title: A brief survey on computational approaches to reveal drug and disease associations.
abstract: People worldwide are still threatened by various complex disease phenotypes, especially cancer which is usually caused by the accumulation of multi-factor-driven alterations. Although drugs achieve the therapeutic functions by targeting particular molecular, the therapies used nowadays against diseases are not effective enough due to the limitation of the knowledge about the drug-disease associations. The rapid increasing of the available experimental data and knowledge enable scientists to reveal drug-disease associations by the systematic integration and analysis. In this review, we show that several computational methods can help us to explain the underlying relationships between pharmacology and pathology. It is expected that newer computational methods will take advantage of heterogeneous and multi-dimensional data and increase the efficacy and safety of existing drugs for disease treatment.
authors.affiliation: Department of Computer Science, National Tsing Hua University, HsinChu, Taiwan.
authors.forename: Hsiang-Yuan
authors.initials: HY
authors.lastname: Yeh
authors.forename: Wei-Chih
authors.initials: WC
authors.lastname: Lin
authors.forename: Yu-Fen
authors.initials: YF
authors.lastname: Huang
authors.forename: Von-Wun
authors.initials: VW
authors.lastname: Soo
mesh_terms: D015195:Drug Design; D020022:Genetic Predisposition to Disease; D006801:Humans; D058990:Molecular Targeted Therapy; D049490:Systems Biology