Unsupervised machine learning methods that discover the molecular programs underlying cellular biology

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A decade ago, the human genome was sequenced. That is, we learned the string of three billion letters A/C/T/G that make up our genetic code. Since then, we have collected thousands of genome-wide measurements about the activity of the genome. These measurements span hundreds of biochemical properties of the genome, such as the measurement of which base pairs a given protein binds to, and span hundreds of human tissues. In total, we have on the order of 10^13 genomic measurements. Yet despite these massive data sets, much of how the genome functions remains unknown. ******The goal of my research is to discover new types of genomic functions. To do this, I develop computational methods that automatically discover patterns in high-dimensional data sets; these algorithms are known as unsupervised machine learning algorithms. In the past, I developed a class of unsupervised genome annotation algorithms and used them to discover several new categories of functional activity in the human genome. Inspired by this success, I will apply the same discovery-based research philosophy to continue to improve our understanding of genome biology, through (1) leveraging newly available types of data, and (2) developing new unsupervised machine learning algorithms that can discover more complex patterns that was possible in the past. ******This work will have impact in two independent ways. First, I will discover new mechanisms of genome biology, which will in turn improve our understanding of human heritable traits, disease and evolution. Second, I will develop new general-purpose methods for unsupervised machine learning. These methods will pave the way for automated discovery to become standard practice in other fields where high-dimensional data is available.