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Introduction: Advances in Computational Systems Bioinformatics

This special issue of the Journal of Bioinformatics and Computational Biology is devoted to the Computational Systems Bioinformatics Conference (CSB) held in August 2010 at Stanford University. Out of 19 peer-reviewed manuscripts that have been presented at the conference and subsequently been published in the Proceedings of the Nineth Computational Systems Bioinformatics Conference (CSB’10), we selected eight papers to be published here. The selected papers have reached the highest scores in the initial peer-reviewing process. As we offered the authors to expand the conference manuscript by up to 30%, a second review process has been conducted to strengthen their scientific quality. The final result is this exciting collection of eight high-quality papers: Parker et al. present in “Optimization of therapeutic proteins to delete T-Cell epitopes while maintaining beneficial residue interactions” an integer programming approach that attacks the NP-hard problem of selecting sets of mutations predicted to delete immunogenic T-cell epitopes, while simultaneously maintaining important residues and residue interactions. In “Temporal graphical models for cross-species gene regulatory network discovery”, Liu et al. concentrated on cross-species gene expression analysis. They developed a hidden Markov random field regression to jointly uncover the regulatory networks for multiple species, thus capturing the causal relations between genes from time-series microarray data across species. In their paper “Classification of large microarray datasets using fast random forest construction”, Manilich et al. customized the widely used random forest classifier to address specific properties of microarray data. By reducing overlapping computations and eliminating dependency on the size of the main memory, their implementation shows an increased performance for this application. Ozer et al. studied ways to compare multiple ChIP-seq experiments in their manuscript “Comparing multiple ChIP-sequencing experiments” in order to attack the challenge of comparing multiple cell lines under different experimental conditions despite the massive amount of data produced by high-throughput sequencing