Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection
Editat de Byung-Jun Yoon, Xiaoning Qianen Limba Engleză Hardback – 14 ian 2021
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 datawith topological structure or relations can also benefit from the book's insights.
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Specificații
ISBN-13: 9783030571726
ISBN-10: 3030571726
Ilustrații: XII, 217 p. 42 illus., 29 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.5 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030571726
Ilustrații: XII, 217 p. 42 illus., 29 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.5 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Chapter 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.
Notă biografică
Prof. Byung-Jun Yoon received the B.S.E. (summa cum laude) degree from the Seoul National University (SNU), Seoul, Korea, and the M.S. and Ph.D. degrees from the California Institute of Technology (Caltech), Pasadena, CA, all in Electrical Engineering. He is currently an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, College Station, TX, USA. Prof. Yoon also holds a joint appointment at the Brookhaven National Laboratory (BNL), Upton, NY, where he is a Scientist in Computational Science Initiative (CSI). His awards and honors include the National Science Foundation (NSF) CAREER Award, the Best Paper Award at the 9th Asia Pacific Bioinformatics Conference (APBC), the Best Paper Award at the 12th Annual MCBIOS Conference, and the SLATE Teaching Excellence Award from the Texas A&M University System. Prof. Yoon’s main research interests include bioinformatics, computational network biology, machine learning, and signal processing.
Prof. Xiaoning Qian received the Ph.D. degree in Electrical Engineering from Yale University, New Haven, CT, USA. He is currently an Associate Professor with the Department of Electrical and Computer Engineering. He is also a member of the TEES (Texas A&M Engineering Experiment Station)-AgriLife Center for Bioinformatics and Genomic Systems Engineering and the Center for Translational Environmental Health Research at Texas A&M University. His awards include the National Science Foundation CAREER Award, the Texas A&M Engineering Experiment Station (TEES) Faculty Fellow, and the Montague-Center for Teaching Excellence Scholar at Texas A&M University. His research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, especially in learning, uncertainty quantification, and experimental design. He has actively applied probabilistic models and optimization algorithms for systematic analysis of biomedical data and systems, including biomedical signals, images, gene expression, and molecular networks. He has been working on several funded interdisciplinary projects applying developed computational algorithms in biomedicine.
Prof. Xiaoning Qian received the Ph.D. degree in Electrical Engineering from Yale University, New Haven, CT, USA. He is currently an Associate Professor with the Department of Electrical and Computer Engineering. He is also a member of the TEES (Texas A&M Engineering Experiment Station)-AgriLife Center for Bioinformatics and Genomic Systems Engineering and the Center for Translational Environmental Health Research at Texas A&M University. His awards include the National Science Foundation CAREER Award, the Texas A&M Engineering Experiment Station (TEES) Faculty Fellow, and the Montague-Center for Teaching Excellence Scholar at Texas A&M University. His research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, especially in learning, uncertainty quantification, and experimental design. He has actively applied probabilistic models and optimization algorithms for systematic analysis of biomedical data and systems, including biomedical signals, images, gene expression, and molecular networks. He has been working on several funded interdisciplinary projects applying developed computational algorithms in biomedicine.
Textul de pe ultima copertă
This 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 datawith topological structure or relations can also benefit from the book's insights.
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 datawith topological structure or relations can also benefit from the book's insights.
Caracteristici
Presents an accessible overview of the emerging field of computational network biology for readers who are new to the field Introduces the latest techniques in computational network biology, especially, comparative network analysis and network module detection Points readers to the latest available resources in the field, including network and pathway databases, benchmarks, existing algorithms, and source code