With advances in biotechnologies, large-scale biological data has been and will continue to be produced. These large-scale biological data contain insightful information for understanding the mechanism of biological systems and have proven useful in the diagnosis, treatment, and drug design for genomic-alerted diseases. In this special section, five papers in their significantly extended versions were selected from the papers presented at the IEEE Conference onBioinformatics and Biomedicine (BIBM), 2009. These papers present recent research in bioinformatics and systems biology to make sense from large-scale biological data.
In past decades, much attention has been paid to biomedical research. As a result, the biomedical literature is growing exponentially. To comprehensively utilize these biological text data, text data mining becomes a very important tool. Yanpeng Li, Xiaohua Hu, Hongfei Lin, and Zhihao Yang in “A Framework for Semisupervised Feature Generation and Its Applications in Biomedical Literature Mining” present a framework for creating new features for text data and show its applications to text mining tasks in biomedical domain.
With the complete sequences of many genomes, the tremendous amount of protein sequences is available now. It is challenging to predict protein functions from their sequences. Jong Cheol Jeong, Xiaotong Lin, and Xue-Wen Chen in “On Position-Specific Scoring Matrix for Protein Function Prediction” design some new features extracted from protein sequences only and propose machine learning- based methods for protein function prediction.
The classification can be used for classifying various biological data and for predicting the biological function from these data. However, imbalanced data can seriously affect the classification results and thus prediction performance. Sangyoon Oh, Min Su Lee, and Byoung-Tak Zhang in “Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification” present a method for the classification of imbalanced biological data.
In most (if not all) cases, it is prohibitive to directly do biological experiments on real-life biological systems. Therefore, modeling and simulation techniques become very important for understanding the dynamical behavior of biological systems. Zina M. Ibrahim, Alioune Ngom, and Ahmed Y. Tawfik in “Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks” present a model for discovering monotonic relations among genes for constructing gene regulatory networks from gene expression data. Amit Sabnis and Robert W. Harrison in “A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems” propose a novel method for simulating biochemical networks based on a deterministic solution with a modification that permits the incorporation of stochastic effects.
The guest editors would like to thank all of the authors for their high-quality work contributed to this special section and all of the anonymous reviewers for their great efforts and expert comments in evaluating the manuscripts.
F.-X. Wu is with the Division of Biomedical Engineering, Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada. E-mail: email@example.com.
J. Huan is with the Department of Electrical Engineering and Computer Science, Information and Telecommunication Technology Center, University of Kansas, Lawrence, KS 66045. E-mail: firstname.lastname@example.org.
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(M'06) received the BSc and MSc degrees in applied mathematics, both from Dalian University of Technology, China, in 1990 and 1993, respectively. He received his first PhD degree in control theory and its applications from Northwestern Polytechnical University, Xi'an, China, in 1998, and his second PhD degree in biomedical engineering from the University of Saskatchewan, Saskatoon, Canada, in 2004. During 2004-2005, he worked as a postdoctoral fellow in the Laval University Medical Research Center (CHUL), Quebec City, Canada. Dr. Wu joined the University of Saskatchewan (U of S) in 2005 and is currently an associate professor of bioengineering in the Department of Mechanical Engineering and the Division of Biomedical Engineering. His current research interests include systems biology, genomic and proteomic data analysis, biological system identification and parameter estimation, and applications of control theory to biological systems. He has published more than 100 technical papers in refereed journals and conference proceedings. He is serving as an editorial board member of two international journals and as a program committee member of several international conferences. He has also reviewed papers for many international journals. He is a member of the IEEE.
Jun (Luke) Huan
received the PhD degree in computer science from the University of North Carolina at Chapel Hill in 2006. He has been an assistant professor in the Electrical Engineering and Computer Science Department at the University of Kansas (KU) since 2006. He is an affiliated member of the Information and Telecommunication Technology Center (ITTC), Bioinformatics Center, Bioengineering Program, and the Center for Biostatistics and Advanced Informatics—all KU research organizations. Before joining KU, he worked at the Argonne National Laboratory (with Ross Overbeek) and at GlaxoSmithKline plc. (with Nicolas Guex). Dr. Huan was a recipient of the US National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award in 2009. He serves on the program committees of leading international conferences, including ACM SIGKDD, IEEE ICDE, ACM CIKM, and IEEE ICDM.