English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The Fifth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2018) was held in Washington, D.C. on August 29, 2018. The workshop was organized in conjunction with the ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), the flagship conference of the ACM SIGBio. The CNB-MAC workshop aims to provide an international scientific forum for presenting recent advances in computational network biology that involve modeling, analysis, and control of biological systems and system-oriented analysis of large-scale OMICS data. CNB-MAC 2018 was co-chaired by Drs. Byung-Jun Yoon, Xiaoning Qian, Tamer Kahveci, and Ranadip Pal. The workshop featured 14 oral presentations and 9 poster presentations, which were selected by the workshop chairs based on the reviews performed by the technical committee members. With the generous support provided by the National Science Foundation (NSF), Student Travel Grants have been awarded to student authors of outstanding research papers and posters that have been invited for presentation at CNB-MAC 2018. Dr. Ranadip Pal served as the award chair for CNB-MAC 2018. 13 awardees were selected by the award committee after a careful review of the applications and the submitted work. Research papers presented at CNB-MAC 2018 After the workshop, eleven papers [1–11] were accepted for publication in the CNB-MAC 2018 partner journals: BMC Bioinformatics and BMC Genomics. In the following we provide a brief summary of these selected papers. Clinical studies often track dose-response curves of subjects over time which can be modeled separately in either time or dose without capturing the simultaneous evolution of the curves. Dhruba et al. [1] propose a parametric model to explain the dose-time response behavior and derive a recursive relation to predict dose-response curves over time for individuals using the corresponding dose-time proteomic data. By comparing the proposed recursive approach with individual dose-response predictive models at desired time points, Dhruba et al. [1] demonstrate that the recursive methodology provides a superior fit to the dose-time response behavior post drug application for both synthetic experimentation and pharmacological data from the HMS-LINCS database. Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, whose goal is to group data points based on some similarity criterion. In [2], Boluki et al. consider missing values in the context of optimal clustering, which finds an optimal clustering operator with reference to an underlying random labeled point process. They incorporate the missing value mechanism into the random labeled point process, and obtain the optimal clustering operator by marginalizing out the missing-value process. Optimal clustering with missing values obviates the need for imputation-based pre-processing of the given data, while at the same time

Selected research articles from the 2018 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)