课题基金 / 基金详情

Multivariate Methods for the Analysis of Microarray Gene-Expression Data with Applications to Cancer Diagnostics

Multivariate Methods for the Analysis of Microarray Gene-Expression Data with Applications to Cancer Diagnostics
微阵列基因表达数据分析的多变量方法及其在癌症诊断中的应用
批准号:
DP0772887
负责人:
Prof Geoffrey McLachlan
金额:
$62.64万
依托单位国家:
澳大利亚
项目类别:
Discovery Projects
财政年份:
2007
资助国家:
澳大利亚
项目状态:
已结题
起止时间:
2007-01-01 至 2011-12-31

项目摘要

项目成果

Prof Geoffrey McLachlan的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
The project will benefit the Australian Society as a whole by developing statistical methodology for the analysis of high-throughput data. In particular, it will develop a novel and easily implemented model for the analysis of correlated and structured data that may be of high dimension. It thus has wide applicability to improving the quality and validity of applied research in most industries in Australia. More specifically, it is to be applied here to the diagnosis and prognosis of ovarian cancer. This cross-disciplinary project will strengthen Australian researchers' capacity and capability of participating in cutting-edge DNA microarray research.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
A Novel Approach to Semi-Supervised Statistical Machine Learning
  • 批准号:
    DP230101671
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $29.08万
  • 财政年份:
    2023
  • 负责人:
    Prof Geoffrey McLachlan
  • 依托单位:
Classification methods for providing personalised and class decisions
  • 批准号:
    DP180101192
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $23.95万
  • 财政年份:
    2018
  • 负责人:
    Prof Geoffrey McLachlan
  • 依托单位:
Expanding the role of mixture models in statistical analyses of big data
  • 批准号:
    DP170100907
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $23.44万
  • 财政年份:
    2017
  • 负责人:
    Prof Geoffrey McLachlan
  • 依托单位:
Large-Scale Statistical Inference: Multiple Testing
  • 批准号:
    DP150103720
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $19.39万
  • 财政年份:
    2015
  • 负责人:
    Prof Geoffrey McLachlan
  • 依托单位:
国内基金
海外基金
Computational Methods for Analyzing Toponome Data