Tomida Shuta, Hanai Taizo, Honda Hiroyuki, Kobayashi Takeshi
Department of Biotechnology, School of Engineering Nagoya University, Furo-cho, Chikusa-ku, Japan.
Bioinformatics. 2002 Aug;18(8):1073-83. doi: 10.1093/bioinformatics/18.8.1073.
It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report.
We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. The clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). In terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence.
The software is available at http//www.nubio.nagoya-u.ac.jp/proc/index.html
众所周知，成功的表达谱聚类能为理解未表征基因的功能提供有益思路。为实现这种成功的聚类，我们在本报告中研究了一种基于自适应共振理论（ART）的聚类方法。
我们应用模糊ART作为聚类方法来分析酿酒酵母孢子形成过程中的时间序列表达数据。将模糊ART的聚类结果与其他聚类方法（如层次聚类、k均值算法和自组织映射（SOM））的结果进行了比较。在数学验证方面，模糊ART实现了最合理的聚类。我们还使用噪声数据验证了模糊ART的稳健性。此外，我们基于时间表达具有生物学特征的基因定义了聚类正确率。使用这个定义，证明了模糊ART的聚类能力优于层次聚类、k均值算法和SOM等其他聚类方法。最后，我们从生物学功能和证据方面验证了模糊ART的聚类结果。
该软件可在http//www.nubio.nagoya-u.ac.jp/proc/index.html获取
