Computational Prediction of Protein Complexes from Protein Interaction Networks

Sriganesh Srihari is a Senior Research Fellow with the Institute for Molecular Bioscience at The University of Queensland, Australia. He has a background in computer science (having received a Ph.D. in 2012 from National University of Singapore) and has worked extensively on graph (network) and combinatorial algorithms and in applying these to large omics datasets in biomedicine. He has devised systems-biology models to integrate ""multiomics"" datasets spanning genomics, RNAseq, and proteomics (protein-protein interaction) with clinical profiles to decipher molecular-clinical associations and identify new therapeutic targets in cancers. He has published in leading journals in the field including Bioinformatics, BMC Systems Biology, Biology Direct, Molecular Biosystems, and Nucleic Acids Research. He has closely collaborated with experimental biologists and has contributed to joint publications in Oncogene (Nature Publishing), Trends in Pharmacological Sciences (Cell Press), and Molecular Oncology. His postdoctoral work on cancer network models was highlighted in International Innovation (Healthcare issue, 2014), a Research Media periodical. His recent computational approach MutExSL (Biology Direct, 2015), co-authored with Limsoon Wong, for predicting synthetic-lethal targets by mining mutually exclusive genetic alterations in cancers was presented at the San Antonio Breast Cancer Symposium 2015 (San Antonio, Texas, USA), for which he won an American Association for Cancer Research (AACR)—Susan G.Komen for the Cure(R) Scholar-in-training Award. He serves on the Editorial Board for the cancer bioinformatics theme of Scientific Reports, and is a Guest Editor for Methods. Srihari has recently moved to the South Australian Health and Medical Research Institute, Australia, as a Senior Research Scientist. He is also an Adjunct Senior Lecturer with the School of Computer Science, Engineering, and Mathematics at Flinders University, Australia. Chern Han Yong is a Research Fellow in the Program in Cancer and Stem Cell Biology and the Centre for Computational Biology at the Duke-NUS Medical School, Singapore. He currently works on cancer genomics and epigenomics, and is particularly interested in the role of aberrant DNA methylation in carcinogenesis. He obtained his Ph.D. in computational biology from the National University of Singapore, where he researched the challenges of predicting protein complexes from high-throughput protein-protein interaction data. He obtained his M.Sc. in 2004 and B.Sc. in 2000 in computer science from the University of Texas at Austin, where he worked on neural networks, genetic algorithms, and the evolution of multi-agent cooperative behavior. Limsoon Wong is the Kwan-Im-Thong-Hood-Cho-Temple Chair Professor in the Department of Computer Science and a professor in the Department of Pathology at the National University of Singapore. Before that, he was the Deputy Executive Director for Research at A*STAR's Institute for Infocomm Research. He currently works mostly on knowledge discovery technologies and their application to biomedicine. He has also done, especially in the earlier part of his career, significant research in database query language theory and finite model theory, as well as significant development work in broad-scale data integration systems. He is a Fellow of the ACM, inducted for his contributions to database theory and computational biology. Some of his awards include the 2003 FEER Asian Innovation Gold Award, for his work on treatment optimization of childhood leukemias, and the ICDT 2014 Test of Time Award, for his work on naturally embedded query languages. He serves/served on the editorial boards of Journal of Bioinformatics and Computational Biology, Bioinformatics, Biology Direct, Drug Discovery Today, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Genomics Proteomics & Bioinformatics, Journal of Biomedical Semantics, Methods, Scientific Reports, Information Systems, and IEEE Transactions on Big Data. He is also an ACM Books Area Editor. He received his B.Sc. (Eng.) in 1988 from Imperial College London and his Ph.D. in 1994 from the University of Pennsylvania.

Preface
1. Introduction to Protein Complex Prediction
2. Constructing Reliable Protein-Protein Interaction (PPI) Networks
3. Computational Methods for Protein Complex Prediction from PPI Networks
4. Evaluating Protein Complex Prediction Methods
5. Open Challenges in Protein Complex Prediction
6. Identifying Dynamic Protein Complexes
7. Identifying Evolutionarily Conserved Protein Complexes
8. Protein Complex Prediction in the Era of Systems Biology
9. Conclusion
References
Authors' Biographies