Current Science

Open Access Open Access  Restricted Access Subscription Access

MSR-based algorithms for biclustering of microarray gene expression data


Affiliations
1 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632 014, India
2 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632 014, India
 

Biclustering plays a vital role in the analysis of gene expression data. The biclustering technique was proposed in the year 2000. For the past two decades, several biclustering methods and applications have been used to improve the quality to make sense of large microarray datasets. To find a highly correlated set of genes under specific conditions, usually one uses a measure or cost function. In such cases, it does not indicate that biclustering methods base their search on evaluation measures to identify the coherent biclusters. However, there is a substantial deviation between exploration in biclustering techniques and qualitative measure. Here, we present a review of different biclustering methods with the use of the most efficient measure called mean square residue within the search method. This review will guide researchers to fruitfully investigate their large microarray gene expression data and give meaningful, novel insights with greater efficiency
User
Notifications
Font Size

  • Achuthsankar, S. N., Computational biology and bioinformatics: a gentle overview. Commun. Comput. Soc. India, 2003, 1–12.

  • Liew, A. W. C., Yan, H. and Yang, M., Data mining for bioinformatics. In Bioinformatics Technologies (eds Chen, P. and Yi-Ping), Springer, Heidelberg, 2005, chapter 4, pp. 63–116.

  • Pérez-Suárez, A., Martínez-Trinidad, J. F. and Carrasco-Ochoa, J. A., A review of conceptual clustering algorithms. Artif. Intell. Rev., 2019, 52, 1267–1296.

  • Shannon, W., Culverhouse, R. and Duncan, J., Analyzing microarray data using cluster analyses. Pharmacogenomics, 2003, 4(1), 41–52.

  • Domany, E., Cluster analysis of gene expression data. J. Stat. Phys., 2003, 110(3–6), 1–18.

  • Risch, N. and Merikangas, K., The future of genetic studies of complex human diseases. Science, 1996, 273, 1516–1517.

  • Hartigan, J., Direct clustering of a data matrix. J. Am. Stat. Assoc., 1972, 67(337), 123–129.

  • Cheng, Y. and Church, G. M., Biclustering of expression data. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, Menlo Park, USA, 2000, pp. 93–103.

  • Yan, D. and Wang, J., Biclustering of gene expression data based on related genes and conditions extraction. Pattern Recogn., 2013, 46, 1170–1182.

  • Xie, J., Ma, A., Fennell, A., Ma, Q. and Zhao, J., It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data. Brief. Bioinform., 2013, 20(4), 1449–1464.

  • Madeira, S. C. and Oliveira, A. L., Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinform., 2004, 1(1), 24–45.

  • Pontes, B., Girldez, R. and Aguilar-Ruiz, J. S., Biclustering on expression data: a review. J. Biomed. Inform., 2015, 57, 163–180.

  • Yang, J., Wang, H., Wang, W. and Yu, P., Enhanced biclustering on expression data. In Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengineering, Bethesda, USA, 2003, pp. 321–327.

  • Zhang, Z., Teo, A., Ooi, B. C. and Tan, L., Mining deterministic biclusters in gene expression data. In Proceedings of International Conference on Fourth IEEE Symposium on Bioinformatics and Bio-engineering, Taichung, Taiwan, 2004, pp. 157–169.

  • Bleuler, S., Prelic, A. and Zitzler, E., An EA framework for biclustering of gene expression data. In Proceedings of Congress on Sixth Evolutionary Computation, Portland, OR, USA, 2004, pp. 166–173.

  • Chakraborty, A., Biclustering of gene expression data by simulated annealing. In Proceedings of Eighth International Conference on High-Performance Computing in Asia-Pacific Region, Beijing, 2005, pp. 78–90.

  • Chakraborty, H. M., Biclustering of gene expression data using genetic algorithm. In Proceedings of Computational Intelligence in Bio-informatics and Computational Biology, Toronto, Canada, 2006.

  • Divina, F. and Aguilar-Ruiz, J. S., Biclustering of expression data with evolutionary computation. IEEE Trans. Knowl. Data Eng., 2006, 18(5), 590–602.

  • Mitra, S. and Banka, H., Multi-objective evolutionary biclustering of gene expression data. Pattern Recogn., 2006, 39(12), 2464–2477.

