TY  - BOOK
AU  - Przulj,Natasa
TI  - Analyzing network data in biology and medicine: An interdisciplinary textbook for biological, medical and computational scientists 
SN  - 9781108432238 (hardback : alk. paper)
U1  - 570.285 
PY  - 2019///
CY  - New York
PB  - Cambridge University Press
KW  - Medical informatics
KW  - Bioinformatics
N1  -  	1. From genetic data to medicine: from DNA samples to disease risk prediction in personalized genetic tests Luis Leal, Rok Kosir and Natasa Przulj; 2. Epigenetic data and disease Rodrigo Gonzalez-Barrios, Marisol Salgado-Albarran, Nicolas Alcaraz, Cristian Arriaga-Canon, Lissania Guerra-Calderas, Laura Contreras-Espinoza and Ernesto Soto-Reyes; 3. Introduction to graph and network theory Thomas Gaudelet and Natasa Przulj; 4. Protein-protein interaction data, their quality, and major public databases Anne-Christin Hauschild, Chiara Pastrello, Max Kotlyar and Igor Jurisica; 5. Graphlets in network science and computational biology Khalique Newaz and Tijana Milenkovic; 6. Cluster analysis Richard Roettger; 7. Machine learning for data integration in cancer precision medicine: matrix factorization approaches Noel Malod-Dognin, Sam Windels and Natasa Przulj; 8. Machine learning for biomarker discovery: significant pattern mining F. Llinares-Lopez and K. Borgwardt; 9. Network alignment Noel Malod-Dogning and Natasa Przulj; 10. Network medicine Pisanu Buphamalai, Michael Caldera, Felix Muller and Joerg Menche; 11. Elucidating genotype-to-phenotype relationships via analyzes of human tissue interactomes Idan Hekselman, Moran Sharon, Omer Basha and Esti Yeger-Lotem; 12. Network neuroscience Alberto Cacciola, Alessandro Muscoloni and Carlo Vittorio Cannistraci; 13. Cytoscape: tool for analyzing and visualizing network data John H. Morris; 14. Analysis of the signatures of cancer stem cells in malignant tumours using protein interactomes and STRING database Kresimir Pavelic, Marko Klobucar, Dolores Kuzelj, Natasa Przulj and Sandra Kraljevic Pavelic
N2  - Bringing together leading experts in the field of network data analysis, this text introduces graph and network theory, cluster analysis and machine learning. Using real-world biological and medical examples, applications of these theories are discussed and creative thinking is encouraged in the analysis of such complex network data sets
ER  -