People:
Md Bahadur Badsha: Ph.D, Kyushu Institute of Technology, Japan. Postdoc Research Fellow.
Rui Li: Ph.D, Washington State University. Postdoc Research Fellow.
Evan Martin: Ph.D student in The Graduate Program in Bioinformatics and Computational Biology.

Research interests:

In general, I am interested in developing statistical models and efficient computational methods for genetic and genomic data. Past projects include:

  • A Bayesian clustering method and associated MCMC algorithm for efficiently clustering time-course gene expression data;
  • Bayesian hierarchical models and associated Markov Chain Monte Carlo (MCMC) algorithms for DNA methylation data;
  • A hidden Markov model for studying identity-by-descent in sibling pairs while accounting for linkage disequilibrium.

I am also interested in applying statistical learning and computational methods in tackling problems related to human genetics and diseases. Past and ongoing projects include:

  • Application of a regression model and survival analysis methods to integrate high-throughput RNAi double knockdown data with diverse genomic and clinical data from The Cancer Genome Atlas (TCGA) consortium. We inferred a map of interactions for frequently mutated genes in breast cancer, and explored its topological properties, implications for gene regulation and impact on the survival of cancer patients.
  • Analysis of sequencing data from the 454 technology of the MHC region in HIV patients showing various degrees of resistance to HIV progression.
  • Development of graphical model-based methods for inferring the cell differentiation process from single-cell gene expression data.

Publications:
Google Scholar

Software:
noise: R package for estimation of intrinsic and extrinsic noise from gene expression data from single-cell two-reporter experiments.
cancerGI: R package for analyses of cancer gene interactions, using RNAi knockdown data, as well as data from the TCGA consortium.
DIRECT: R package for Bayesian clustering of multivariate data under the Dirichlet-process prior.
MethylHMM: A collection of R and C code for inference under hidden Markov models for double-stranded DNA methylation data.

Teaching:
Fall 2016: STAT 431 Statistial Analysis
Fall 2016: BCB 501 Research Seminar
Fall 2015: STAT 431 Statistial Analysis

Awards:

  • NIH Pathway to Independence Award (K99/R00), NIH/NHGRI, 2014-2018.
  • International Society for Bayesian Analysis Travel Award, 2010.
  • Dorothy and Leon Gilford Fellowship, Department of Statistics, University of Washington, 2003.

Email:
audreyf at uidaho dot edu

Mailing Address:
UI-Department of Statistical Science, University of Idaho
875 Perimeter Dr. MS 1104, Moscow, ID 83844-1104