  • Liu, F., Zhou, H. and Liu, J., Biclustering of gene expression data using EDA-GA hybrid. In Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 2006, pp. 1598–1602.

  • Divina, F. and Aguilar-Ruiz, J. S., A multi-objective approach to discover biclusters in microarray data. In Proceedings of the Ninth Annual Conference on Genetic and Evolutionary Computation, London, UK. 2007, pp. 385–392.

  • Cano, L., Adarve, J., López, A. and Blanco, Possibilistic approach for biclustering microarray data. Comput. Biol. Med., 2007, 37(10), 1426–1436.

  • Maulik, U. A., Mukhopadhyay, S., Bandyopadhyay, M. Q. and Zhang, X. Z., Multi objective fuzzy biclustering in microarray data: method and a new performance measure. In Proceedings of the IEEE Congress on Evolutionary Computation, Hong Kong, China, 2008, pp. 1536–1543.

  • Angiulli, F., Cesario, E. and Pizzuti, C., Random walk biclustering for microarray data. J. Inform. Sci., 2008, 178(6), 1479–1497.

  • Gremalschi, S. and Altun, G., Mean squared residue based biclustering algorithms. In Bioinformatics Research and Applications (eds Măndoiu, I., Sunderraman, R. and Zelikovsky, A.), ISBRA, Lecture Notes in Computer Science, Springer, Berlin, Germany, ISBRA, 2008, vol. 4983.

  • Liu, J., Li, Z. and Chen, Y., Microarray data biclustering with multi-objective immune optimization algorithm. In Proceedings of the Fifth International Conference on Natural Computation, Tianjin, China, 2009, pp. 564–580.

  • Dorigo, M. and Stützle T., Ant Colony Optimization, MIT Press, Cambridge, USA, 2004.

  • Liu, J., Li, Z. and Hu, X., Multi-objective ant colony optimization biclustering of microarray data. In Proceedings of the IEEE International Conference on Granular Computing, Nanchang, China, 2009, pp. 424–429.

  • Liu, J., Li, Z., Hu, X. and Chen, Y., Biclustering of microarray data with MOSPO based on crowding distance. Bioinformatics, 2009, 10(4), 1–9.

  • Coelho, G. P., De Franca, F. O. and Zuben, F. J. V., Multi-objective biclustering: when non-dominated solutions are not enough. J. Math. Modell. Algorithms, 2009, 8(2), 175–202.

  • Gallo, C. A., Carballido, J. A. and Ponzoni, I., Microarray biclustering: a novel memetic approach based on the PISA platform. In Proceedings of the Seventh European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Tübingen, Germany, 2009, pp. 44–55.

  • Liu, J., Li, Z. and Hu, X., Dynamic biclustering of microarray data by multi-objective immune optimization. BMC Genomics, 2011, 12(1), S11.

  • Joung, J. G., Kim, S. J., Shin, S. Y. and Zhang, B. T., A probabilistic coevolutionary biclustering algorithm for discovering coherent patterns in gene expression dataset. BMC Bioinformatics, 2012, 13(1), S12.

  • Huang, Q., Tao, D., Li, X. and Liew, A. W. C., Parallelized evolutionary learning for detection of biclusters in gene expression data. IEEE/ACM Trans. Comput. Biol. Bioinform., 2012, 9, 560–570.

  • Ayadi, W., Elloumi, M. and Hao, J. K., Pattern-driven neighborhood search for biclustering of microarray data. BMC Bioinformatics, 2012, 13(7), 1–15.

  • Liu, J., Li, Z., Hu, X. and Chen, Y., Multi-objective dynamic population shuffled frog leaping biclustering of microarray data. BMC Genomics, 2012, 13(3), 25–36.

  • Maatouk, O., Ayadi, W., Bouziri, H. and Duval, B., Evolutionary algorithm based on new crossover for the biclustering of gene expression data. In Proceedings of the Pattern Recognition in Bioinformatics, Stockholm, Sweden, 2014, pp. 48–59.

  • Li, Y., Tian, X., Jiao, L. and Zhang, X., Biclustering of gene expression data using particle swarm optimization integrated with pattern-driven local search. IEEE Congress Evolut. Comput., 2014, 29, 1367–1373.

  • Balamurugan, R., Natarajan, A. M. and Premalatha, K., Stellarmass black hole optimization for biclustering microarray gene expression data. App. Artif. Intell. Int. J., 2015, 29(4), 353–381.

