A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties

Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results: In this work, we introduce a computational systems bi...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang Huang, Xiaobo Zhou
• Keywords: BMCBI 2010, Genetic Interactions, Synthetic Genetic Arrays, Synthetic Genetic Interactions