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Computational Prediction of Protein Complexes from Protein Interaction Networks

TaschenbuchKartoniert, Paperback
296 Seiten
Englisch
Systematically walks through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). The authors present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios.mehr
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Produkt

KlappentextSystematically walks through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). The authors present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios.
Details
ISBN/GTIN978-1-970001-52-5
ProduktartTaschenbuch
EinbandartKartoniert, Paperback
Erscheinungsjahr2017
Erscheinungsdatum30.05.2017
Seiten296 Seiten
SpracheEnglisch
MasseBreite 191 mm, Höhe 235 mm, Dicke 16 mm
Gewicht557 g
Artikel-Nr.43386365

Inhalt/Kritik

Inhaltsverzeichnis
Preface1. Introduction to Protein Complex Prediction2. Constructing Reliable Protein-Protein Interaction (PPI) Networks3. Computational Methods for Protein Complex Prediction from PPI Networks4. Evaluating Protein Complex Prediction Methods5. Open Challenges in Protein Complex Prediction6. Identifying Dynamic Protein Complexes7. Identifying Evolutionarily Conserved Protein Complexes8. Protein Complex Prediction in the Era of Systems Biology9. ConclusionReferencesAuthors' Biographiesmehr

Autor

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.
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