Santiago Schnell

Santiago Schnell

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Dr. Schnell works at the interface between biophysical chemistry, mathematical and computational biology, and pathophysiology. As an independent scientist, his primary research interest is to use mathematical, computational and statistical methods to design or select optimal procedures and experiments, and to provide maximum information by analyzing biochemical data. His laboratory deals with the following topics:

(i) Development and implementation of mathematical, computational, and statistical methods to identify and characterize reaction mechanisms.

(ii) Investigate and test performance design of experiments or standards to quantify, interpret and analyze biochemical data.

(iii) Development of new algorithms and software to analyze biochemical data.

The key objective of my research is to create suitable standards and appropriate support of standards leading to reproducible results in the biochemical sciences. Reproducibility is central to scientific credibility. Meta-research has repeatedly shown that accurate reporting and sound peer-review do not by themselves guarantee the reproducibility of scientific results. One of the leading causes of poor reproducibility is limited research efforts in quantitative biology and chemometrics. In my laboratory, we are developing new ways to assess the reproducibility of quantitative findings in the biochemical sciences.

As a team scientist, Dr. Schnell’s research interest is to investigate complex biomedical systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole. His collaborators are primarily basic scientists who focus on the identification of molecular, biochemical or developmental mechanisms associated with diseases. To this end, Dr. Schnell’s expertise plays a central role in the identification of these mechanisms. Using mathematical and computational models, Dr. Schnell can formulate several hypothetical model mechanisms in parallel, which are compared with independent experimental data used to construct the models. The resulting comparisons are then independent between models, and any models that satisfy statistical measures of similarity will be used to make predictions, which will be tested experimentally by his collaborators. The model validated by the experiments will be considered the mechanism capable of explaining the behavior of the systems.

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Jieping Ye

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The Ye Lab has been conducting fundamental research in machine learning and data mining, developing computational methods for biomedical data analysis, and building informatics software. We have developed novel machine learning algorithms for feature extraction from high-dimensional data, sparse learning, multi-task learning, transfer learning, active learning, multi-label classification, and matrix completion. We have developed the SLEP (Sparse Learning with Efficient Projections) package, which includes implementations of large-scale sparse learning models, and the MALSAR (Multi-tAsk Learning via StructurAl Regularization) package, which includes implementations of state-of-the-art multi-task learning models. SLEP achieves state-of-the-art performance for many sparse learning models, and it has become one of the most popular sparse learning software packages. With close collaboration with researchers at the biomedical field, we have successfully applied these methods for analyzing biomedical data, including clinical image data and genotype data.

Cancer Center, April Harris

Jeremy M G Taylor

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I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

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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.

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Ivo D. Dinov

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Dr. Ivo Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).

Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.

Analyzing Big observational data including thousands of Parkinson's disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.

Analyzing Big observational data including thousands of Parkinson’s disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.