BioMathProject2016 The Facebook of genes: expanding known pathways using network information
Background': Curated pathway databases such as KEGG or Gene Ontology are essential resources for biomedical research. However, they are still largely incomplete and biased towards well-studied genes: while some genes have been studied ad nauseam over decades by entire armies of biologists, other genes remain completely unstudied. Computational methods to expand our knowledge from well-annotated genes to unknown genes are thus of great interest.
Goal: The aim of this project is to explore methods that leverage biological networks to predict additional genes that belong to a given known pathway, i.e., expand the pathway by identifying new members on the basis of their network connections. We will use a guilt-by-association approach, which is the same principle as facebook uses to infer information about you from the information it has on your friends, for instance.
Computational tools: This project has a computational flavor and is best suited for students with interest in programming. We will apply standard tools used in many diverse problems in computational biology (e.g., clustering, gene ontology enrichment analysis, permutation tests, etc). This project will be implemented using R (unless the group strongly favors another programming environment).
Keywords: Network biology, pathway analysis
Supervisor: Daniel Marbach
