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Guest Editorial: Advanced Algorithms of Bioinformatics

Yi-Ping Phoebe Chen

Pages: pp. 273-273

Advanced algorithms can help to identify functional relationships of genes in a biological process, discovery sequence, and structural similarities and provide insights into the regulatory mechanism in bioinformatics. In this special section, three papers in their significantly extended versions were selected from the papers presented at the Tenth Asia Pacific Bioinformatics Conference (APBC2012). These papers have shown great cooperation and conscientious consideration throughout the challenging bioinformatics experiments.

In “Chain-RNA: A Comparative ncRNA Search Tool Based on the Two-Dimensional Chain Algorithm,” Jikai Lei, Prapaporn Techa-angkoon, and Yanni Sun introduced a new de novo ncRNA search method and its implementation called chain-RNA. They formulated stem alignment as an extended 2D chain problem and utilized existing chain algorithms. The experimental results show that chain-RNA has better tradeoff between sensitivity and false positive rate in ncRNA prediction than conventional sequence similarity search tools and is more time competent than structural alignment tools.

In “Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data,” Pradipta Maji and Sushmita Paul proposed an efficient method to select initial prototypes of different gene clusters, which enables the proposed c-means algorithm to converge to an optimum or near optimum solution and helps to discover coexpressed gene clusters. They developed a new gene clustering algorithm, which integrates judiciously c-means algorithm, rough sets, and probabilistic and possibilistic memberships of fuzzy sets. In this paper, the effectiveness of the proposed algorithm is established.

In “Computational Reconstruction of Transcriptional Relationships from ChIP-Chip Data,” Ngoc Tu Le, Tu Bao Ho, and Bich Hai Ho developed a method to reconstruct a Bayesian network (BN) model representing functional relationships among various transcriptional components. The resulting network model showed that transcriptional components positively influence each other. Along with them, GTFs, Mediator, and CRCs play critical roles in regulating the outcome of the whole process. Their process can also be extended to recreate more accurate model as data on other aspects of transcription become available. A few new functional relationships were also recommended, which may carry insights into regulatory mechanism and transcriptional regulation.

Yi-Ping Phoebe Chen

Guest Editor

About the Authors

Bio Graphic
Yi-Ping Phoebe Chen received the BInfTech degree with first class honours and the PhD degree in computer science (bioinformatics) from the University of Queensland. She is a professor and chair and director of research in the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. Professor Chen is the chief investigator in the ARC Centre of Excellence in Bioinformatics. She is currently working on knowledge discovery technologies and is especially interested in their application to genomics and biomedical science. Her research focus is to find the best solutions for mining, integrating, and analyzing complex data structure and functions for scientific and biomedical applications. She has been working in the area of bioinformatics, health informatics, multimedia databases, query system, and systems biology, has coauthored more than 185 research papers, with many published in top journals and conferences. She is the steering committee chair of the Asia-Pacific Bioinformatics Conference (founder) and the International Conference on Multimedia Modelling. She has been on the program committees of more than 100 international conferences, including top ranking conferences such as ICDE, ICPR, ISMB, CIKM, etc. More information about her can be found at
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