Valentin Dinu, Ph.D.

The main objective of this research project is to develop an
informatics/statistics platform that will address the major
limitations encountered in the analysis of GWA study data by techniques that can dramatically reduce the number of computations needed and to facilitate user interaction for stepwise guidance of the analysis process. One particular focus of our approach is to explore the use of biomedical domain knowledge, such as pathway information,
to supplement statistical analysis and data mining methods in order to address the problem of computational limitations when analyzing GWAS data with the aim of identifying the causes of complex diseases.

Experience

2007 – Assistant Professor

Department of Biomedical Informatics, Arizona State University

2007 – Ph.D.

Computational Biology and Bioinformatics, Yale University

2000 – A.B.

Magna cum Laude with Highest Honors, Physics and Mathematics, Harvard University

1997 – 2003 – software consultant/engineer

IBM, Trilogy, Kiodex, AQR Capital Management

 

Collaborators

 

Jeffrey Kriseman, Research Associate, ASU
David Craig, Associate Investigator, TGen
John Pearson, Head, Bioinformatics Research Lab, TGen
Daniel Stanzione, Director, Fulton High Performance Computing, ASU
Dietrich Stephan, Director, Neurogenomics Division, TGen
Nicholas J. Carriero, Research Scientist, Yale
Judy H. Cho, Professor of Internal Medicine and Genetics, Yale
Josephine J. Hoh, Professor of Public Health and Ophthalmology, Yale
Matt Holford, CIS,Center for Statistical Genetics and Proteomics, Yale
Shrikant Mane,Director,Keck Biotechnology Resource Laboratory,Yale
Perry L. Miller, Director of Center for Medical Informatics, Yale
Hongyu Zhao, Professor of Public Health and Genetics, Yale

 

Jeffrey Kriseman,M.S.BMI

The ultimate goal of my research is to enable personalized medicine and facilitate a more efficient translation of basic biomedical research findings into the clinical practice.

Experience

2010 – Ph.D.

Biomedical Informatics, Arizona State University

2008 – M.S.

Magna cum Laude, Biomedical Informatics, Arizona State University

2003 – B.S.

Information Technology / Data Management

1993-present

Enterprise Software Development / Data Management

 

Current Research

 

Bioinformatics

 

BING! - Biomedical Informatics Next Generation Sequencing pipeline - In collaboration with TGEN
GWAS tools to enable biomedical researchers to identify genetic and environmental factors associated with disease; including Multiple Sclerosis, Alzheimer’s, Age-Related Macular Degeneration, among others.
VDRE - VDR Sequence Analysis

Clinical Informatics

 

Universal Healthcare Information Exchange - Supported by The Geneva Foundation
Analysis of ambulatory survey to uncover factors associated with physician diagnosis of cardio-vascular disease - University of Arizona School of Medicine.

Public Health Informatics

 

Data Management, Modeling, and Analysis of Public Health Data - Supported by The Center for Health Information and Research

Translational Informatics

 

RxExplorer - a tool to identify novel drug targets and better
understand biological basis of adverse drug events utilizing
biological, clinical, and public health data
Clinical Decision Support Systems - Supported by The Geneva Foundation

BioWorkbench - a web based collaborative toolkit including a dynamic workflow designer, engine, and integration of disparate data repositories to facilitate cross study analysis.

Chris Busick

Here at Dinulab, we investigate the best methods of implementing a network-wide solution to provide all the necessary tools required to be able integrate any type of bio-informatics data into an unified analytic system..

Experience

2009 – Research Associate

Department of Biomedical Informatics, Arizona State University

2006 – Research Associate

Department of Functional Genomics, Arizona State University

2010 – B.S.

Genetics, Arizona State University

 

Current Research

 

Bioinformatics

 

BING! - Biomedical Informatics Next Generation Sequencing pipeline - In collaboration with TGEN
GWAS tools to enable biomedical researchers to identify genetic and environmental factors associated with disease; including Multiple Sclerosis, Alzheimer’s, Age-Related Macular Degeneration, among others.

Transaltional Informatics

 

RxExplorer - a tool to identify novel drug targets and better
understand biological basis of adverse drug events utilizing
biological, clinical, and public health data

BioWorkbench - a web based collaborative toolkit including a dynamic workflow designer, engine, and integration of disparate data repositories to facilitate cross study analysis.

 

Justin Brown

Given the promise and potential encapsulated within the vast amount of new biological data available, it is imperative to develop and test methodologies which will allow researchers to better understand the meaning and complex relationships underlying such data.

Experience

2009 – Research Associate

Department of Biomedical Informatics, Arizona State University

2011 – Ph.D.

Biomedical Informatics, Arizona State University

 

Current Research

 

Bioinformatics

 

Cancer Immunosignaturing: Using peptide micro arrays we are attempting to model immune response of cancer patients with the goal of early diagnosis and ultimately a cancer vaccine Age Related Macular Degeneration Genome wide association study analysis to better understand gene environment interactions associated with the disease such, specifically related to Complement Factor H, vitamin use and smoking behavior. Cardiovascular Disease Analysis of ambulatory survey to uncover factors associated with physician diagnosis of cardiovascular disease

Transaltional Informatics

 

RxExplorer - a tool to identify novel drug targets and better
understand biological basis of adverse drug events utilizing
biological, clinical, and public health data

BioWorkbench - a web based collaborative toolkit including a dynamic workflow designer, engine, and integration of disparate data repositories to facilitate cross study analysis.

 

Ashutosh Singraur

Experience

2010 – Research Associate

Department of Biomedical Informatics, Arizona State University

2011 – M.S.

Biomedical Informatics, Arizona State University

 

Current Research

 

Bioinformatics

 

Computational design of peptide inhibitors for classB G protein coupled receptors:Prediction of 3-D structure of peptide-ECD complex of secretin receptor by global optimization of free energy function using commercial available ICM software. The structure of ECD is generated using comparative modeling based on structure of PACAP receptor. The set of peptides predicted to bind strongly to the secretin receptor ECD will then be experimentally tested using competition binding assays. The project is collaborated with Dr. Andrew Bordner at Mayo Clinic.

 

Sheetal Shetty

Experience

2010 – Research Associate

Department of Biomedical Informatics, Arizona State University

MS

Epidemiology, University of Arizona

2013 – Ph.D.

Biomedical Informatics, Arizona State University

 

Current Research

 

Bioinformatics

 

Schizophrenia Study: The study aims to hypothesize a pathway which can be implicated in the development of schizophrenia. We are doing this by identifying genes involved in various known biological pathways involving stress and depression and studying the expression of these genes in schizophrenia subjects through genome wide association studies. This project is being conducted in collaboration with Dr Amelia Gallitano of the University of Arizona, College of Medicine who is a leading expert in psychiatric diseases.

 

Nate Sutton

2011 – M.S.

Biomedical Informatics, Arizona State University

 

Current Research

 

Bioinformatics

 

Computational design of peptide inhibitors for classB G protein coupled receptors:Prediction of 3-D structure of peptide-ECD complex of secretin receptor by global optimization of free energy function using commercial available ICM software. The structure of ECD is generated using comparative modeling based on structure of PACAP receptor. The set of peptides predicted to bind strongly to the secretin receptor ECD will then be experimentally tested using competition binding assays. The project is collaborated with Dr. Andrew Bordner at Mayo Clinic.

Translational Informatics

 

BioWorkbench - a web based collaborative toolkit including a dynamic workflow designer, engine, and integration of disparate data repositories to facilitate cross study analysis.