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Jason H Moore
University of Pennsylvania
Philadelphia, Pennsylvania
 

Jason Moore is the Frank Lane Research Scholar in Computational Genetics and Director of Bioinformatics at Dartmouth Medical School, where he holds positions as Associate Professor of Genetics and Adjunct Associate Professor of Community and Family Medicine. He also holds positions as Affiliate Associate Professor of Computer Science at the University of New Hampshire and Adjunct Associate Professor of Computer Science at the University of Vermont. He was previously an Ingram Associate Professor of Cancer Research at Vanderbilt University Medical School, where he held positions as Assistant and Associate Professor of Molecular Physiology and Biophysics. He has won numerous awards including the James V. Neel Young Investigator award from the International Genetic Epidemiology Society and a Best Paper award from the ACM Genetic and Evolutionary Computing Conference (GECCO). He is currently Editor in Chief of a new journal, BioData Mining, that is published by BioMed Central.

Moore is a graduate of the University of Michigan in Ann Arbor, where he earned an MS in Human Genetics, an MA in Applied Statistics, and a PhD in Human Genetics. His dissertation work focused on computational and statistical methods for the genetic analysis of blood pressure change over a 24-hour period. He is also a graduate of Florida State University in Tallahassee, where he earned a BS in Biological Sciences.

His research focuses on the development, evaluation, and application of data mining and machine learning methods for the identification of genetic, genomic, and proteomic predictors of common human diseases, such as cancer and cardiovascular disease. Moore and his team have developed a number of novel machine learning methods, including multifactor dimensionality reduction (MDR) that uses constructive induction to detect nonlinear interactions among two or more attributes. He has published more than 150 peer-reviewed papers in journals and conference proceedings, and has delivered more than 100 invited lectures, including keynote lectures at the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology and the 2006 Annual Conference on the Mathematics of Information Technology and Complex Systems. More information about Moore and his work can be found at www.epistasis.org.


     

 A vision for heart rate health through wearables
Albaghli R., Anderson K.  UbiComp 2016 (Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, Sep 12-16, 2016) 1101-1105, 2016.  Type: Proceedings

Mobile health (mHealth) is an increasingly important area of biomedical informatics that is focused on the use of wearable sensors, such as those found in smartphones, to monitor health. This is an exciting area because of the widespre...

 

 Tractome: a visual data mining tool for brain connectivity analysis
Porro-Muñoz D., Olivetti E., Sharmin N., Nguyen T., Garyfallidis E., Avesani P. Data Mining and Knowledge Discovery 29(5): 1258-1279, 2015.  Type: Article

Brain imaging has revolutionized neuroscience and related areas such as neurogenetics by providing detailed measures of brain structure and function that can be used to advance our understanding of neuroanatomy and neurophysiology. Thi...

 

Exploring the interdisciplinary evolution of a discipline: the case of biochemistry and molecular biology
Chen S., Arsenault C., Gingras Y., Larivière V. Scientometrics 102(2): 1307-1323, 2015.  Type: Article

It is natural to subdivide science into disciplines that can focus on particular aspects of scientific inquiry. This becomes a problem when two or more disciplines are required to solve a complex research problem. For example, the succ...

 

EpiMiner: a three-stage co-information based method for detecting and visualizing epistatic interactions
Shang J., Zhang J., Sun Y., Zhang Y. Digital Signal Processing 241-13, 2014.  Type: Article

Common human diseases are the result of complex interactions between many genetic and environmental factors. Parametric statistical methods such as logistic regression are underpowered to detect interactions due to unstable parameter e...

 

Living cell as a universal computer
Sergienko I., Biletskyy B., Gupal A. Cybernetics and Systems Analysis 49(4): 562-568, 2013.  Type: Article

Computer science has long looked to biology for inspiration in the design of computer systems and software, and the reverse is also true. This paper explores the connection between the biomolecular processes that occur in a cell and th...

 
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