SLAS2017 Short Courses
Data Analytic Concepts for High Throughput Screening & Biomarker Applications NEW!
This short course will provide an overview of some data analytic concepts such as the MSR (minimum significant ratio) and error-rate estimation for HTS, and FDR (false discovery rate), multivariate predictive modeling process and performance evaluation for high-dimensional -omics data, and some statistical considerations for biomarker assay evaluations.
Who Should Attend:
- Bioinformaticians & Statisticians new to the field
How You Will Benefit From This Course:
- High-level understanding of key fundamental concepts around high-dimensional data from HTS, genomics and related applications.
- Reproducibility, robustness evaluation and error-rate estimation for HTS
- Assay correlations, comparisons & multivariate modeling
- False Discovery Rate, Hit selection, etc., for -omics data
- Multivariate signatures, predictive modeling & performance evaluation
- Statistical considerations for biomarker assay evaluation
Dr. Devanarayan is the Global Head of Exploratory Statistics and a Senior Research Fellow at AbbVie, and an elected Fellow of the American Association of Pharmaceutical Scientists (AAPS). He has 20 years of combined pharmaceutical research experience from Eli Lilly, Merck, and AbbVie. His statistical & data-analytic contributions across drug discovery and development include applications such as target identification, high-throughput-screening, genomics, proteomics, bioanalytical methods, biomarker discovery, and precision medicine. He has filed 10 patent applications, given over 100 invited talks at scientific meetings, and co-authored over 50 publications. He has published several white-papers with regulatory, academic and industry scientists on immunogenicity, bioanalytical methods, genomics, and biomarker qualification. He is currently also an Adjunct Professor at Northern Illinois University and the University of Illinois in Chicago.