Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/118600
Title: Bioinformatics Analysis and Modelling of Therapeutically Relevant Molecules
Authors: TAO LIN
Keywords: Bioinformatics, Machine learning, Protein-protein interaction, Protein function, Natural product, Clustering
Issue Date: 14-Aug-2014
Source: TAO LIN (2014-08-14). Bioinformatics Analysis and Modelling of Therapeutically Relevant Molecules. ScholarBank@NUS Repository.
Abstract: Target and lead discoveries are two critical steps in drug discovery, and the quality of their relevant works may dramatically affect the final outcome. Computational methods can be applied in these two steps for facilitating and economizing the process of drug discovery. This thesis describes my studies on the computational analysis and prediction of therapeutically relevant molecules in these two steps with three directions: (1) analysis of natural products for drug lead discovery, (2) prediction of protein functional families and their interactions related to target discovery, (3) disease, drug and target data collection and the development of molecular profile prediction software as to update Therapeutic Targets Database and construct Molecular Feature Server. The results show that these studies are potentially useful for drug discovery.
URI: http://scholarbank.nus.edu.sg/handle/10635/118600
Appears in Collections:Ph.D Theses (Open)

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