  • Balamurugan, R., Natarajan, A. M. and Premalatha, K., Biclustering microarray gene expression data using modified Nelder–Mead method. Int. J. Inf. Commun. Technol., 2016, 9(1), 43–63.

  • Lagarias, J. C., Reeds, J. A., Wright, M. H. and Wright, P., Convergence properties of the Nelder–Mead simplex algorithm in low dimensions. SIAM J. Optimiz., 1998, 9(1), 112–147.

  • Zhu, X., Qiub, J. and Jianxin, M., A multi-objective biclustering algorithm based on fuzzy mathematics. Neurocomputing, 2017, 253, 177–182.

  • Balamurugan, R., Natarajan, A. M. and Premalatha, K., A new hybrid cuckoo search algorithm for biclustering of microarray geneexpression data. Appl. Artif. Intell., 2018, 32(7–8), 644–659.

  • Balamurugan, R., Natarajan, A. M. and Premalatha, K., Cuckoo search with mutation for biclustering of microarray gene expression data. Int. Arab J. Infor. Technol., 2017, 14(3), 300–306.

  • Huang, Q., Huang, X., Kong, Z., Li, X. and Tao, D., Bi-phase evolutionary searching for biclusters in gene expression data. IEEE Trans. Evolut. Comput., 2019, 23(5), 803–814.

  • Cui, Y., Zhang, R. and Gao, H., A novel biclustering of gene expression data based on hybrid BAFS–BSA algorithm. Multimedia Tools Appl., 2019; doi:org/10.1007/s11042-019-7656-7.

  • Azad, M. A. K., Rocha, A. M. A. C. and Fernandes, E. M. G. P., A simplified binary artificial fish swarm algorithm for 0–1 quadratic knapsack problems. J. Comput. Appl. Math., 2014, 259, 897–904.

  • Wang, S., Gutell, R. R. and Miranker, D. P., Biclustering as a method for RNA local multiple sequence alignment. Bioinformatics, 2007, 15(23), 3289–3296.

  • Chu, H.-M., Kong, X.-Z., Liu, J.-X., Wang, J., Yuan, S.-S. and Dai, L.-Y., Joint CC and Bimax: a biclustering method for single-cell RNA-seq data analysis. Bioinfor. Res. Appl., 2021, 18, 499–510.

  • Fang, Q., Su, D., Ng, W. and Feng, J., An effective biclustering-based framework for identifying cell subpopulations from scRNA-seq data. IEEE/ACM Trans. Comput. Biol. Bioinform., 2021, 18(6), 2249–2260.

  • Xie, J. et al., QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data. Bioinformatics, 2020, 36(4), 1143–1149.

  • Berriz, G. F., Beaver, J. E., Cenik, C., Tasan, M. and Roth, F. P., Next generation software for functional trend analysis. Bioinformatics, 2009, 25(22), 3043–3044.

  • Roy, S., Bhattacharyya, D. K. and Kalita, J. K., CoBi: pattern based co-regulated biclustering of gene expression data. Pattern Recog. Lett., 2013, 34(14), 1669–1678.

  • Balamurugan, R., Natarajan, A. M. and Premalatha, K., A modified harmony search method for biclustering microarray gene expression data. Int. J. Data Min. Bioinform., 2016, 16(4), 269–289.


Abstract Views: 247

PDF Views: 123




  • MSR-based algorithms for biclustering of microarray gene expression data

Abstract Views: 247  |  PDF Views: 123

Authors

R. Balamurugan
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632 014, India
S. P. Raja
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632 014, India

Abstract


Biclustering plays a vital role in the analysis of gene expression data. The biclustering technique was proposed in the year 2000. For the past two decades, several biclustering methods and applications have been used to improve the quality to make sense of large microarray datasets. To find a highly correlated set of genes under specific conditions, usually one uses a measure or cost function. In such cases, it does not indicate that biclustering methods base their search on evaluation measures to identify the coherent biclusters. However, there is a substantial deviation between exploration in biclustering techniques and qualitative measure. Here, we present a review of different biclustering methods with the use of the most efficient measure called mean square residue within the search method. This review will guide researchers to fruitfully investigate their large microarray gene expression data and give meaningful, novel insights with greater efficiency

References





DOI: https://doi.org/10.18520/cs%2Fv123%2Fi4%2F530-541