Edited by Byung-Jun Yoon, Xiaoning Qian and Tamer Kahveci
Citation: BMC Bioinformatics 2017 18(Suppl 4):159
Volume 18 Supplement 4
Selected original research articles from the Third International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2016): 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.
Seattle, WA, USA02-Oct-16
Other articles from the conference have been published as supplements to BMC Genomics and BMC Systems Biology.
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Towards targeted combinatorial therapy design for the treatment of castration-resistant prostate cancer
Prostate cancer is one of the most prevalent cancers in males in the United States and amongst the leading causes of cancer related deaths. A particularly virulent form of this disease is castration-resistant ...
Citation: BMC Bioinformatics 2017 18(Suppl 4):134
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Combination therapy design for maximizing sensitivity and minimizing toxicity
Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without m...
Citation: BMC Bioinformatics 2017 18(Suppl 4):116
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Interpretive time-frequency analysis of genomic sequences
Time-Frequency (TF) analysis has been extensively used for the analysis of non-stationary numeric signals in the past decade. At the same time, recent studies have statistically confirmed the non-stationarity ...
Citation: BMC Bioinformatics 2017 18(Suppl 4):154
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Comparison of tissue/disease specific integrated networks using directed graphlet signatures
Analysis of integrated genome-scale networks is a challenging problem due to heterogeneity of high-throughput data. There are several topological measures, such as graphlet counts, for characterization of biol...
Citation: BMC Bioinformatics 2017 18(Suppl 4):135
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Comprehensive evaluation of RNA-seq quantification methods for linearity
Deconvolution is a mathematical process of resolving an observed function into its constituent elements. In the field of biomedical research, deconvolution analysis is applied to obtain single cell-type or tis...
Citation: BMC Bioinformatics 2017 18(Suppl 4):117
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