LEADER 03035oam 2200457 450 001 9910483890403321 005 20210618072357.0 010 $a3-030-57173-4 024 7 $a10.1007/978-3-030-57173-3 035 $a(CKB)4100000011716973 035 $a(DE-He213)978-3-030-57173-3 035 $a(MiAaPQ)EBC6456140 035 $a(PPN)25325552X 035 $a(EXLCZ)994100000011716973 100 $a20210618d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRecent advances in biological network analysis $ecomparative network analysis and network module detection /$fByung-Jun Yoon, Xiaoning Qian, editors 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (XII, 217 p. 42 illus., 29 illus. in color.) 311 $a3-030-57172-6 320 $aIncludes bibliographical references. 327 $aChapter 1: Global Alignment of PPI Networks -- Chapter 2: Integrated Network-Based Computational Analysis for Drug Development -- Chapter 3: Effective Random Walk Models for Comparative Network Analysis -- Chapter 4: Computational Methods for Protein-Protein Interaction Network Alignment -- Chapter 5: Network Propagation for the Analysis of Multi_Omics Data -- Chapter 6: Motifs in Biological Networks -- Chapter 7: Bio Fabric Visualization of Network Alignments -- Chapter 8: Module Identification of Biological Networks via Graph Partition -- Chapter 9: Network Module Detection to Decipher the Heterogeneity of Cancer Mutations. 330 $aThis book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network analysis and network module detection. The chapters in this volume are contributed by leading international researchers in computational network biology and offer in-depth insight on the latest techniques in network alignment, network clustering, and network module detection. Chapters discuss the advantages of the respective techniques and present the current challenges and open problems in the field. Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection will serve as a great resource for graduate students, academics, and researchers who are currently working in areas relevant to computational network biology or wish to learn more about the field. Data scientists whose work involves the analysis of graphs, networks, and other types of data with topological structure or relations can also benefit from the book's insights. . 606 $aBioinformatics 615 0$aBioinformatics. 676 $a570.285 702 $aYoon$b Byung-Jun 702 $aQian$b Xiaoning 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910483890403321 996 $aRecent advances in biological network analysis$92804055 997 $aUNINA