- Applied mathematics
Michael L. Lecocke, Ph.D., is an alumnus of St. Mary’s University, having received his Bachelor of Science degree in Electrical Engineering, with a Minor in Mathematics, in 1999. He received both his Master of Arts and Ph.D. in Statistics at Rice University, finishing in 2005. In 2005, he began his career as a faculty member in the Department of Mathematics at St. Mary’s University.
In addition to his role as Associate Professor of Mathematics, Lecocke serves as the NCAA Faculty Athletics Representative for St. Mary’s University. He is also a Marianist Educational Associate at St. Mary’s. In the Department of Mathematics, he serves as the primary undergraduate adviser and works with the St. Mary’s Society of Mathematicians. He also works with the Actuarial Science Committee within the department (including successful completion of the first actuarial exam: Exam P – Probability). In 2009, he received the St. Mary’s University Distinguished Faculty Award for the School of Science, Engineering, and Technology.
His primary research interests lie in the fields of bioinformatics and biostatistics. In particular, the bulk of his research has involved the statistical analysis of high-dimensional gene expression data from microarrays, primarily in the context of cancer research.
Since 2005, Lecocke has also held an adjunct assistant professor position at the University of Texas Health Science Center at San Antonio in the Department of Epidemiology and Biostatistics.
He also maintains professional memberships in the Mathematical Association of America, the American Statistical Association (ASA), and the San Antonio Chapter of the ASA.
Zimmer S, Zhou Q, Zhou T, Cheng Z, Abboud-Werner S, Horn D, Lecocke M, White R, Krivtsov A, Armstrong S, Kung A, Livingston D, Rebel V. Crebbp haploinsufficiency in mice alters the bone marrow microenvironment, leading to loss of stem cells and excessive myelopoiesis. Blood. 118(1):69-79. May 2011.
Lecocke M. Feature Selection and Binary Classification Using Microarray Data: Backgrounds, Methods, and Comparison of Classifiers Using Univariate and Genetic Algorithm-Based Feature Selection and Supervised Learning Techniques, VDM Verlag Dr. Muller, Germany, 2008.
Lecocke M and Hess K. An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data. Cancer Informatics, 2:313-327, 2006.
Ayers M, Symmans, WF, Stec J, Damokosh A, Clark E, Hess K, Lecocke M, Metivier J, Bolt A, Brown J, Booser D, Ibrahim N, Valero V, Royce M, Arun B, Whitman G, Ross J, Sneige N, Hortobagyi GN, Pusztai L. Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel/FAC chemotherapy in breast cancer. Journal of Clinical ology, 22(12):2284-2293, June 2004.