Selected research articles from the 2017 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)

Introduction The Fourth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2017) was held in Boston, Massachusetts on August 20, 2017. 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, as in previous years. 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 2017 was co-chaired by Drs. Byung-Jun Yoon, Xiaoning Qian, and Tamer Kahveci. The workshop featured 14 oral presentations, which were carefully selected by the workshop chairs based on thorough reviews by the technical committee members. The final presentations at the workshop included 12 original research, one review, and one extended abstract. 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 2017. Dr. Ranadip Pal served as the award chair for CNB-MAC 2017, and 9 awardees were selected by the award committee after a careful review of the applications and the submitted work.

[1]  Jian Yu,et al.  Overlapping Functional Modules Detection in PPI Network with Pairwise Constrained Nonnegative Matrix Tri-Factorization , 2017, BCB.

[2]  Kwong-Sak Leung,et al.  SMILE: a novel procedure for subcellular module identification with localisation expansion , 2018, IET systems biology.

[3]  Edward R. Dougherty,et al.  Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks , 2018, BMC Systems Biology.

[4]  Aniruddha Datta,et al.  A Bayesian approach to determine the composition of heterogeneous cancer tissue , 2018, BMC Bioinformatics.

[5]  Jianhua Ruan,et al.  Robust edge-based biomarker discovery improves prediction of breast cancer metastasis , 2020, BMC Bioinformatics.

[6]  Han Zhang,et al.  Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application , 2018, BMC Genomics.

[7]  Ranadip Pal,et al.  Investigation of model stacking for drug sensitivity prediction , 2017, BMC Bioinformatics.

[8]  Stefano Lonardi,et al.  DeeplyEssential: a deep neural network for predicting essential genes in microbes , 2019, BMC Bioinformatics.

[9]  Talip Zengin,et al.  Meta-Analysis of Genomic and Transcriptomic Variations in Lung Adenocarcinoma , 2019, 1911.00511.

[10]  Oliver Bonham-Carter,et al.  Systematic Normalization with Multiple Housekeeping Genes for the Discovery of Genetic Dependencies in Cancer , 2020, bioRxiv.

[11]  Lenore Cowen,et al.  Detangling PPI networks to uncover functionally meaningful clusters , 2017, BMC Systems Biology.

[12]  Carito Guziolowski,et al.  Constraints On Signaling Networks Logic Reveal Functional Subgraphs On Multiple Myeloma OMIC Data , 2017, BCB.

[13]  Xiaoning Qian,et al.  Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data , 2019, BMC Genomics.

[14]  Chanaka Bulathsinghalage,et al.  Network-based method for regions with statistically frequent interchromosomal interactions at single-cell resolution , 2020, BMC Bioinformatics.

[15]  Aniruddha Datta,et al.  Simulating variance heterogeneity in quantitative genome wide association studies , 2017, BMC Bioinformatics.

[16]  Banghua Zhu,et al.  Sparse Tensor Decomposition for Haplotype Assembly of Diploids and Polyploids , 2017, bioRxiv.

[17]  Tamer Kahveci,et al.  Selected research articles from the 2018 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC) , 2019, BMC Bioinformatics.

[18]  Baris E. Suzek,et al.  Meta-analysis of Gene Expression in Neurodegenerative Diseases Reveals Patterns in GABA Synthesis and Heat Stress Pathways , 2019, 1909.07469.

[19]  Peng Qiu,et al.  GLaMST: grow lineages along minimum spanning tree for b cell receptor sequencing data , 2018, BMC Genomics.

[20]  Edward R. Dougherty,et al.  Intrinsically Bayesian Robust Classifier for Single-Cell Gene Expression Time Series in Gene Regulatory Networks , 2017, BCB.

[21]  Haris Vikalo,et al.  ComHapDet: a spatial community detection algorithm for haplotype assembly , 2019, BMC Genomics.

[22]  Yuan Ji,et al.  Bayesian graphical models for computational network biology , 2017, BMC Bioinformatics.

[23]  Kwong-Sak Leung,et al.  SMILE: A Novel Procedure for Subcellular Module Identification with Localization Expansion , 2017, BCB.

[24]  Carito Guziolowski,et al.  Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data , 2018, BMC Systems Biology.

[25]  Lori A. Dalton,et al.  Heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure , 2018, BMC Bioinformatics.

[26]  Sang-Mun Chi,et al.  Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets , 2019, Nucleic acids research.

[27]  Peng Qiu,et al.  Leveraging TCGA gene expression data to build predictive models for cancer drug response , 2020, BMC Bioinformatics.

[28]  Marek Kimmel,et al.  Analysis of two mechanisms of telomere maintenance based on the theory of g-Networks and stochastic automata networks , 2020, BMC Genomics.

[29]  Tamer Kahveci,et al.  Selected research articles from the 2016 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC) , 2017, BMC Bioinformatics.