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Abstract of the talk:
Statisticians play important roles in drug discovery and development: design, power calculation, data management and analysis of studies, communication of results and report writing etc. In this talk, I will give a brief overview of drug development from a statistician’s point of view, introduce biomarkers in drug discovery and development, then focus on two case studies applying machine learning methods for biomarker analyses. Case 1: Application of Random Forest to building Proteomics Classifier; Case 2: Gene signature derivation using SuperLearning with application to the epithelial mesenchymal transition in lung cancer.

About Dr. Song:
Dr. Qinghua Song got his PhD from Department of Statistics, University of Wisconsin-Madison in 2005. Since then he has worked in world top pharmaceutical companies such as Merck, Genentech and Gilead. He has provided statistical analysis support for research, non-clinical, pre-clinical, PKPD and clinical studies, and co-authored on multiple scientific papers in application of machine learning, biomarker selection, application of statistical modeling, analysis on large data such as Mass Spectrometry Proteomics, Next Generation Sequencing and High Throughput Screening. He managed a group of statisticians for early phase virology studies in Gilead Science and he is leading a group of data scientists, to develop efficient and innovative statistical tools for data visualization and advanced analytical methods.