Near Unsupervised Learning For Early Discovery Of Novel Patterns: Methods, Scalability And Label Dependability

Grant number: DP1096296 | Funding period: 2010 - 2014

Completed

Abstract

This project aims to predict the unknown class labels using the existing small number of class labels. The outcomes of the project have direct relevance to the economy, environment, energy and health sectors due to the abundance of data coming out of these areas. For example, if an oncogene, a gene that can cause cancer when mutated can be found using data with only few labels and a large amount of unlabelled data, the costs and time needed for lab experimentation can be greatly reduced enabling pharmaceutical companies to develop corresponding medicines quicker. It will not only save more lives but also generates millions of dollars of income.

Related publications (9)

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Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges

Mario A Munoz, Yuan Sun, Michael Kirley, Saman K Halgamuge

2015-10-01

Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challeng..

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EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data

Chalini D Wijetunge, Isaam Saeed, Berin A Boughton, Jeffrey M Spraggins, Richard M Caprioli, Antony Bacic, Ute Roessner, Saman K Halgamuge

2015-10-01

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Extended Differential Grouping for Large Scale Global Optimization with Direct and Indirect Variable Interactions

Yuan Sun, Michael Kirley, Saman K Halgamuge

2015-01-01

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Inferring copy number and genotype in tumour exome data

Kaushalya C Amarasinghe, Jason Li, Sally M Hunter, Georgina L Ryland, Prue A Cowin, Ian G Campbell, Saman K Halgamuge

2014-08-28

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Bioinformatics Pipelines for Targeted Resequencing and Whole-Exome Sequencing of Human and Mouse Genomes: A Virtual Appliance Approach for Instant Deployment

Jason Li, Maria A Doyle, Isaam Saeed, Stephen Q Wong, Victoria Mar, David L Goode, Franco Caramia, Ken Doig, Georgina L Ryland, Ella R Thompson, Sally M Hunter, Saman K Halgamuge, Jason Ellul, Alexander Dobrovic, Ian G Campbell, Anthony T Papenfuss, Grant A McArthur, Richard W Tothill

2014-04-21

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On the Selection of Fitness Landscape Analysis Metrics for Continuous Optimization Problems

Yuan Sun, Saman K Halgamuge, Michael Kirley, Mario A Munoz

2014-01-01

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Exploratory analysis of high-throughput metabolomic data

Chalini D Wijetunge, Zhaoping Li, Isaam Saeed, Jairus Bowne, Arthur L Hsu, Ute Roessner, Antony Bacic, Saman K Halgamuge

2013-12-01

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Microbial and viral metagenomes of a subtropical freshwater reservoir subject to climatic disturbances

Ching-Hung Tseng, Pei-Wen Chiang, Fuh-Kwo Shiah, Yi-Lung Chen, Jia-Rong Liou, Ting-Chang Hsu, Suhinthan Maheswararajah, Isaam Saeed, Saman Halgamuge, Sen-Lin Tang

2013-12-01

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CoNVEX: copy number variation estimation in exome sequencing data using HMM

KC Amarasinghe, J Li, SK Halgamuge

2013-01-01

BACKGROUND: One of the main types of genetic variations in cancer is Copy Number Variations (CNV). Whole exome sequencing (WES) is..