Machine Learning Approach to Pattern Discovery in Oncogenomics


Discovering hidden patterns in sparse mutation data from lung cancer patients

In this paper, we present the development and validation of a novel algorithm for pattern discovery and its application in discovering biologically relevant genes and gene associations in unlabeled, and sparse genomic datasets. The strength of our method is not only in embracing the complexity of genetic architecture to identify distinct patterns embedded in a noisy background in a purely data-driven way but also, the ease with which it can be integrated into a comprehensive and flexible framework for data mining and actionable knowledge discovery that goes beyond human genetics and genomics.

Read our paper here. Watch our video here.