| Chapter title |
Functional Interaction Network Construction and Analysis for Disease Discovery
|
|---|---|
| Chapter number | 11 |
| Book title |
Protein Bioinformatics
|
| Published in |
Methods in molecular biology, February 2017
|
| DOI | 10.1007/978-1-4939-6783-4_11 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6781-0, 978-1-4939-6783-4
|
| Authors |
Guanming Wu, Robin Haw |
| Editors |
Cathy H. Wu, Cecilia N. Arighi, Karen E. Ross |
| Abstract |
Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 60% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 100 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 20% |
| Researcher | 18 | 18% |
| Student > Master | 12 | 12% |
| Student > Bachelor | 9 | 9% |
| Student > Doctoral Student | 4 | 4% |
| Other | 11 | 11% |
| Unknown | 26 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 30 | 30% |
| Agricultural and Biological Sciences | 12 | 12% |
| Medicine and Dentistry | 7 | 7% |
| Computer Science | 5 | 5% |
| Immunology and Microbiology | 2 | 2% |
| Other | 15 | 15% |
| Unknown | 29 | 29% |