From Data to Therapies Research in Xinghua Lu’s Lab Research Themes Cancer Pathway Discovery Decision support system for personalized medicine Machine learning Deep learning Text mining and knowledge representation http://www.saphana.com/community/blogs/blog/2013/10/15/sap-hana-for-healthcare-bringing-the-world-closer-to-real-time-personalized-medicine Grant support as PI: R01 LM 010144, R01 LM 011155 R01 LM012011 Collaborative funding U54HG008540-02   R01 GM100387 1U01HL112707

Integrative analysis of cancer genomic data to discover cancer pathways Combine genomic alteration and transcriptomic data to reveal driving pathways Pathway discovery leads to novel treatment strategy

Decision support system for personalized precision medicine Cancer is a genomic disease; each patient has unique genomic alteration and cellular signal perturbation Cancer can benefit from personalized precision medicine We are developing computational methods to support the above task at the individual patient level. Methods for identify driver genomic alterations in a given tumor Identify the pathways perturbed in the tumor Predict the sensitivity of a given tumor to existing drug therapy

Deep learning in biology Cellular signaling system is organized in hierarchy. Deep hierarchical models are a interesting framework to learn systems Identifying TF-binding motifs using deep convolutional neural networks. gene 7 gene 9 gene 5 gene 3 gene 2 gene 4 gene 6 gene 8 gene 10 gene 1 AACTGT CACTGT CACTCT Learning to predict human cell response based on rat cells’ responses