Editorial [Hot Topic: Special Issue on Advanced Intelligent Computing Theory and Methodology in Protein Science (Guest Editor: De-Shuang Huang) ]

We are very pleased to offer this special issue to the readers of Protein and Peptide Letters by selecting the candidate papers from the 2007 International Conference on Intelligent Computing (ICIC), held in Qingdao, Shandong Province, China, 21-24 August 2007. Twelve papers (representing less than one half of one percent of all eligible papers accepted at the 2007 ICIC) were selected for inclusion in this special issue. In recent years, we have witnessed intelligent computing techniques, such as artificial intelligence, machine learning, colony intelligence, and others being dedicated to various research aspects of bioinformatics, neuroinformatics, chemoinformatics, computational biology, system biology, etc. Meanwhile, intelligent computing knowledge has been enriched by the development of more solid mathematical frameworks, elaborating more efficient and powerful algorithms as well as structures. More importantly, it has been driven by its application to many amazing research fields, such as bioinformatics. Currently, intelligent computing techniques for protein bioinformatics are being used to encode biological information, extract biological features, recognize biological patterns, mine and comprehend biological data, build models of biological systems and processes, and thus automatically form theories from the unprecedentedly vast experimental biological data. Its main objective is to find the rule and useful biological information from limited observation examples that cannot be obtained using classical biological methods and theories. It extends the rule to predict and infer protein’s structure, function, structurefunction relationship, interaction networks, and other significant aspects of protein and peptide science. Hence, the intelligent computing technique, an in silico method, is supplementary to conventional experimental methods and makes it possible to use computers to extract knowledge from large amounts of biological and biomedical information. This special issue includes several papers on how to use intelligent computing techniques to solve problems in protein bioinformatics. Three papers in this issue focus on exploring computational algorithms and applications in protein bioinformatics. Busa- Fekete et al. discuss using propagation on unrooted binary trees to perform protein classification. Zhang et al. address an antcolony algorithm for solving protein-folding problems by placing pheromones on the arcs connecting adjacent squares in the lattice. Peng et al. present an artificial immune network-based algorithm for diabetes diagnosis. In the next two papers, Srisawat et al. use combined classifiers for HIV drug-resistance prediction, and Liu, Xia et al. adopt ensemble schemes for protein secondary structure prediction. The next four papers present network or sequence multiple alignment-based protein structure and function predictions. Liu et al. discuss using a pocket similarity network to analyze protein surface patterns. Zhao et al. propose a framework of protein domain function annotation with predicted domain-domain interaction networks. Zhang et al. predict the binding motifs in hepatitis protein C virus NS5A and human proteins using sequence multiple alignment. Gao et al. present their computational phylogenetic analysis for the functional annotation of TBC (Tre-2/Bub2/Cdc16) domain-containing proteins. The last three papers focus on applying special coding methods, support vector machine (SVM) and its modified techniques to protein folding recognition, ligand binding, and long-range interaction site predictions, respectively. More specifically, Chen et al. combine error-correcting output codes and SVM methods for protein fold recognition. Cai et al. apply SVM to the function prediction of DNA-/RNA- binding proteins, GPCR, and drug ADME-associated proteins. Chen et al. present a bounded SVM method for locating key long-range interaction sites by predicted, local B-factors. It should be stressed that recommendations for this special issue were made by the ICIC's International Program Committee, and the final selections were made on the basis of quality, novelty, and theoretical or practical importance. All papers were subjected to two rounds of review with a minimum of three reviewers, reflecting the demand for improving the quality of ICIC papers. We hope that you find this special issue both useful and enjoyable. As guest editor, I would like to take this opportunity to thank all the authors for their contributions to this special issue, the reviewers for their valuable input, insight, and expert comments, and the Editor-in-Chief, Distinguished Professor Ben M. Dunn, for his valuable advice and strong support during the preparation of this special issue.