Marinka Zitnik

Fusing bits and DNA

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ACM XRDS: Understanding Cancer with Matrix Factorization

The Summer issue of ACM XRDS is here! In this issue we write about computational biology. Our features and interviews present different perspectives about some of the most recent advances of computational biology. You can read about personalized medicine and the use of genetic data to improve drug treatment, pharmacogenetics, machine learning techniques for mapping genetic differences to phenotypes in large-scale genome-wide association studies, computational approaches towards prediction of patient outcomes based on electronic health records, statistical techniques for drug discovery, etc. This issue also includes discussions on cutting-edge techniques, such as the analysis of single cell measurement data.

My department contributed a column on mining cancer data with matrix factorization, an established class of algorithms that proved useful in many bioinformatic studies. Diversity and abundance of data provided by the cancer projects like The International Cancer Genome Consortium challenge computer scientists of all kinds to develop innovative software, hardware, and analytic solutions for data analysis. We expect that with computationally and statistically stronger approaches, such as factorization models, we will be once able to reveal biological features that drive cancer development, define cancer types relevant for prognosis, and, ultimately, enable the development of new cancer therapies.