【論】Okada,2005,Knowledge-assisted recognition of ～

Y.Okada, T.Sahara, H.Mitsubayashi, S.Ohgiya, T.Nagashima.
Knowledge-assisted recognition of cluster boundaries in gene expression data.
Artif Intell Med. 2005 Sep-Oct;35(1-2):171-83.
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・遺伝子データベースの情報を利用して、クラスタの閾値を自動的に決定する方法を提案する。
・データ：その1 Yeast, Cell cycle data, 416遺伝子[Cho] その2 Yeast, Cold stress response data, 1609遺伝子[Sahara]
・クラス分け法：Hierarchical clustering, K-means, SOM, AutoClass
・クラス分け評価指標：The rand index, The cluster separation, The cluster entropy

・目的「The aim of our algorithm is to find cluster boundaries so as to compose functionally independent agglomerations on the dendrogram derived from similarities among gene expression patterns.」
・アルゴリズム「The algorithm proposed in this paper is composed of three steps: (1) hierarchical clustering based on gene expression profiles, (2) automatic determination of the cluster boundaries using the VIF among the Gene Function Vectors representing distributions of gene functions in each cluster, and (3) annotation for each cluster according to dominant gene functions within the respective clusters.」

《チェック論文》
[1]Sahara T, Goda T, Ohgiya S.,Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature., J Biol Chem. 2002 Dec 20;277(51):50015-21. Epub 2002 Oct 11.
[2]Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ, Davis RW.,A genome-wide transcriptional analysis of the mitotic cell cycle., Mol Cell. 1998 Jul;2(1):65-73.
[3]Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL.,Model-based clustering and data transformations for gene expression data.,Bioinformatics. 2001 Oct;17(10):977-87.
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