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: