Near-unsupervised computational methods for exploring "omic" data

用于探索“组学”数据的近无监督计算方法

• 批准号: DP150103512
• 负责人: Prof Saman Halgamuge
• 金额: $ 18.37万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP150103512
• 关键词: Near; unsupervised; computational; methods; exploring

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.

该项目旨在通过批判性地分析和比较现有方法来研究最近提出的有前途的机器学习范式“近无监督学习”。该项目还旨在开发更广泛的大数据分析中的新算法，并将其应用于以下三个应用：宏基因组数据中的物种分离;开发将基因组信息与癌症药物敏感性相关的模型;以及鉴定质谱代谢组学数据中不同的代谢物分布模式。潜在的成果包括增加对肥料在不同植物品种和新出现的植物疾病上的有用性的理解，癌症的基因组变异以及对微生物的更多了解。