Advances in Bioinformatics

Computational and Statistical Approaches for Modeling of Proteomic and Genomic Networks


Publishing date
15 Mar 2013
Status
Published
Submission deadline
26 Oct 2012

Lead Editor

1Department of Chemical Engineering, Texas A&M University at Qatar, Doha, Qatar

2Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha, Qatar

3Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

4Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USA


Computational and Statistical Approaches for Modeling of Proteomic and Genomic Networks

Description

The current postgenomic period is characterized by a huge interest towards understanding how genes and proteins interact within cells via complex networks of structural, metabolic, and regulatory pathways. Recent high-throughput genomic and proteomic technologies opened up the possibility of learning the structure and functionality of genomic and proteomics networks on a large scale. However, developing reliable algorithms for inference of genomic and proteomic networks is hindered by a series of factors. The most stringent limitations are the undetermined nature of data sets, which manifests in the large number of unknown variables and reduced data samples, and the stochastic nature of measurements, which are often corrupted by noise and unknown latent variables. Another major limitation is the lack of a comprehensive computational framework to integrate efficiently the information provided by multiple heterogeneous data sets that refer to different characteristics and features of the protein-protein and gene-protein interactions. Finding computationally efficient data fusion and modeling algorithms for proteomic and genomic networks to overcome these limitations represents currently one of the most important research challenges in the field of computational biology.

In this special issue, we invite the submission of original research articles as well as review articles that present computational advances in modeling, validation, and perturbation of genomic and proteomic networks, as well as overview articles dealing with the interpretation, integration, and processing of the heterogeneous data sets that are available for modeling the interactions between genes and proteins. Computational and statistical inference techniques for modeling genomic and proteomic networks and that can fully exploit the potential offered by the existing biological data sets are also welcome. Potential topics include, but are not limited to:

  • Modeling of genomic and proteomic networks
  • Validation and quality assessment of inferred models
  • Computational and statistical aspects pertaining to model selection and data fusion
  • Robust statistical inference approaches
  • Data analysis and assessment of biological data sets

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/abi/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2013
  • - Article ID 561968
  • - Editorial

Computational and Statistical Approaches for Modeling of Proteomic and Genomic Networks

Mohamed Nounou | Hazem Nounou | ... | Yufei Huang
  • Special Issue
  • - Volume 2013
  • - Article ID 205763
  • - Research Article

Reverse Engineering Sparse Gene Regulatory Networks Using Cubature Kalman Filter and Compressed Sensing

Amina Noor | Erchin Serpedin | ... | Hazem Nounou
  • Special Issue
  • - Volume 2013
  • - Article ID 360678
  • - Research Article

Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis

Mario Flores | Tzu-Hung Hsiao | ... | Yidong Chen
  • Special Issue
  • - Volume 2013
  • - Article ID 171530
  • - Research Article

Spectral Analysis on Time-Course Expression Data: Detecting Periodic Genes Using a Real-Valued Iterative Adaptive Approach

Kwadwo S. Agyepong | Fang-Han Hsu | ... | Erchin Serpedin
  • Special Issue
  • - Volume 2013
  • - Article ID 618461
  • - Research Article

Identification of Robust Pathway Markers for Cancer through Rank-Based Pathway Activity Inference

Navadon Khunlertgit | Byung-Jun Yoon
  • Special Issue
  • - Volume 2013
  • - Article ID 953814
  • - Review Article

An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks

Amina Noor | Erchin Serpedin | ... | Lotfi Chouchane
  • Special Issue
  • - Volume 2013
  • - Article ID 527295
  • - Research Article

MRMPath and MRMutation, Facilitating Discovery of Mass Transitions for Proteotypic Peptides in Biological Pathways Using a Bioinformatics Approach

Chiquito Crasto | Chandrahas Narne | ... | Stephen Barnes
  • Special Issue
  • - Volume 2012
  • - Article ID 534810
  • - Research Article

Intervention in Biological Phenomena via Feedback Linearization

Mohamed Amine Fnaiech | Hazem Nounou | ... | Aniruddha Datta

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.