Edited by Byung-Jun Yoon, Xiaoning Qian, Tamer Kahveci
Volume 19 Supplement 3
Selected original research articles from the Fourth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2017): bioinformatics
Research
Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
Boston, MA, USA20 August 2017
Related articles have been published as a supplement to BMC Genomics and BMC Systems Biology.
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Citation: BMC Bioinformatics 2018 19(Suppl 3):69
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Heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure
Many bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions ...
Citation: BMC Bioinformatics 2018 19(Suppl 3):70 -
Investigation of model stacking for drug sensitivity prediction
A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting fr...
Citation: BMC Bioinformatics 2018 19(Suppl 3):71 -
Simulating variance heterogeneity in quantitative genome wide association studies
Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects va...
Citation: BMC Bioinformatics 2018 19(Suppl 3):72 -
A Bayesian approach to determine the composition of heterogeneous cancer tissue
Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, g...
Citation: BMC Bioinformatics 2018 19(Suppl 3):90 -
Bayesian graphical models for computational network biology
Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of inter...
Citation: BMC Bioinformatics 2018 19(Suppl 3):63
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