Maulik Ujjwal, Mukhopadhyay Anirban, Bandyopadhyay Sanghamitra
Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India.
IEEE Trans Inf Technol Biomed. 2009 Nov;13(6):969-75. doi: 10.1109/TITB.2009.2017527. Epub 2009 Mar 16.
Microarray technology enables the simultaneous monitoring of the expression pattern of a huge number of genes across different experimental conditions. Biclustering in microarray data is an important technique that discovers a group of genes that are coregulated in a subset of conditions. Biclustering algorithms require to identify coherent and nontrivial biclusters, i.e., the biclusters should have low mean squared residue and high row variance. A multiobjective genetic biclustering technique is proposed here that optimizes these objectives simultaneously. A novel encoding scheme that uses variable chromosome length is developed. Moreover, a new quantitative measure to evaluate the goodness of the biclusters is proposed. The performance of the proposed algorithm has been evaluated on both simulated and real-life gene expression datasets, and compared with some other well-known biclustering techniques.
微阵列技术能够在不同实验条件下同时监测大量基因的表达模式。微阵列数据中的双聚类是一种重要技术，可发现一组在部分条件下共调控的基因。双聚类算法需要识别连贯且有意义的双聚类，即双聚类应具有低均方残差和高行方差。本文提出了一种多目标遗传双聚类技术，可同时优化这些目标。开发了一种使用可变染色体长度的新颖编码方案。此外，还提出了一种评估双聚类质量的新定量方法。所提算法的性能已在模拟和实际基因表达数据集上进行了评估，并与其他一些著名的双聚类技术进行了比较。
