Pages: pp. 1265
Bioinformatics and Computational Biology comprises the application and development of data theoretical and analytical approaches, computational simulation methods, and mathematical modeling to the study of biological and biomedical systems. In this special section, five papers in their significantly extended versions were selected from the papers presented at the 10th Asia Pacific Bioinformatics Conference (APBC2012). These papers show recent research in bioinformatics and computational biology consisting of peptide identification, gene regulatory networks, protein interaction networks, and signal transduction cascades or pathways. These papers have shown great collaboration and conscientious thinking throughout the complex and challenging bioinformatics and computational biology experiments.
In “QuickVina: Accelerating AutoDock Vina Using Gradient-Based Heuristics for Global Optimization,” Stephanus Daniel Handoko, Xuchang Ouyang, Chinh Tran To Su, Chee Keong Kwoh, and Yew Soon Ong studied accelerating autoDock Vina using gradient-based heuristics for global optimization. They proposed to further improve the local search algorithm of Vina by the prevention of some points from undergoing the local search.
In “Improving X!Tandem on Peptide Identification from Mass Spectrometry by Self-Boosted Percolator,” Pengyi Yang, Jie Ma, Penghao Wang, Yunping Zhu, Bing B. Zhou, and Yee Hwa Yang studied improving X!Tandem on peptide identification from mass spectrometry by self-boosted percolator. They proposed a self-boosted percolator for postprocessing X!Tandem search results. They improved the performance through multiple boost runs, which enabled more PSM identifications without sacrificing false discovery rate (FDR).
In “CEDER: Accurate Detection of Differentially Expressed Genes by Combining Significance of Exons Using RNA-Seq,” Lin Wan and Fengzhu Sun learned of an accurate detection of differentially expressed genes by combining the significance of exons using RNA-Seq, widely used in transcriptiome studies and the detection of differentially expressed genes (DEGs) between two classes of individuals. They proposed developing a novel program, termed CEDER, to detect DEGs accurately.
In “How Little Do We Actually Know? On the Size of Gene Regulatory Networks,” Richard Röttger, Ulrich Rückert, Jan Taubert, and Jan Baumbach studied the size of gene regulatory networks. They proposed predicting the sizes of the whole-organizing regulatory networks of seven species. It was concluded that they had lacked substantial understanding of fundamental molecular control mechanism on a large scale.
In “A Comparative Assessment of Ranking Accuracies of Conventional and Machine-Learning-Based Scoring Functions for Protein-Ligand Binding Affinity Prediction,” Hossam M. Ashtawy and Nihar R. Mahapatra proposed exploring a range of novel SFs, employing different machine-learning approaches in conjunction with a variety of physicochemical and geometrical features characterizing protein-ligand complexes. SFs, which has a limited ranking accuracy, has been a major roadblock toward cost-effective drug discovery.
Yi-Ping Phoebe Chen