Biclustering Performance Evaluation of Cheng and Church Algorithm and Iterative Signature Algorithm

I Made Sumertajaya Sumertajaya, Wiwik Andriyani Lestari Ningsih, Asep Saefuddin, Embay Rohaeti

Abstract


Biclustering has been widely applied in recent years. Various algorithms have been developed to perform biclustering applied to various cases. However, only a few studies have evaluated the performance of bicluster algorithms. Therefore, this study evaluates the performance of biclustering algorithms, namely the Cheng and Church algorithm (CC algorithm) and the Iterative Signature Algorithm (ISA). Evaluation of the performance of the biclustering algorithm is carried out in the form of a comparative study of biclustering results in terms of membership, characteristics, distribution of biclustering results, and performance evaluation. The performance evaluation uses two evaluation functions: the intra-bicluster and the inter-bicluster. The results show that, from an intra-bicluster evaluation perspective, the optimal bicluster group of the CC algorithm produces bicluster quality which tends to be better than the ISA. The biclustering results between the two algorithms in inter-bicluster evaluation produce a deficient level of similarity (20-31 percent). This is indicated by the differences in the results of regional membership and the characteristics of the identifying variables. The biclustering results of the CC algorithm tend to be homogeneous and have local characteristics. Meanwhile, the results of biclustering ISA tend to be heterogeneous and have global characteristics. In addition, the results of biclustering ISA are also robust.


Keywords


Biclustering; Cheng and Church algorithm; Inter bicluster; Intra bicluster; Iterative signature algorithm.

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Ahmed, H. A., Mahanta, P., Bhattacharyya, D. K., & Kalita, J. K. (2014). Shifting-and-Scaling Correlation Based Biclustering Algorithm. IEEE/ACM Transactions On Computational Biology And Bioinformatics, 11(6), 1–14. https://doi.org/10.1109/TCBB.2014.2323054

Alzahrani, M., Kuwahara, H., Wang, W., & Gao, X. (2017). Gracob: A novel graph-based constant-column biclustering method for mining growth phenotype data. Bioinformatics, 33(16), 2523–2531. https://doi.org/10.1093/bioinformatics/btx199

Ardaneswari, G., Bustamam, A., & Siswantining, T. (2017). Implementation of parallel k-means algorithm for two-phase method biclustering in Carcinoma tumor gene expression data. AIP Conference Proceedings, 1825, 020004. https://doi.org/10.1063/1.4978973

Balamurugan, R., Natarajan, A. M., & Premalatha, K. (2015). Stellar-mass black hole optimization for biclustering microarray gene expression data. Applied Artificial Intelligence, 29(4), 353–381. https://doi.org/10.1080/08839514.2015.1016391

Ben Saber, H., & Elloumi, M. (2014). A Comparative Study of Clustering and Biclustering of Microarray Data. International Journal of Computer Science and Information Technology, 6(6), 93–111. https://doi.org/10.5121/ijcsit.2014.6607

Brewer, M. J., Butler, A., & Cooksley, S. L. (2016). The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods in Ecology and Evolution, 7(6), 679–692. https://doi.org/10.1111/2041-210X.12541

Brizuela, C. A., Luna-Taylor, J. E., Martinez-Perez, I., Guillen, H. A., Rodriguez, D. O., & Beltran-Verdugo, A. (2013). Improving an evolutionary multi-objective algorithm for the biclustering of gene expression data. 2013 IEEE Congress on Evolutionary Computation, CEC 2013, 221–228. https://doi.org/10.1109/CEC.2013.6557574

Castanho, E. N., Aidos, H., & Madeira, S. C. (2022). Biclustering fMRI time series: a comparative study. BMC Bioinformatics, 23(1), 1–30. https://doi.org/10.1186/s12859-022-04733-8

Chen, S., Zhang, L., Lu, L., Meng, J., & Liu, H. (2022). FBCwPlaid: A Functional Biclustering Analysis of Epi-Transcriptome Profiling Data Via a Weighted Plaid Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(3), 1640–1650. https://doi.org/10.1109/TCBB.2021.3049366

Cotelo, J. M., Ortega, F. J., Troyano, J. A., Enríquez, F., & Cruz, F. L. (2020). Known by who we follow: A biclustering application to community detection. IEEE Access, 8, 192218–192228. https://doi.org/10.1109/ACCESS.2020.3032015

Di Iorio, J., Chiaromonte, F., Cremona, M. A., & Cremona, M. A. (2020). On the bias of H-scores for comparing biclusters, and how to correct it. Bioinformatics, 36(9), 2955–2957. https://doi.org/10.1093/bioinformatics/btaa060

Divina, F., Vela, F. A. G., & Torres, M. G. (2019). Biclustering of smart building electric energy consumption data. Applied Sciences (Switzerland), 9(2), 222. https://doi.org/10.3390/app9020222

Ferraro, M. B., Giordani, P., & Vichi, M. (2021). A class of two-mode clustering algorithms in a fuzzy setting. Econometrics and Statistics, 18, 63–78. https://doi.org/10.1016/j.ecosta.2020.03.006

Flores, A., Tito, H., & Silva, C. (2019). Local Average of Nearest Neighbors: Univariate Time Series Imputation. International Journal of Advanced Computer Science and Applications, 10(8), 45–50. https://doi.org/10.14569/ijacsa.2019.0100807

Guo, H., Zhang, W., Ni, C., Cai, Z., Chen, S., & Huang, X. (2020). Heat map visualization for electrocardiogram data analysis. BMC Cardiovascular Disorders, 20(1), 1–8. https://doi.org/10.1186/s12872-020-01560-8

Henriques, R., Antunes, C., & Madeira, S. C. (2015). A structured view on pattern mining-based biclustering. Pattern Recognition, 48(12), 3941–3958. https://doi.org/10.1016/j.patcog.2015.06.018

Henriques, R., & Madeira, S. C. (2014). BicSPAM: Flexible biclustering using sequential patterns. BMC Bioinformatics, 15(1), 1–20. https://doi.org/10.1186/1471-2105-15-130

Henriques, R., & Madeira, S. C. (2018). BSig: evaluating the statistical significance of biclustering solutions. Data Mining and Knowledge Discovery, 32(1), 124–161. https://doi.org/10.1007/s10618-017-0521-2

Huang, Q., Chen, Y., Liu, L., Tao, D., & Li, X. (2020). On Combining Biclustering Mining and AdaBoost for Breast Tumor Classification. IEEE Transactions on Knowledge and Data Engineering, 32(4), 728–738. https://doi.org/10.1109/TKDE.2019.2891622

Kaban, P. A., Kurniawan, R., Caraka, R. E., Pardamean, B., Yuniarto, B., & Sukim. (2019). Biclustering method to capture the spatial pattern and to identify the causes of social vulnerability in Indonesia: A new recommendation for disaster mitigation policy. Procedia Computer Science, 157, 31–37. https://doi.org/10.1016/j.procs.2019.08.138

Kamranrad, R., Soltanzadeh, S., & Mardan, E. (2021). A Combined Data Mining Based-Bi Clustering and Order Preserved Sub-Matrices Algorithm for Set Covering Problem. Journal of Quality Engineering and Production Optimization, 6(2), 1–16. https://doi.org/10.22070/JQEPO.2021.5330.1144

Kavitha Sri, N., & Porkodi, R. (2019). An extensive survey on biclustering approaches and algorithms for gene expression data. International Journal of Scientific and Technology Research, 8(9), 2228–2236. https://ipb.link/ijstr-2277-8616

Khalili, B., Tomasoni, M., Mattei, M., Mallol Parera, R., Sonmez, R., Krefl, D., Rueedi, R., & Bergmann, S. (2019). Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites. Journal of Proteome Research, 18(9), 3360–3368. https://doi.org/10.1021/acs.jproteome.9b00295

