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. Methodology Article

    A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support

    Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is imp...

    Brian Connolly, K. Bretonnel Cohen, Daniel Santel, Ulya Bayram and John Pestian

    BMC Bioinformatics 2017 18:361

    Published on: 7 August 2017

  2. Correspondence

    Bioinformatics: indispensable, yet hidden in plain sight?

    Bioinformatics has multitudinous identities, organisational alignments and disciplinary links. This variety allows bioinformaticians and bioinformatic work to contribute to much (if not most) of life science r...

    Andrew Bartlett, Bart Penders and Jamie Lewis

    BMC Bioinformatics 2017 18:311

    Published on: 21 June 2017

  3. Research Article

    Learning rule sets from survival data

    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...

    Łukasz Wróbel, Adam Gudyś and Marek Sikora

    BMC Bioinformatics 2017 18:285

    Published on: 30 May 2017

  4. Research Article

    On the association analysis of CNV data: a fast and robust family-based association method

    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...

    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

    BMC Bioinformatics 2017 18:217

    Published on: 18 April 2017

  5. Research article

    Comparison of different cell type correction methods for genome-scale epigenetics studies

    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...

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

    BMC Bioinformatics 2017 18:216

    Published on: 14 April 2017

  6. Database

    DisBind: A database of classified functional binding sites in disordered and structured regions of intrinsically disordered proteins

    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 ...

    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

    BMC Bioinformatics 2017 18:206

    Published on: 5 April 2017

  7. Software

    StrAuto: automation and parallelization of STRUCTURE analysis

    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...

    Vikram E. Chhatre and Kevin J. Emerson

    BMC Bioinformatics 2017 18:192

    Published on: 24 March 2017

  8. Methodology article

    MOST: most-similar ligand based approach to target prediction

    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...

    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

    BMC Bioinformatics 2017 18:165

    Published on: 11 March 2017

  9. Software

    stpm: an R package for stochastic process model

    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...

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

    BMC Bioinformatics 2017 18:125

    Published on: 23 February 2017

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