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Analyzing network data in biology and medicine: An interdisciplinary textbook for biological, medical and computational scientists

Contributor(s): Material type: TextTextPublication details: New York Cambridge University Press 2019Description: xiv, 632 p. ill. 27 cmISBN:
  • 9781108432238 (hardback : alk. paper)
Subject(s): DDC classification:
  • 570.285 PRZ.A
Contents:
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.
Summary: 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.
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Holdings
Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 570.285 PRZ.A (Browse shelf(Opens below)) Available DCB3773
Book Book IUCEIB Library, University of Kerala Processing Center IUCEIB Library, University of Kerala 610.285 PRZ.A (Browse shelf(Opens below)) Available CEB1002

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.

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.

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