The research focus of the laboratory is the development of statistical and bioinformatics methods for learning fromdiverse genomics data types, and the application of such methods through interdisciplinary biomedical efforts. Members of the laboratory are also developing protocols for comprehensive data management and the bioinformatics analysis of microarray and next-gen sequencing data generated by the University of Cincinnati Genomics Core.   NIH-funded methodological research (PI: Medvedovic) Active U01HL111638 Integrative statistical methods and tools for analysis of perturbation signatures Funding Agency: NIH The objective of this research project is the development of statistical methods and computational tools for inferring mechanistic network models by integrative analysis of diverse perturbation signatures. These methods and infrastructure will open important new avenues for interpreting results from disease-related genomics experiments by comparing them to perturbation signatures and meta-signatures. The resulting infrastructure will remove methodological and infrastructural barriers for meaningful re-use of LINCS perturbation signatures and related network models, enabling scientists throughout the world to use as resource to gain insight into the genomic conditions underlying human disease. R01HG003749   Bayesian mixtures for modeling functional genomics data Funding Agency: NIH The objective of this research project is to develop a comprehensive framework for identifying statistically significant patterns in functional genomics data. Based on the Bayesian infinite mixture models, mathematical models will be developed that accommodate incorporation of prior knowledge and joint analysis of different data types in a context-specific framework. Corresponding computational tools for fitting these models will be developed, optimized and delivered to biomedical community by developing a Bioconductor package and as stand-alone command-line applications.   Completed R21 LM009662 Integrative Probabilistic Models for Identifying Transcriptional Modules Funding Institute: NLM We propose to develop Infinite Transcriptional Modules (ITM) framework consisting of a novel probabilistic model and related computational tools for identifying transcriptional modules by jointly modeling gene expression and regulatory data. The unifying probabilistic model will utilize the Infinite Mixtures Model mechanism for averaging over models with different number of modules and thus circumvent the problem of estimating the “correct” number of modules. Each different data type will be modeled separately within different context of a Context Specific Infinite Mixture Model. Such modular approach will facilitate the use of the most appropriate probabilistic models for representing different types of data. We hypothesize that our unifying modeling approach will result in significantly higher precision of identified transcriptional modules than it would be achieved by either separately analyzing different data types, or by applying currently available algorithms for joint analysis. We also expect that the posterior distribution of co-membership in a TM, based on our model, will offer credible assessment of statistical significance of identified TMs. Using real world data; we will construct datasets and protocols for objectively comparing key performance aspects of different methods for TM reconstruction. R03 LM 8248 Joint modeling of genomic and functional genomic data                 Funding Institute: NLM The objective of this study is to develop mathematical models and corresponding computational tools for efficient and reproducible extraction of relevant expression patterns, related regulatory motifs and genomic aberrations by jointly modeling genomic and functional genomic data. R21HG002849-01 Computational tools for Bayesian mixture modeling of functional genomic data Funding Institute: NHGRI The objective of this study is to develop computational tools for efficient and reproducible extraction of biologically significant patterns from functional genomic data. The computational procedures will be based on Infinite Bayesian Mixtures model which is unique in its ability to accommodate all sources of uncertainty in the process of identifying statistically significant patterns in noisy data.   Collaborative Research Projects Laboratory is also involved in providing bioinformatics support to several NIH-funded projects such as the Center for Environmental Genomics (CEG), Cincinnati's Breast Cancer and Environment Research Center (BCERC) and the efforts to dissect molecular mechanism of acute lung injury after exposure to hazardous chemical.