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The COMBREX Project: Design, Methodology, and Initial Results

Full Text: journal.pbio.1001638.PDF PDF

COMBREX (COMputational BRidges to EXperiments, http://combrex.bu.edu) is an NIH-funded enterprise that has brought computational and experimental biologists together, with the goal of greatly improving our overall understanding of microbial protein function [1],[2]. Since its inception, it has made significant progress toward the following goals: identifying the minority of proteins that have already been experimentally characterized, serving as a public repository of novel protein function predictions made by diverse methods, producing a clear chain of evidence from experiment to prediction, identifying (“recommending”) those functional predictions whose verification will contribute most to our overall understanding of protein function, and actually funding the experiments to test function. The recommendation system is a proof of concept based on active learning principles and includes, for a given protein, criteria including phylogenetic distribution of its protein family, biological and clinical phenotypes associated with it, the availability of protein structure data, and its sequence distance from experimentally determined proteins or from the other proteins in its family.

Citation

B. Anton, Y. Chang, P. Brown, H. Choi, L. Faller, J. Guleria, Z. Hu, N. Klitgord, V. Mazumdar, M. McGettrick, L. Osmani, R. Pokrzywa, J. Rachlin, R. Swaminathan, C. Monahan, K. Rochussen, K. Tao, A. Bhagwat, S. Brenner, L. Columbus, A. Fomenkov, G. Gadda, R. Morgan, A. Osterman, D. Rodionov, I. Rodionova, K. Rudd, D. Söll, J. Spain, S. Xu, A. Bateman, R. Blumenthal, J. Bollinger, I. Friedberg, M. Galperin, J. Gobeill, D. Haft, J. Hunt, P. Karp, W. Klimke, C. Krebs, M. Martin, J. Miller, C. O'Donovan, B. Palsson, P. Ruch, A. Setterdahl, G. Sutton, J. Tate, A. Yakunin, D. Tchigvintsev, G. Plata, J. Hu, R. Greiner, D. Horn, K. Sjölander, S. Salzberg, D. Vitkup, S. Letovsky, D. Segrè, C. DeLisi, R. Roberts, M. Steffen, S. Kasif. "The COMBREX Project: Design, Methodology, and Initial Results". PLOS Biology, 11(8), pp e1001638, August 2013.

Keywords: bioinformatics, machine learning, COMBREX
Category: In Journal
Web Links: DOI
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BibTeX

@article{Anton+al:PlosBiology13,
  author = {Brian P. Anton and Yi-Chien Chang and Peter Brown and Han-Pil Choi
    and Lina L. Faller and Jyotsna Guleria and Zhenjun Hu and Niels Klitgord
    and Varun Mazumdar and Mark McGettrick and Lais Osmani and Revonda Pokrzywa
    and John Rachlin and Rajeswari Swaminathan and Caitlin Monahan and Krista
    Rochussen and Kevin Tao and Ashok S. Bhagwat and Steven E. Brenner and
    Linda Columbus and Alexey Fomenkov and Giovanni Gadda and Richard D. Morgan
    and Andrei L. Osterman and Dmitry A. Rodionov and Irina A. Rodionova and
    Kenneth E. Rudd and Dieter Söll and James Spain and Shuang-yong Xu and
    Alex Bateman and Robert M. Blumenthal and J. Martin Bollinger and Iddo
    Friedberg and Michael Y. Galperin and Julien Gobeill and Daniel Haft and
    John Hunt and Peter Karp and William Klimke and Carsten Krebs and Maria J.
    Martin and Jeffrey H. Miller and Claire O'Donovan and Bernhard Palsson and
    Patrick Ruch and Aaron Setterdahl and Granger Sutton and John Tate and
    Alexander Yakunin and Dmitri Tchigvintsev and Germán Plata and Jie Hu
    and Russ Greiner and David Horn and Kimmen Sjölander and Steven L.
    Salzberg and Dennis Vitkup and Stanley Letovsky and Daniel Segrè and
    Charles DeLisi and Richard J. Roberts and Martin Steffen and Simon Kasif},
  title = {The COMBREX Project: Design, Methodology, and Initial Results},
  Volume = "11",
  Number = "8",
  Pages = { e1001638},
  journal = {PLOS Biology},
  year = 2013,
}

Last Updated: February 10, 2020
Submitted by Sabina P

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