“1-bit” Compressed Sensing and Genetic Disease

Written by: Stephen Hsu

Primary Source:  Information Processing

This is an ASHG poster (click for larger version) describing work on predictive modeling of genetic disease using Compressed Sensing. Our previous work dealt with continuous traits (quantitative phenotypes). In the case of disease, one sometimes only has binary data to work with: individuals in the sample are either cases (have the condition) or controls (do not have the condition). Their underlying genetic susceptibility to the condition is not directly measurable. However, sophisticated techniques can use even this type of data to deduce the underlying genetic architecture. As in our earlier work, we demonstrate a “phase transition” in the performance of our algorithms as the amount of data available increases.

See related posts on quantitative traits: linear models 2, linear models, nonlinear method, and this talk: Genetic architecture and predictive modeling of quantitative traits.

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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