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Volume 11

Journal of Proteomics & Bioinformatics Open Access

Computational Biology 2018

September 05-06, 2018

September 05-06, 2018 Tokyo, Japan

International Conference on

Computational Biology and Bioinformatics

J Proteomics Bioinform 2018, Volume 11

DOI: 10.4172/0974-276X-C1-113

Statistical machine learning in big data analytics

S Ejaz Ahmed

Brock University, Canada

N

owadays a large amount of data is available and the need for novel statistical strategies to analyze such data sets is pressing.

This talk focuses on the development of statistical and computational strategies for a sparse regression model in the

presence of mixed signals. The existing estimation methods have often ignored contributions from weak signals. However,

many predictors altogether provide useful information for prediction, although the amount of such useful information in

a single predictor might be modest. The search for such signals, sometimes called networks or pathways, is for instance an

important topic for those working on personalized medicine. We discuss a new post selection shrinkage estimation strategy that

considers the joint impact of both strong and weak signals to improve the prediction accuracy and opens pathways for further

research in such scenarios.

sahmed5@brocku.ca