MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02551cam a22001698i 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781108432238 (hardback : alk. paper) |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
570.285 |
| Item number |
PRZ.A |
| 245 00 - TITLE STATEMENT |
|---|
| Title |
Analyzing network data in biology and medicine: An interdisciplinary textbook for biological, medical and computational scientists |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
New York |
| Name of publisher, distributor, etc. |
Cambridge University Press |
| Date of publication, distribution, etc. |
2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 632 p. ill. 27 cm |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Medical informatics |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Bioinformatics. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Przulj, Natasa, |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Book |
