Website
http://dataportal.arc.gov.au/NCGP/API/grants/DP150103512
Funding Status
Closed
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Funded Activity Summary
Near-unsupervised computational methods for exploring "omic" data. The project aims to investigate the recently proposed promising machine learning paradigm "Near Unsupervised Learning" by critically analysing and comparing existing methods. The project also aims to develop new algorithms in the broader spectrum of Big Data Analytics and their adaptation to the following three applications: species separation in metagenomic data; development of a model to relate genomic information to cancer drug sensitivity; and, the identification of distinct metabolite distribution patterns in mass spectrometry metabolomic data. The potential outcomes include increased understanding of the usefulness of fertilisers on different plant varieties and newly emerging plant diseases, genomic variations in cancer and more insights into microbes.Funded Activity Details
Start Date: 2015
End Date: 12-2019
Funding Scheme: Discovery Projects
Funding Amount: $266,300.00
Funder: Australian Research Council
Research Topics
ANZSRC Field of Research (FoR)
Neural, Evolutionary and Fuzzy Computation | Artificial Intelligence and Image Processing
ANZSRC Socio-Economic Objective (SEO)
Environmentally Sustainable Plant Production not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in the Biological Sciences |
