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 University1997 – 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
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
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.