PLoS Computational Biology Journal Impact Factor & Information

Publisher: Public Library of Science; International Society for Computational Biology, Public Library of Science

Journal description

PLoS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery.

Current impact factor: 4.87

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2011 Impact Factor 5.215
2010 Impact Factor 5.515
2009 Impact Factor 5.759
2008 Impact Factor 6.236
2007 Impact Factor 6.236
2006 Impact Factor 4.914
2005 Impact Factor

Impact factor over time

Impact factor
Year

Additional details

5-year impact 5.94
Cited half-life 3.60
Immediacy index 0.82
Eigenfactor 0.08
Article influence 2.71
Website PLoS Computational Biology website
Other titles PLOS computational biology (Online), PLOS computational biology, Public Library of Science computational biology, Computational biology
ISSN 1553-734X
OCLC 57176679
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Public Library of Science

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Creative Commons Attribution License
    • Eligible UK authors may deposit in OpenDepot
    • Publisher's version/PDF may be used
    • All titles are open access journals
  • Classification
    ‚Äč green

Publications in this journal

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    ABSTRACT: Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensive demographic, behavioural and epidemiological data available from Uganda. The tutorial describes history matching and emulation. History matching is an iterative procedure that reduces the simulator's input space by identifying and discarding areas that are unlikely to provide a good match to the empirical data. History matching relies on the computational efficiency of a Bayesian representation of the simulator, known as an emulator. Emulators mimic the simulator's behaviour, but are often several orders of magnitude faster to evaluate. In the case study, we use a 22 input simulator, fitting its 18 outputs simultaneously. After 9 iterations of history matching, a non-implausible region of the simulator input space was identified that was [Formula: see text] times smaller than the original input space. Simulator evaluations made within this region were found to have a 65% probability of fitting all 18 outputs. History matching and emulation are useful additions to the toolbox of infectious disease modellers. Further research is required to explicitly address the stochastic nature of the simulator as well as to account for correlations between outputs.
    PLoS Computational Biology 01/2015; 11(1):e1003968. DOI:10.1371/journal.pcbi.1003968.
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    ABSTRACT: Inter-individual variation in regulatory circuits controlling gene expression is a powerful source of functional information. The study of associations among genetic variants and gene expression provides important insights about cell circuitry but cannot specify whether and when potential variants dynamically alter their genetic effect during the course of response. Here we develop a computational procedure that captures temporal changes in genetic effects, and apply it to analyze transcription during inhibition of the TOR signaling pathway in segregating yeast cells. We found a high-order coordination of gene modules: sets of genes co-associated with the same genetic variant and sharing a common temporal genetic effect pattern. The temporal genetic effects of some modules represented a single state-transitioning pattern; for example, at 10-30 minutes following stimulation, genetic effects in the phosphate utilization module attained a characteristic transition to a new steady state. In contrast, another module showed an impulse pattern of genetic effects; for example, in the poor nitrogen sources utilization module, a spike up of a genetic effect at 10-20 minutes following stimulation reflected inter-individual variation in the timing (rather than magnitude) of response. Our analysis suggests that the same mechanism typically leads to both inter-individual variation and the temporal genetic effect pattern in a module. Our methodology provides a quantitative genetic approach to studying the molecular mechanisms that shape dynamic changes in transcriptional responses.
    PLoS Computational Biology 12/2014; 10(12):e1003984. DOI:10.1371/journal.pcbi.1003984
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    ABSTRACT: Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc-FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.
    PLoS Computational Biology 12/2014; 10(12):e1004018. DOI:10.1371/journal.pcbi.1004018