Cancer Institute

  • Sunil Mathur

    mathur_sunil.jpgAssociate Professor, Department of Mathematics
    Cancer Genetics Program
    PhD, Statistics, 1999, University of Delhi, India

    Contact information
    Hume 325, Department of Mathematics
    University of Mississippi, University, MS 38677
    Phone: (662) 915-7398
    E-mail: skmathur@olemiss.edu

    Research interests

    • Biostatistics
    • Genomics
    • Epidemiology
    • Statistics

    Research synopsis

    My research interests focus on both statistical applications and theoretical development of statistics. My research interests include nonparametric statistics, likelihood procedures, genomics, epidemiology, and biostatistics. My statistical application work has contributed to the field by developing solutions to some of the complex problems in genomics. Some of the developed statistical test procedures (Mathur and Sadana, 2011; Mathur, 2009; Mathur and Dolo 2007; Bokka and Mathur, 2007; Mathur, Doke, and Sadana, 2006) identify differentially expressed genes using genomics data. These test procedures are efficient as compared to their competitors, and one of the unique qualities of these procedures is that they can be applied even when the sample size is small or when distribution is unknown. My recent work has been in the area of proteomics. The aim of my research is to determine the specific proteins and their abundance and compare them across different experiments (patients), outcomes, and treatment groups, and then find protein markers for the disease.

    Proteins play the most important part in the creation and development of cells and tissues, and directing their functions. Identification of the protein signatures of various diseases, especially at their onset will help tremendously in diagnosis and treatment. These protein signatures are particularly important in drug design and clinical trials and help us to understand how the proteins change during a particular treatment or phase of growth. My work will enable researchers in medical field to identify these protein signatures and hence help the researchers to develop targeted drugs and cure the disease in more effective ways. My work in the area of nonparametric inference has focused on bivariate and multivariate location problems. In some of the papers (Mathur and Smith, 2007; Mathur and Dolo, 2007; Bokka and Mathur, 2006; Mathur, 2005; Sen and Mathur, 1997, 2000), we proposed new methodologies for analyzing bivariate data using nonparametric procedures.

    Recent accomplishments and honors

    • Faculty Research Fellow Award, University of Mississippi, 2006, and 2007
    • First Prize, Sigma Xi Research Poster Competition, University of Mississippi, 2007

    Selected publications

    • Statistical Bioinformatics with R, Academic Press, USA, January 2010.
    • Mathur, S. K. (2009). A Run based Procedure to Identify Time-lagged Gene Clusters in Microarray Experiments. Statistics in Medicine, 28 (2), 326-337.
    • Mathur, S.K. (2009). A New Nonparametric Bivariate Test for Two-Sample Location Problem. Statistical Methods and Applications, 18 (3), 375-388.
    • Murthy, S.S., Kiran, V.S.R, Mathur, S.K., and Murthy, S. (2008). Noninvasive Transcutaneous Sampling of Glucose by Electroporation. Journal of Diabetes Science and Technology, 2(2), 250-254.
    • Mathur, S.K., and Smith, P.F. (2008). An Efficient Nonparametric Test for Bivariate Two-Sample Location Problem, Statistical Methodology, 5 (2), 142-159.
    • Mathur, S.K. and Dolo, S. (2008). A new Efficient Statistical Test for Detecting Variability in the Gene Expression Data, Statistical Methods in Medical Research, 17, 405-419.