John Boik's Stanford Home Page

Background

I am currently a VIGRE postdoctoral fellow in the Department of Statistics at Stanford University. I received my Ph.D. in Biomedical Sciences in August of 2007 from the University of Texas, Graduate School of Biomedical Sciences, Houston, with an area of focus in Biomathematics and Biostatistics. I conducted my work at M.D. Anderson Cancer Center in the Pharmaceutical Development Center under adviser Robert A Newman. My background is varied. Prior to my work in Texas, I obtained a BS in civil engineering from the University of Colorado, Boulder, and a masters degree in Oriental Medicine from Oregon College of Oriental Medicine, Portland, OR. After obtaining my master's degree I operated an acupuncture clinic in Portland for a couple of years. In my study of Traditional Chinese Medicine, I became interested in the use of plant compounds for treating diseases, cancer in particular. This interest eventually led to the publication of two books, which were essentially summaries and syntheses of published preclinical and clinical studies. Between books I met Dr. Newman, and he later invited me to M.D. Anderson to work on a Ph.D.

Research Interests

My primary interest is in developing statistical and dynamic models to predict the pharmacokinetic properties of plant-derived compounds, as well their pharmacological effects on cancer cells. The approach I have taken is systematic in that rather than studying a particular compound or a small set of similar compounds, I am interested in developing larger, more complex models to predict the properties of arbitrary compounds. Accurate predictive models will be useful to efficiently screen large libraries of compounds for promising drugs. Due to synergistic interactions, combinations of compounds can sometimes be more effective than single drugs. Thus I am also interested in modeling drug interactions, which can depend upon protein-drug binding affinity and protein-protein interactions. Specific research interests include:

Quantitative structure-activity relationship (QSAR) models and other statistical models for predicting drug activity and absorption, distribution, metabolism, excretion (ADME) characteristics
Statistical learning methods, including kernel learning methods (support vector machines, for example)
Prediction of protein-drug binding affinity and protein-protein interactions using knowledge-based approaches
Learning methods for relational data
Systems biology, in general
Methods to quantify drug interactions (synergism/antagonism/additivity)

Publications

Boik JC, Newman RA, Boik RJ. Quantifying synergism/antagonism using nonlinear mixed-effects modeling: A simulation study. Statistics in Medicine. 2008
Boik JC, Newman RA. A classification model to predict synergism/antagonism of cytotoxic mixtures using protein-drug docking scores. BMC Pharmacol 2008
Boik JC, Newman RA. Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds. BMC Pharmacol 2008
Kaufman PB, Duke JA, Brielmann H, Boik J, Hoyt JE. A comparative survey of leguminous plants as sources of the isoflavones, genistein and daidzein: implications for human nutrition and health. J Altern Complement Med. 1997
Boik JC. Natural Compounds in Cancer Therapy. Princeton MN: Oregon Medical Press; 2001
Boik JC. Cancer and Natural Medicine. Princeton MN: Oregon Medical Press; 1996

Teaching links

STATS 141, Biostatistics, Fall 2007, R Computing Lab
STATS 60, Intro to Statistical Methods, Spring 2008, Course files
STATS 141, Biostatistics, Fall 2008
STATS 60, Intro to Statistical Methods, Spring 2009

Contact

Department of Statistics -- Sequoia Hall
390 Serra Mall
Stanford University
Stanford, CA 94305-4065