This book presents methods and tools of network biology and bioinformatics for understanding the disease dynamics and identification of drug targets. The initial section of chapters introduce the theoretical aspects followed by the different applications for construction and analysis of biological networks, methods for identifying crucial nodes in networks, and network dynamics. The book covers the latest advances in the network medicine, exploring the different types of biological networks, and their applications. It further reviews the role of R language in the network-based approaches that help in understanding biological systems and identifying biological functions. Towards the end, the book explores the recent developments and applications in machine learning and its potential for advancing network biology. Finally, the book elucidates a comprehensive yet a representative description of challenges associated with the understanding of disease dynamics using network biology. Given its scope, the book is intended for researchers and advanced postgraduate students of bioinformatics, computational biology, and medical sciences.
Auflage
Sprache
Verlagsort
Zielgruppe
Illustrationen
1
1 s/w Abbildung
X, 125 p. 1 illus.
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 14 mm
Gewicht
ISBN-13
978-981-99-4241-1 (9789819942411)
DOI
10.1007/978-981-99-4242-8
Schweitzer Klassifikation
Dr. Romana Ishrat is Associate Professor at the Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia University, where she has been a faculty member since 2007. Her research interests lie in the area of Bioinformatics and computational biology, ranging from graph theory to network analysis, network medicine and its implementation in investigating various biomedical problems at molecular biology level. She has served on various international and national conferences program committees and successfully organized a conference on Big Data and Computational Biology (2019). She has more than 15 years of teaching experience in computer science and bioinformatics. Her research group presently working in the areas of analyzing networks for understanding disease dynamics like p53, breast cancer, Tuberculosis MTB, Turner Syndrome, Spesis, CardioRenal Syndrome and Parathyroid. She has 50 research articles in the peer-reviewed international.
Chapter 1. Graph Theory in the Biological Networks.- Chapter 2. Biological Networks Analysis.- Chapter 3. Network Analysis based software packages, tools, and web servers to accelerate bioinformatics research.- Chapter 4. Networks Analytics of Heterogeneous Big Data.- Chapter 5. Network Medicine: Methods and Applications.- Chapter 6. Role of R in Biological Network Analysis.- Chapter 7. Machine Learning in Biological Networks.
