use & Attribution / Privacy
  • Harmful Language Statement
  • Support the Libraries
Duke University