{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:14:55Z","timestamp":1760242495158,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,9,21]],"date-time":"2017-09-21T00:00:00Z","timestamp":1505952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The computational discovery of DNA motifs is one of the most important problems in molecular biology and computational biology, and it has not yet been resolved in an efficient manner. With previous research, we have solved the single-objective motif discovery problem (MDP) based on biogeography-based optimization (BBO) and gained excellent results. In this study, we apply multi-objective biogeography-based optimization algorithm to the multi-objective motif discovery problem, which refers to discovery of novel transcription factor binding sites in DNA sequences. For this, we propose an improved multi-objective hybridization of adaptive Biogeography-Based Optimization with differential evolution (DE) approach, namely MHABBO, to predict motifs from DNA sequences. In the MHABBO algorithm, the fitness function based on distribution information among the habitat individuals and the Pareto dominance relation are redefined. Based on the relationship between the cost of fitness function and average cost in each generation, the MHABBO algorithm adaptively changes the migration probability and mutation probability. Additionally, the mutation procedure that combines with the DE algorithm is modified. And the migration operators based on the number of iterations are improved to meet motif discovery requirements. Furthermore, the immigration and emigration rates based on a cosine curve are modified. It can therefore generate promising candidate solutions. Statistical comparisons with DEPT and MOGAMOD approaches on three commonly used datasets are provided, which demonstrate the validity and effectiveness of the MHABBO algorithm. Compared with some typical existing approaches, the MHABBO algorithm performs better in terms of the quality of the final solutions.<\/jats:p>","DOI":"10.3390\/info8040115","type":"journal-article","created":{"date-parts":[[2017,9,21]],"date-time":"2017-09-21T12:17:40Z","timestamp":1505996260000},"page":"115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting DNA Motifs by Using Multi-Objective Hybrid Adaptive Biogeography-Based Optimization"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8627-2028","authenticated-orcid":false,"given":"Siling","family":"Feng","sequence":"first","affiliation":[{"name":"College of Information Science & Technology, Hainan University, No. 58 Renmin Avenue, Hai\u2019kou 570228, China"},{"name":"State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, No. 58 Renmin Avenue, Hai\u2019kou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqiang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Science & Technology, Hainan University, No. 58 Renmin Avenue, Hai\u2019kou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengxing","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Science & Technology, Hainan University, No. 58 Renmin Avenue, Hai\u2019kou 570228, China"},{"name":"State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, No. 58 Renmin Avenue, Hai\u2019kou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1038\/nbt0406-423","article-title":"What are DNA sequence motifs","volume":"24","author":"Patrik","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lones, M.A., Yo, Y., and Tyrrell, A.M. (2005, January 25\u201329). The Evolutionary Computation Approach to Motif Discovery in Biological Sequences. Proceedings of the 7th Annual Workshop on Genetic and Evolutionary Computation (GECCO\u2019052005), Washington, DC, USA.","DOI":"10.1145\/1102256.1102258"},{"key":"ref_3","first-page":"2741","article-title":"A CoEvolutionary Algorithm Based on Elitism and Gravitational Evolution Strategies","volume":"7","author":"Lou","year":"2012","journal-title":"J. Comput. Inf. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Che, D., Song, Y., and Rashedd, K. (2005, January 25\u201329). MDGA: Motif discovery using a genetic algorithm. Proceedings of the 2005 Conference on Genetic and Evolutionary Computation (GECCO 2005), Washington, DC, USA.","DOI":"10.1145\/1068009.1068080"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shao, L., and Chen, Y. (2009, January 1\u20134). Bacterial Foraging Optimization Algorithm Integrating Tabu Search for Motif Discovery. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2009), Washington, DC, USA.","DOI":"10.1109\/BIBM.2009.12"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shao, L., Chen, Y., and Abraham, A. (2009, January 4\u20137). Motif Discovery using Evolutionary Algorithms. Proceedings of the International Conference of Soft Computing and Pattern Recognition (SOCPAR 2009), Malacca, Malaysia.","DOI":"10.1109\/SoCPaR.2009.88"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1109\/TSMCC.2011.2172939","article-title":"Predicting DNA Motifs by Using Evolutionary Multiobjective Optimization","volume":"42","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-\u00c1lvarez, D.L., Vega-Rodr\u00edguez, M.A., Pulido, J.A.G., and S\u00e1nchez-P\u00e9rez, J.M. (2011, January 27\u201329). Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm. Proceedings of the 9th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO), Torino, Italy.","DOI":"10.1007\/978-3-642-20389-3_9"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1016\/j.eswa.2007.11.008","article-title":"MOGAMOD: Multi-objective genetic algorithm for motif discovery","volume":"36","author":"Kaya","year":"2009","journal-title":"Int. J. Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","article-title":"An evolutionary many-objective optimization algorithm using reference-point-based non-dominated sorting approach. Part I: Solving problems with box constraints","volume":"18","author":"Deb","year":"2014","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","article-title":"MOEA\/D: A multiobjective evolutionary algorithm based on decomposition","volume":"11","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","article-title":"Biogeography-based optimization","volume":"12","author":"Simon","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_13","first-page":"6999","article-title":"Biogeography-Based Optimization for Motif Discovery Problem","volume":"9","author":"Feng","year":"2013","journal-title":"J. Comput. Inf. Syst."},{"key":"ref_14","first-page":"3343","article-title":"Hybridizing Biogeography-Based Optimization with Differential Evolution for Motif Discovery Problem","volume":"7","author":"Feng","year":"2013","journal-title":"ICIC Express Lett."