Nations, U. (2011). EVI Indicators. ipb.link/un-evi

Ningsih, W. A. L., Sumertajaya, I. M., & Saefuddin, A. (2022a). Biclustering Application In Indonesian Economic And Pandemic Vulnerability. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 16(4), 1453–1464. https://doi.org/10.30598/barekengvol16iss4pp1453-1464

Ningsih, W. A. L., Sumertajaya, I. M., & Saefuddin, A. (2022b). Pattern Detection of Economic and Pandemic Vulnerability Index in Indonesia Using Bi-Cluster Analysis. JUITA : Jurnal Informatika, 10(2), 273. https://doi.org/10.30595/juita.v10i2.14940

Oghabian, A., Kilpinen, S., Hautaniemi, S., & Czeizler, E. (2014). Biclustering methods: Biological relevance and application in gene expression analysis. PLoS ONE, 9(3), e90801. https://doi.org/10.1371/journal.pone.0090801

Pang, C. (2022). Construction and Analysis of Macroeconomic Forecasting Model Based on Biclustering Algorithm. Journal of Mathematics, 2022, 7768949. https://doi.org/10.1155/2022/7768949

Patowary, P., Sarmah, R., & Bhattacharyya, D. K. (2020). Developing an effective biclustering technique using an enhanced proximity measure. In Network Modeling Analysis in Health Informatics and Bioinformatics (Vol. 9, Issue 1, p. 6). https://doi.org/10.1007/s13721-019-0211-7

Pontes, B., Giráldez, R., & Aguilar-Ruiz, J. S. (2015). Biclustering on expression data: A review. Journal of Biomedical Informatics, 57, 163–180. https://doi.org/10.1016/j.jbi.2015.06.028

Putri, C. A., Irfani, R., & Sartono, B. (2021). Recognizing poverty pattern in Central Java using Biclustering Analysis. Journal of Physics: Conference Series, 1863(1), 012068. https://doi.org/10.1088/1742-6596/1863/1/012068

Ramkumar, M., Basker, N., Pradeep, D., Prajapati, R., Yuvaraj, N., Arshath Raja, R., Suresh, C., Vignesh, R., Barakkath Nisha, U., Srihari, K., & Alene, A. (2022). Healthcare Biclustering-Based Prediction on Gene Expression Dataset. BioMed Research International, 2022(Special Issue), 1–7. https://doi.org/10.1155/2022/2263194

Saber, H. Ben, & Elloumi, M. (2015). A New Survey on Biclustering of MicroArray Data. International Journal for Computational Biology, 4(1), 21–37. https://doi.org/10.5121/csit.2014.41314

Sciences, N. I. of E. H. (2020). Details for PVI Maps. ipb.link/niehs

Siswantining, T., Bustamam, A., Puspa, S. D., Rustam, Z., & Zubedi, F. (2021). Biclustering of diabetic nephropathy and diabetic retinopathy microarray data using a similarity-based biclustering algorithm. International Journal of Bioinformatics Research and Applications, 17(4), 343–362. https://doi.org/10.1504/ijbra.2021.10041400

Wei, W. J., Shi, B., Guan, X., Ma, J. Y., Wang, Y. C., & Liu, J. (2019). Mapping theme trends and knowledge structures for human neural stem cells: a quantitative and co-word biclustering analysis for the 2013-2018 period. Neural Regeneration Research, 14(10), 1823–1832. https://doi.org/10.4103/1673-5374.257535

Xie, J., Ma, A., Fennell, A., Ma, Q., & Zhao, J. (2018). It is time to apply biclustering: A comprehensive review of biclustering applications in biological and biomedical data. Briefings in Bioinformatics, 20(4), 1449–1464. https://doi.org/10.1093/bib/bby014

Zhang, L., Chen, S., Ma, J., Liu, Z., & Liu, H. (2021). REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data. Frontiers in Genetics, 12(5), 1–10. https://doi.org/10.3389/fgene.2021.654820




DOI: https://doi.org/10.31764/jtam.v7i3.14778

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