Implemented by
Sungchul Kim (subright@postech.ac.kr)
Hwanjo Yu (hwanjoyu@postech.ac.kr)
Abstract:
Given a large quantity of genome mutation data collected from clinics, how can we search for similar patients? Similarity search based on patient mutation profiles can solve various translational bioinformatics tasks, including prognostics and treatment efficacy predictions for better clinical decision making through sheer volume of data. However, this is a challenging problem due to heterogeneous and sparse characteristics of the mutation data as well as its high dimensionality. To solve this problem we introduce a compact representation and search strategy based on Gene-Ontology and orthogonal non-negative matrix factorization. Results show that our method is able to identify and characterize clinically meaningful tumor subtypes better than the recently introduced Network Based Stratification method while enabling real-time search. To the best of our knowledge, this is the first attempt to simultaneously characterize and represent somatic mutational data for efficient search purposes.
Using This Code:
This code is publicly available to facilitate research and education in the related areas of bioinformatics (especially, analysis genome mutation data).
Reference:
Hofree, M., Shen, J. P., Carter, H., Gross, A., and Ideker, T. (2013). Network-based stratification of tumor mutations. Nat Methods, 10(11), 1108-15