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Research

Biclustering Problem

    Biclustering is a kind of clustering technique that performs clustering on both the rows and columns of a matrix (usually a data set from special areas). In biology, the gene expression data, generated by DNA chips or other microarray techniques, are usually represented as matrices, whose rows contain the expression levels of genes and columns correspond to different conditions. It was Cheng and Church who firstly applied biclustering methods on gene expression data [1], and later Tanay et al. proposed the SAMBA[2] method based on statistical modelling and bipartite graph theory. Other latest proposed biclustering methods include Order Preserving Submatrix Algorithm (OPSM)[3], Iterative Signature Algorithm (ISA)[4], xMotif[5], cHawk[6], Genetic Fuzzy Biclustering Algorithm (GFBA)[7], Possibilistic Spectral Biclustering algorithm (PSB)[8], and other evolutionary methods[9].

 

Protein Methylation Prediction

    Protein Methylation Prediction

 

Publications

CONFERENCE PAPERS

  • Z. J. Ding, J. Yu, and Y.-Q. Zhang, "A New Improved K-means Algorithm with Penalized Term," Proc. of 2007 IEEE International Conference on Granular Computing (IEEE-GrC2007), Nov. 2-4, Silicon Valley, 2007.
  • Z. J. Ding, Y. Feng, Y. G. Zheng and Y.-Q. Zhang, "Granular Decision Fusion Systems for Effective Protein Methylation Prediction," accepted by 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2008), Sept. 15-17, Sun Valley, Idaho.

BOOK CHAPTERS

  • Z.J. Ding and Y.-Q. Zhang, "Fuzzy Logic," The Handbook of Technology Management, H. Bidgoli (Ed.), John Wiley & Sons, 2008.
  • Z. Ding, J. Yu, "Review of Recent Clustering Techniques," Machine Learning and Appication(in Chinese), J. Wang, Z.H. Zhou, A. Zhou (Ed.), Tsinghua University Press, February 2006.

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References
  • [1] Cheng, Y., Church, GM. (2000). "Biclustering of expression data". Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology: 93-103.
  • [2] Tanay, A. et al.(2002). "Discovering statistically significant biclusters in gene expression data". Bioinformatics, 18, 136-144.
  • [3] Ben-Dor, A., Chor, B., Karp, R. and Yakhini,Z. (2002). Discovering local structure in gene expression data: the order-preserving sub-matrix problem. Proceedings of the 6th International Conference on Computational Biology, ACM Press, New York, NY, USA, 49-57.
  • [4] Ihmels, J. et al. (2002). Revealing modular organization in the yeast transcriptional network. Nat. Genet., 31, 370¨C377.
  • [5] Murali, T.M. and Kasif, S. (2003). Extracting conserved gene expression motifs from gene expression data. Pac. Symp. Biocomput., 8, 77-88.
  • [6] Ahmad, W., and Khokhar, A. (2007). cHawk: An Efficient Biclustering Algorithm based on Bipartite Graph Crossing Minimization. 2007 VLDB.
  • [7] Fei, X., Lu, S., Pop, H. F., and Liang, L. R. (2007). GFBA: A genetic fuzzy biclustering algorithm for discovering value-coherent biclusters. In ISBRA 2007, L.N. in Bioinformatics 4463, 1-12.
  • [8] Cano, C., Adarve, L., Lopez, J., and Blanco, A. (2007). Possibilistic approach for biclustering microarray data. Computers in Biology and Medicine 37, 1426-1436.
  • [9] Banka, H., and Mitra, S. (2006). Evolutionary Biclustering of Gene Expressions. ACM Ubiquity. 7(42), 1-12.