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Brian D. Athey

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Data science applications: Bioinformatics and ‘Big Data’ based customizable pipelines that predict pharmacogenomic response elements for clinical testing in psychiatric disease; Integrative Biomedical Informatics platforms such as tranSMART (see tranSMART Foundation.org); and High-throughput multi-scale imaging and analysis platforms directed to understanding the ‘4D Nucleome’.

The Athey Lab in the Department of Computational Medicine and Bioinformatics (DCM&B) University of Michigan Medical School, is led by Dr. Brian Athey (see Atheylab.ccmb.med.umich.edu).

The lab is working on two complementary domains of research and development.

1. The Athey Lab’s recent research interests are in the creation and use of bioinformatics pipelines and machine learning methods to radically improve the efficacy of psychiatric pharmacogenomics—allowing patients to take the most effective drug for their illness and suffer the fewest side effects. This area of research centers on the exploration of the ‘pharmacoepigenome’ in psychiatry, neurology, anesthesia and addiction medicine. This research employs high-throughput 4D microscopic imaging of enhancers, promoters and chromatin features, using fluorescence in situ hybridization (FISH). These methods are coupled with Hi-C chromatin conformation capture, chromatin state annotation, localization in postmortem human brain tissue and induced neuronal pluripotent stem cells, and machine learning for identification of regulatory variants, to provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response

2. The Athey Lab is also developing new high-throughput methods to analyze images of genes in the context of the cellular nucleus to better understand the machinery of bioinformatics in context. One main area of research is the application of high resolution fluorescence optical microscopy coupled with high-throughput analysis, 3D imaging and machine learning to explore the chromatin structure and nuclear architecture of cells. This research emphasizes the convergence between 3D structural predictions and 3D structural measurements with microscopy, to provide insight into the transcriptional architecture of the interphase nucleus.

This area of research centers on the exploration of the ‘pharmacoepigenome’ in psychiatry, neurology, anesthesia and addiction medicine. This research employs high-throughput 4D microscopic imaging of enhancers, promoters and chromatin features, using fluorescence in situ hybridization (FISH). These methods are coupled with Hi-C chromatin conformation capture, chromatin state annotation, localization in postmortem human brain tissue and induced neuronal pluripotent stem cells, and machine learning for identification of regulatory variants, to provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response.

Collaborations: The lab works very closely with Assurex Health, Inc. (Mason, Ohio) on project 1. This work is governed by a Regents-approved Master Agreement between U-M and Assurex Health, Inc. Similarly, the lab collaborates closely with the tranSMART Foundation (tF), and this is also governed by a Master Agreement between U-M and tF.

The lab collaborates with the Brady Urological Institute at Johns Hopkins Medical School, lead by Dr. Ken Pienta, to build on their extensive 2D characterization of prostate tumors, by the introduction of simple chromatin dyes, advanced biomarkers, and 3D imaging systems.

The lab works closely with Dr. John Wiley of University of Michigan Health System, studying the effect of glucocorticoids on the neuroblastoma based cell line Sy5y before and after treatment with retinoic acid and BDNF, particularly in their terminally differentiated condition.

The lab also collaborates with Dr. Christoph Cremer from the Institute of Molecular Biology in Mainz, Germany, investigating super-resolution microscopy techniques.

Lithium response network in human brain in bipolar disorder: A regulatory network in human brain mediating lithium response in bipolar patients was revealed by analysis of functional single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping. Noncoding SNPs characterized as altering enhancer and promoter function were imputed using fine epigenetic mapping, followed by bioinformatics analysis. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p < 10-27; Fisher’s exact test) with this ionotropic AMPA2 glutamate receptor network in human brain67. Higgins GA, Allyn-Feuer A,Barbour E and Athey BD. “A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis.” Accepted, Journal of Pharmacogenomics. August 2015.

Lithium response network in human brain in bipolar disorder: A regulatory network in human brain mediating lithium response in bipolar patients was revealed by analysis of functional single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping. Noncoding SNPs characterized as altering enhancer and promoter function were imputed using fine epigenetic mapping, followed by bioinformatics analysis. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p < 10-27; Fisher’s exact test) with this ionotropic AMPA2 glutamate receptor network in human brain67. Higgins GA, Allyn-Feuer A,Barbour E and Athey BD. “A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis.” Accepted, Journal of Pharmacogenomics. August 2015.

HeroJan2010

Alfred Hero

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Alfred O. Hero, PhD, is the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan and co-Director of the Michigan Institute for Data Science.

The Hero group focuses on building foundational theory and methodology for data science and engineering. Data science is the methodological underpinning for data collection, data management, data analysis, and data visualization. Lying at the intersection of mathematics, statistics, computer science, information science, and engineering, data science has a wide range of application in areas including: public health and personalized medicine, brain sciences, environmental and earth sciences, astronomy, materials science, genomics and proteomics, computational social science, business analytics, computational finance, information forensics, and national defense. The Hero group is developing theory and algorithms for data collection, analysis and visualization that use statistical machine learning and distributed optimization. These are being to applied to network data analysis, personalized health, multi-modality information fusion, data-driven physical simulation, materials science, dynamic social media, and database indexing and retrieval. Several thrusts are being pursued:

  1. Development of tools to extract useful information from high dimensional datasets with many variables and few samples (large p small n). A major focus here is on the mathematics of “big data” that can establish fundamental limits; aiding data analysts to “right size” their sample for reliable extraction of information. Areas of interest include: correlation mining in high dimension, i.e., inference of correlations between the behaviors of multiple agents from limited statistical samples, and dimensionality reduction, i.e., finding low dimensional projections of the data that preserve information in the data that is relevant to the analyst.
  2. Data representation, analysis and fusion on non-linear non-euclidean structures. Examples of such data include: data that comes in the form of a probability distribution or histogram (lies on a hypersphere with the Hellinger metric); data that are defined on graphs or networks (combinatorial non-commutative structures); data on spheres with point symmetry group structure, e.g., quaternion representations of orientation or pose.
  3. Resource constrained information-driven adaptive data collection. We are interested in sequential data collection strategies that utilize feedback to successively select among a number of available data sources in such a way to minimize energy, maximize information gains, or minimize delay to decision. A principal objective has been to develop good proxies for the reward or risk associated with collecting data for a particular task (detection, estimation, classification, tracking). We are developing strategies for model-free empirical estimation of surrogate measures including Fisher information, R'{e}nyi entropy, mutual information, and Kullback-Liebler divergence. In addition we are quantifying the loss of plan-ahead sensing performance due to use of such proxies.
Correlation mining pipeline transforms raw high dimensional data (bottom) to information that can be rendered in interpretable sparse graphs and networks, simple screeplots, and denoised images (top). The pipeline controls data collection, feature extraction and correlation mining by integrating domain information and its assessed value relative to the desired task (on left) and accounting for constraints on data collection budget and uncertainty bounds (on right).

Correlation mining pipeline transforms raw high dimensional data (bottom) to information that can be rendered in interpretable sparse graphs and networks, simple screeplots, and denoised images (top). The pipeline controls data collection, feature extraction and correlation mining by integrating domain information and its assessed value relative to the desired task (on left) and accounting for constraints on data collection budget and uncertainty bounds (on right).