Abstract
The case study of plant intelligence inspired novel non-swarm intelligence (NSI) algorithms, namely Venus Flytrap Optimization and Bladder-Worts Suction, concentrated in this paper. These algorithms devised on the prey-hunting mechanisms of the Venus Flytrap (Dionaea Muscipula) and BladderWorts (Utricularia) plants, respectively. A comparative view of these algorithms is discussed. The main-support criterion is the major characteristic of these approaches. The benefits of this main-support criterion and their performances are evidenced with a case study of extracting the highly correlated maximal local patterns in gene expression data through biclustering. The NSI algorithms are proposed for biclustering gene expression data in this paper. The results are compared with existing optimization techniques like PSO and GA, and biclustering approaches like Cheng and Church, OPSM, BiMax, and Plaid approaches. This analysis evidenced the performance of NSI algorithms can yield optimal maximal local patterns with high correlation. Further, various real-time research applications of NSI approaches are also discussed.
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References
Gowri R, Rathipriya R (2015). Biclustering using Venus Flytrap Optimization. In: Computational intelligence in data mining, Proceedings of international conference on CIDM, 5–6 December 2015, advances in intelligent systems and computing series, 410, pp 199–207
Gowri R, Rathipriya R (2015) Venus Flytrap Optimization. In: Computational intelligence in data mining, proceedings of international conference on computational intelligence, cyber security and computational models (ICC3-2015), advances in intelligent systems and computing series. 412. Springer, pp. 519–531
Gowri R, Rathipriya R (2018) Non-swarm plant intelligence algorithm: BladderWorts Suction (BWS) Algorithm. ICCSDET. Kottayam, Kerala: IEEE
Cheng Y, Church G (2000) Biclustering of expression data. In: Proceedings of the eighth international conference on intelligent systems for molecular biology, pp. 93–103
Ben-Dor A, Chor B, Karp R, Yakhini Z (2002) Discovering local structure in gene expression data: the order-preserving submatrix problem. In: Proceedings of the sixth annual international conference on computational biology, pp 49–57
Prelic A, Bleuler S, Zimmermann P, Wille A, BuÈhlmann P, Gruissem W (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9):1122–1129
Lazzeroni L, Owen A (2002) Plaid models for gene expression data. Stat Sin 12(1):61–86
Kennedy J, Eberhart R (1995). Particle swarm optimization. In: Neural networks, proceedings IEEE international conference. 4. IEEE, pp 1942–1948
Goldberg DE (1989) Genetic algorithms in search, optimization & machine learning. Addison-Wesley, Boston
Sheng W, Tucke A, Liu X (2010) A niching genetic k-means algorithm and its applications to gene expression data. Soft Comput 14:9–19
Chira C, Sedano J, Villar JR, Camara M (2016) Gene clustering for time-series microarray with production outputs. Soft Comput 20:4301–4312
Kannan S, Devi R, Ramathilagam S, Hong T (2017) Effective fuzzy possibilistic c-means: an analyzing cancer medical database. Soft Comput 21:2835–2845
Chung F-L, Wang S, Deng Z, Shu C, Hu D (2006) Clustering analysis of gene expression data based on a semi-supervised visual clustering algorithm. Soft Comput 10:981–993
Musheer RA, Verma C, Srivastava N (2019) Novel machine learning approach for the classification of high-dimensional microarray data. Soft Comput 23:13409–13421
Rubio AS, Viveros AM, Luna GB, L´opez MP (2020) Towards BIMAX: binary inclusion-MAXimal parallel implementation of gene expression analysis. Computación y Sistemas 24(1):255–267
Gowri R, Rathipriya R (2017) Score based co-clustering for binary data. Int J Comput Intell Inf 7(2):105–111
Patowary P, Sarmah R, Bhattacharyya DK (2020) Developing an effective biclustering technique using an enhanced proximity measure. Netw Model Anal Health Inf Bioinf 9(6):1–17
Maran P, Shanthi S, Thenmozhi K, Hemalatha D, Nanthini K (2020) A novel deep learning method for identification of cancer genes from gene expression dataset. In: Machine learning and deep learning in real-time applications. IGI Global, pp 129–144
