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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