Biological Data Mining in Protein Interaction Networks

Li, Xiao-Li, Ng, See-Kiong
IGI Global, 31 may 2009 - 450 páginas

Methods for detecting protein-protein interactions (PPIs) have given researchers a global picture of protein interactions on a genomic scale.

Biological Data Mining in Protein Interaction Networks explains bioinformatic methods for predicting PPIs, as well as data mining methods to mine or analyze various protein interaction networks. A defining body of research within the field, this book discovers underlying interaction mechanisms by studying intra-molecular features that form the common denominator of various PPIs.


Páginas seleccionadas


Molecular Biology of ProteinProtein Interactions for Computer Scientists
Data Mining for Biologists
PPI Network Construction and Cleansing
DomainBased Prediction and Analysis of ProteinProtein Interactions
Incorporating Graph Features for Predicting ProteinProtein Interactions
Discovering ProteinProtein Interaction Sites from Sequence and Structure
Reliable Interaction Networks
Discovering Interaction Motifs from Protein Interaction Networks
Predicting Protein Functions from Protein Interaction Networks
Protein Interactions for Functional Genomics
Prioritizing Disease Genes and Understanding Disease Pathways
Dynamics of ProteinProtein Interaction Network in Plasmodium Falciparum
Graphical Analysis and Visualization Tools for Protein Interaction Networks
Network Querying Techniques for PPI Network Comparison
Module Finding Approaches for Protein Interaction Networks
Compilation of References

Discovering Network Motifs in Protein Interaction Networks
Discovering Protein Complexes in Protein Interaction Networks
Evolutionary Analyses of Protein Interaction Networks
Discovering Lethal Proteins in Protein Interaction Networks
About the Contributors
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Sobre el autor (2009)

Xiao-Li Li is currently a principal investigator in the Data Mining Department at the Institute for Infocomm Research, A*Star. He also holds an appointment of adjunct assistant professor in SCE, NTU. Xiao-Li received his PhD degree in computer science from Chinese Academy of Sciences (2001) and was then with National University of Singapore (School of Computing/Singapore-MIT Alliance) as a research fellow from 2001 to 2004. His research interests include bioinformatics, data mining, and machine learning. He has been serving as a member of technical program committees in numerous bioinformatics (a book editor for Biological Data Mining in Protein Interaction Networks, PC members for IEEE BIBE, IEEE BIBM, etc.), data mining (including a PC member in leading data mining conference KDD, CIKM, and SDM), and machine learning related conferences (a session chair of PKDD/ECML). He has also served as an editorial board member for International Journal of Data Analysis Techniques and Strategies (IJDATS), Journal of Information Technology Research (JITR) and other IGI Global editorial advisory review boards. In 2005, he received best paper award in the 16th International Conference on genome informatics (GIW 2005). In 2008, he received the best poster award in the 12th Annual International Conference Research in computational molecular biology (RECOMB 2008). To learn more about Dr. Xiao-Li Li, please visit his Web page:

See-Kiong Ng is currently the Department Head of the Data Mining Department at the Institute for Infocomm Research. He is also an adjunct associate professor at the School of Computer Engineering, Nanyang Technological University. Dr. Ng obtained his PhD in computer science from Carnegie Mellon University. He wrote the TrueAllele software when he was a graduate student at CMU. The program was eventually used by a biotech company in Iceland to genotype the entire Icelandic population, thereby beginning his brave journey into the exciting field of genomics as a computer scientist. Dr. Ng's current research focuses on unraveling the underlying functional mechanisms of protein interaction networks as well as other real-world networks. His continuing and emerging diverse and cross-disciplinary research interests include bioinformatics, text mining, social network mining, and privacy-preserving data mining. [Editor]

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