Filippone M, Masulli F, Rovetta S (2011) Simulated annealing for supervised gene selection. Soft Comput 15:1471–1482
Rathipriya R, Thangavel K (2012) A discrete artificial bees colony inspired biclustering algorithm. Int J Swarm Intell Res 3(1):30–42
Bagyamani J, Thangavel K, Rathipriya R (2013) Biclustering of gene expression data based on hybrid genetic algorithm. Int J Data Min Model Manag 5(4):333–350
Thangavel K, Bagyamani J, Rathipriya R (2012). Novel hybrid PSO-SA model for biclustering of expression data. In: International conference on communication technology and system design. Elsevier, pp 1048–1055
Song W, Ma W, Qiao Y (2017) Particle swarm optimization algorithm with environmental factors for clustering analysis. Soft Comput 21:283–293
Tsai C-W, Huang K-W, Yang C-S, Chiang M-C (2015) A fast particle swarm optimization for clustering. Soft Comput 19:321–338
Saha S, Kaushik K, Alok AK, Acharya S (2015) Multi-objective semi-supervised clustering of tissue samples for cancer diagnosis. Soft Comput 20:3381–3392
Rathipriya R, Thangavel K (2015) Hybrid swarm intelligence based biclustering approach for recommendation of web pages. IGI Global, Pennsylvania
Rathipriya R (2016) A novel evolutionary biclustering approach using MapReduce (EBC-MR). Int J Knowl Discov Bioinf 6(1):26–37
Rathipriya R (2016) Identification of optimal web page set based on web usage using biclustering optimization techniques. In: Design solutions for improving website quality and effectiveness. IGI Global, pp 141–161
Gowri R, Rathipriya R (2016) MRGABiT: MapReduce based genetic algorithm for biclustering time series data. In: IEEE ICACA. IEEE, pp 381–387
Acharya S, Saha S, Sahoo P (2018) Bi-clustering ofmicroarray data using a symmetry-based multi-objective framework. Soft Comput 23:5693–5714
Maâtouk O, Ayadi W, Bouziri H, Duval B (2018) Evolutionary biclustering algorithms: an experimental study on microarray data. Soft Comput 23:7671–7697
Bottarelli L, Bicego M, Denitto M, Pierro AD, Farinelli A (2018) Biclustering with a quantum annealer. Soft Comput 22:6247–6260
Narmadha N, Rathipriya R (2020) An optimized three-dimensional clustering for microarray data. In: Handbook of research on big data clustering and machine learning. IGI Global, pp 366–377
Hanafia S, Palubeckisb G, Glover F (2020) Bi-objective optimization of biclustering with binary data. Inf Sci 538:444–466
Ohnishi K, Fujiwara A, Koeppen M (2016) A non-swarm intelligence search algorithm based on the foraging behaviors of fruit flies. In: 2016 IEEE congress on evolutionary computation (CEC). Vancouver, Canada: IEEE
Nayyar A, Garg S, Gupta D, Khanna A (2018). Evolutionary computation: theory and algorithms. In: Advances in swarm intelligence for optimizing problems in computer science. Chapman and Hall/CRC, pp 1–26
Kumar S, Nayyar A, Paul A (2019) Swarm intelligence and evolutionary algorithms in healthcare and drug development. CRC Press, Boca Raton
Nayyar A, Nguyen N (2018) Introduction to swarm intelligence. In: Advances in swarm intelligence for optimizing problems in computer science, CRC
Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol
Voggenreiter O, Bleuler S (2012) Exact biclustering algorithm for the analysis of large gene expression data sets. BMC Bioinf
Thangavel K, Rathipriya R (2014) Mining correlated bicluster from web usage data using discrete firefly algorithm based biclustering approach. Int J Math Comput Nat Phys Eng 8(4):704–709
Saber HB, Elloumi M (2015) DNA microarray data analysis: a new survey on biclustering. Int J Comput Biol 4(1):21–37
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The first author acknowledges the UGC for financial support to her research under the UGC NET JRF (Student Id: 3384/(OBC)(NET JULY-2016)) Scheme.
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Gowri, R., Rathipriya, R. Non-swarm intelligence algorithms: a case study. Computing 103, 1815–1857 (2021). https://doi.org/10.1007/s00607-020-00870-1
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DOI: https://doi.org/10.1007/s00607-020-00870-1