メディカルインフォマティクスのためのゲノム関連データの解析法とその問題点

• 松浦 正明

  （財）癌研究会 ゲノムセンター 情報解析グループ

• 牛嶋 大

  （財）癌研究会 ゲノムセンター 情報解析グループ

• 宮田 敏

  （財）癌研究会 ゲノムセンター 情報解析グループ

書誌事項

タイトル別名

• Analyses of Genome-related Data for Medical Informatics
• メディカルインフォマティクス ノ タメ ノ ゲノム カンレン データ ノ カイセキホウ ト ソノ モンダイテン

抄録

Recently many methods and tools for bioinformatics have been developed rapidly due to technological progress and the successful Genome Project. However new methodologies for detecting useful and important biomarkers or causal disease genes are still to be developed in order to establish tailor-made medical treatments or personalized medicine. This paper discusses the high throughput genome-related data with clinical information and reviews methods of analyses to search for biomarkers or causal genes. We point out problems in data for statistical analyses and in methods used widely as standards. The data explained here include SNP (Single Nucleotide Polymorphism) data, microarray data and mass-spectrometry proteome data. As for the analyses for high throughput data we discuss study design issues, problems in multidimensional data, and False Discovery Rate (FDR) in multiple testing problems. In the SNP data analyses, we describe that haplotype block based research has replaced separate SNP based analyses as a main research style in association study. In the microarray data analyses, we introduce the usefulness of AdaBoost to search for biomarkers as well as to analyze in the proteome data.

収録刊行物

• 計量生物学

  計量生物学 25 (2), 117-134, 2004

  日本計量生物学会

キーワード

• Bioinformatics
• SNP
• Microarray
• Proteomics
• Mass-spectrometry
• AdaBoost

詳細情報

• CRID: 1390282679345108992
• NII論文ID: 10014334931
• NII書誌ID: AA11591618
• DOI: 10.5691/jjb.25.117
• ISSN: 21856494
• ISSN: 09184430
• NDL書誌ID: 7240911
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7240911
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可