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BMC Bioinformatics

Results and data

This section incorporates novel results, useful tools, and methods using bioinformatics in new ways, including but not limited to: biological conclusions that are only supported by bioinformatics analyses.

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  1. Content type: Research Article

    Survival analysis is an important element of reasoning from data. Applied in a number of fields, it has become particularly useful in medicine to estimate the survival rate of patients on the basis of their co...

    Authors: Łukasz Wróbel, Adam Gudyś and Marek Sikora

    Citation: BMC Bioinformatics 2017 18:285

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  2. Content type: Research Article

    Copy number variation (CNV) is known to play an important role in the genetics of complex diseases and several methods have been proposed to detect association of CNV with phenotypes of interest. Statistical m...

    Authors: Meiling Liu, Sanghoon Moon, Longfei Wang, Sulgi Kim, Yeon-Jung Kim, Mi Yeong Hwang, Young Jin Kim, Robert C. Elston, Bong-Jo Kim and Sungho Won

    Citation: BMC Bioinformatics 2017 18:217

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  3. Content type: Research article

    Whole blood is frequently utilized in genome-wide association studies of DNA methylation patterns in relation to environmental exposures or clinical outcomes. These associations can be confounded by cellular h...

    Authors: Akhilesh Kaushal, Hongmei Zhang, Wilfried J. J. Karmaus, Meredith Ray, Mylin A. Torres, Alicia K. Smith and Shu-Li Wang

    Citation: BMC Bioinformatics 2017 18:216

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  4. Content type: Database

    Intrinsically unstructured or disordered proteins function via interacting with other molecules. Annotation of these binding sites is the first step for mapping functional impact of genetic variants in coding ...

    Authors: Jia-Feng Yu, Xiang-Hua Dou, Yu-Jie Sha, Chun-Ling Wang, Hong-Bo Wang, Yi-Ting Chen, Feng Zhang, Yaoqi Zhou and Ji-Hua Wang

    Citation: BMC Bioinformatics 2017 18:206

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  5. Content type: Database

    Systematic, standardized and in-depth phenotyping and data analyses of rodent behaviour empowers gene-function studies, drug testing and therapy design. However, no data repositories are currently available fo...

    Authors: Bastijn Koopmans, August B. Smit, Matthijs Verhage and Maarten Loos

    Citation: BMC Bioinformatics 2017 18:200

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  6. Content type: Software

    Population structure inference using the software STRUCTURE has become an integral part of population genetic studies covering a broad spectrum of taxa including humans. The ever-expanding size of genetic data...

    Authors: Vikram E. Chhatre and Kevin J. Emerson

    Citation: BMC Bioinformatics 2017 18:192

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  7. Content type: Methodology article

    Finding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved issue. Few...

    Authors: Sinan Abo Alchamlat and Frédéric Farnir

    Citation: BMC Bioinformatics 2017 18:184

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  8. Content type: Methodology article

    Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested t...

    Authors: Tao Huang, Hong Mi, Cheng-yuan Lin, Ling Zhao, Linda L. D. Zhong, Feng-bin Liu, Ge Zhang, Ai-ping Lu and Zhao-xiang Bian

    Citation: BMC Bioinformatics 2017 18:165

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  9. Content type: Software

    The lipid scrambling activity of protein extracts and purified scramblases is typically measured using a fluorescence-based assay. While the assay has yielded insight into the scramblase activity in crude memb...

    Authors: Richard J. Cotton, Birgit Ploier, Michael A. Goren, Anant K. Menon and Johannes Graumann

    Citation: BMC Bioinformatics 2017 18:146

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  10. Content type: Software

    The Stochastic Process Model (SPM) represents a general framework for modeling the joint evolution of repeatedly measured variables and time-to-event outcomes observed in longitudinal studies, i.e., SPM relate...

    Authors: Ilya Y. Zhbannikov, Konstantin Arbeev, Igor Akushevich, Eric Stallard and Anatoliy I. Yashin

    Citation: BMC Bioinformatics 2017 18:125

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  11. Content type: Methodology article

    Intra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorith...

    Authors: Andrew E. Teschendorff, Charles E. Breeze, Shijie C. Zheng and Stephan Beck

    Citation: BMC Bioinformatics 2017 18:105

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  12. Content type: Research Article

    Loss-of-function phenotypes are widely used to infer gene function using the principle that similar phenotypes are indicative of similar functions. However, converting phenotypic to functional annotations requ...

    Authors: Beatriz Serrano-Solano, Antonio Díaz Ramos, Jean-Karim Hériché and Juan A. G. Ranea

    Citation: BMC Bioinformatics 2017 18:96

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    The Erratum to this article has been published in BMC Bioinformatics 2017 18:194

  13. Content type: Software

    MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estim...

    Authors: Raja H. Ali, Mikael Bark, Jorge Miró, Sayyed A. Muhammad, Joel Sjöstrand, Syed M. Zubair, Raja M. Abbas and Lars Arvestad

    Citation: BMC Bioinformatics 2017 18:97

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2016 Journal Metrics

  • Citation Impact
    2.448 - 2-year Impact Factor
    3.450 - 5-year Impact Factor
    0.946 - Source Normalized Impact per Paper (SNIP)
    1.467 - SCImago Journal Rank (SJR)

    1405 Usage Factor

    Social Media Impact
    816 mentions