Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/43993
Title: Integration of heterogeneous data sources for identification of disease genes using computational techniques
Authors: Li, Yongjin
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
DRNTU::Science::Medicine::Computer applications
Issue Date: 2011
Source: Li, Y. J. (2011). Integration of heterogeneous data sources for identification of disease genes using computational techniques. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Genes related to causing some disease are called disease-causing genes or disease genes. In wet-lab experiments, disease genes are identified by mutation analysis, which is expensive and labor extensive. In this thesis, we propose novel computational techniques to predict disease genes. In the first part of this thesis, we proposed five novel topological features obtained from the Protein-Protein Interaction (PPI) network. We applied Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) networks to predict new cancer genes using these features. We found that SVM performed slightly better than MLP. We also found that the feature, named 2N-index, is the most discriminative feature between cancer genes and other genes. With the availability of various data sources related to genes and disease phenotype, accurate prediction of disease genes is possible by integrating the information available from multiple data sources. We propose several novel computational models to integrate multiple data sources for the identification of disease genes. These models are proposed to prioritize set of candidate disease genes, based on their functional similarity to known disease genes.
URI: https://hdl.handle.net/10356/43993
DOI: 10.32657/10356/43993
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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