Complexity

Biomolecular Networks for Complex Diseases


Publishing date
18 Aug 2017
Status
Published
Submission deadline
31 Mar 2017

1University of Saskatchewan, Saskatoon, Canada

2Central South University, Changsha, China

3Ulster University, Belfast, UK


Biomolecular Networks for Complex Diseases

Description

It is widely acknowledged that complex diseases or disorders (e.g., cancer, AIDS, and obesity) stem from the biomolecular dysfunction of not only their isolated components (e.g., genes, proteins, and metabolites). Biomolecular networks typically include gene regulatory networks, protein-protein interaction networks, metabolic networks, and signal transduction networks. With advances in high throughput measurement techniques such as microarray, RNA-seq, ChIP-chip, yeast two hybrid analysis, and mass spectrometry, large-scale biological data have been and will continuously be produced. Such data contain insightful information for understanding the mechanism of molecular biological systems and have proved useful in diagnosis, treatment, and drug design for complex diseases or disorders.

For this special issue, we strongly encourage authors to submit their original studies in modeling/construction, analysis, synthesis, and control of disease-related biomolecular networks.

Potential topics include but are not limited to the following:

  • Biomolecular network modeling and analysis
  • Prediction of disease molecules from biomolecular networks
  • Controllability and observability of biomolecular networks
  • Drug target identification from biomolecular networks
  • Biomolecular network based drug repositioning
  • Dynamic analysis of biomolecular networks
  • Parameter estimation of nonlinear dynamic molecular systems
  • Specific disease-related molecular networks
  • Disease-related protein complex or functional module detection
  • Application tools for biomolecular network visualization and analysis

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 4210160
  • - Editorial

Biomolecular Networks for Complex Diseases

Fang-Xiang Wu | Jianxin Wang | ... | Haiying Wang
  • Special Issue
  • - Volume 2017
  • - Article ID 2713280
  • - Research Article

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Cheng Yan | Jianxin Wang | ... | Yi Pan
  • Special Issue
  • - Volume 2017
  • - Article ID 8362741
  • - Review Article

Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

Jin Liu | Min Li | ... | Jianxin Wang
  • Special Issue
  • - Volume 2017
  • - Article ID 8917258
  • - Research Article

Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer

Le Zhang | Chunqiu Zheng | ... | Ziyuan Zhou
  • Special Issue
  • - Volume 2017
  • - Article ID 2498957
  • - Research Article

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Zhen Shen | You-Hua Zhang | ... | De-Shuang Huang
  • Special Issue
  • - Volume 2017
  • - Article ID 3017632
  • - Research Article

Exploring the Limitations of Peripheral Blood Transcriptional Biomarkers in Predicting Influenza Vaccine Responsiveness

Luca Marchetti | Emilio Siena | ... | Duccio Medini
  • Special Issue
  • - Volume 2017
  • - Article ID 5024867
  • - Research Article

FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis

Lin Yuan | Chang-An Yuan | De-Shuang Huang
  • Special Issue
  • - Volume 2017
  • - Article ID 4120506
  • - Research Article

Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

Jie Zhao | Xiujuan Lei | Fang-Xiang Wu
  • Special Issue
  • - Volume 2017
  • - Article ID 4826206
  • - Research Article

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Pi-Jing Wei | Di Zhang | ... | Chun-Hou Zheng
  • Special Issue
  • - Volume 2017
  • - Article ID 9194801
  • - Research Article

Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

Jincai Yang | Huichao Gu | ... | Xianjun Shen
Complexity
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 Journal metrics
Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
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