},{"key":"ref_15","first-page":"233","article-title":"Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Motif Discovery Problem","volume":"162","author":"Feng","year":"2014","journal-title":"Sens. Transducers"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.cie.2014.01.001","article-title":"A Pareto biogeography-based optimisation for multi objective two-sided assembly line sequencing problems with a learning effect","volume":"69","author":"Chutima","year":"2014","journal-title":"Comput. Ind. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/TMAG.2011.2174205","article-title":"Multi objective biogeography-based optimization based on predator-prey approach","volume":"48","author":"Coelho","year":"2012","journal-title":"IEEE Trans. Magn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.engappai.2015.05.009","article-title":"Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling","volume":"44","author":"Ma","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.comnet.2015.08.037","article-title":"A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization","volume":"91","author":"Goudos","year":"2015","journal-title":"Comput. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Feng, S., Yang, Z., and Huang, M. (2017). Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems. Information, 8.","DOI":"10.3390\/info8030083"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.asoc.2017.04.018","article-title":"Hybrid Artificial Bee Colony algorithm with Differential Evolution","volume":"58","author":"Jadon","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1111\/mice.12124","article-title":"Coupling response surface and differential evolution for parameter identification problems","volume":"30","author":"Loris","year":"2015","journal-title":"Comput.-Aided Civil Infrastruct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.swevo.2015.04.001","article-title":"Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem","volume":"23","author":"Trivedi","year":"2015","journal-title":"Swarm Evol. Comput."},{"key":"ref_24","first-page":"225","article-title":"Multi-objective optimization algorithm based on biogeography with chaos","volume":"7","author":"Wang","year":"2014","journal-title":"Int. J. Hybrid Inf. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1016\/j.engappai.2011.04.012","article-title":"Analysis of migration models of biogeography-based optimization using Markov theory","volume":"24","author":"Ma","year":"2011","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.engappai.2010.08.005","article-title":"Blended biogeography-based optimization for constrained optimization","volume":"24","author":"Ma","year":"2011","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1016\/j.cor.2010.11.004","article-title":"Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO)","volume":"38","author":"Chatterjee","year":"2011","journal-title":"Comput. Oper. Res."},{"key":"ref_28","unstructured":"Cai, Z. (2011, January 12\u201314). A Novel Hybrid Biogeography-based with Differential Mutation. Proceedings of the International Conference on Electronic & Mechanical Engineering and Information Technology, Harbin, China."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00500-010-0591-1","article-title":"DE\/BBO: A hybrid differential evolution with biogeography-based optimization for global numerical optimization","volume":"15","author":"Gong","year":"2010","journal-title":"Soft Comput."},{"key":"ref_30","first-page":"179","article-title":"Multi-objective optimization based on hybrid biogeography-based optimization","volume":"36","author":"Bi","year":"2014","journal-title":"Syst. Eng. Electron."},{"key":"ref_31","unstructured":"Zitzler, E., Laumanns, M., and Thiele, L. (2001, January 19\u201321). SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. Proceedings of the Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1093\/nar\/24.1.238","article-title":"TRANSFAC: A database on transcription factors and their DNA binding sites","volume":"24","author":"Wingender","year":"1996","journal-title":"Nucleic Acids Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/nbt1053","article-title":"Assessing computational tools for the discovery of transcription factor binding sites","volume":"23","author":"Tompa","year":"2005","journal-title":"Nat. Biotechnol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1006\/jmbi.2000.3519","article-title":"Computational identification of cis-regulatory elements associated with functionally coherent groups of genes in Saccharomyces cerevisiae","volume":"296","author":"Hughes","year":"2000","journal-title":"J. Mol. Biol."},{"key":"ref_35","unstructured":"Bailey, T.L., and Elkan, C. (1995, January 16\u201319). The value of prior knowledge in discovering motifs with MEME. Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology, Cambridge, UK."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"W199","DOI":"10.1093\/nar\/gkh465","article-title":"Weeder Web: Discovery of transcription factor binding sites in a set of sequences from co-regulated genes","volume":"32","author":"Pavesi","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"ref_37","unstructured":"(2017, September 19). WebLogo 3. Available online: http:\/\/weblogo.threeplusone.com\/create.cgi."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCBB.2009.25","article-title":"A Cluster Refinement Algorithm for Motif Discovery","volume":"7","author":"Li","year":"2010","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1093\/nar\/gkg618","article-title":"YMF: A program for discovery of novel transcription factor binding sites by statistical overrepresentation","volume":"31","author":"Sinha","year":"2003","journal-title":"Nucleic Acids Res."},{"key":"ref_40","unstructured":"Favorov, A.V., Gelfand, M.S., Gerasimova, A.V., Mironov, A.A., and Makeev, V.J. (2004, January 25\u201330). Gibbs sampler for identification of symmetrically structured, spaced DNA motifs with improved estimation of the signal length and its validation on the ArcA binding sites. Proceedings of the Fourth International Conference on Bioinformatics of Genome Regulation and Structure (BGRS 2004), Novosibirsk, Russia."},{"key":"ref_41","first-page":"191","article-title":"Rare events and conditional events on random strings.","volume":"6","author":"Denise","year":"2004","journal-title":"Discrete Math. Theor. Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1093\/bioinformatics\/17.12.1113","article-title":"A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling","volume":"17","author":"Thijs","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"S354","DOI":"10.1093\/bioinformatics\/18.suppl_1.S354","article-title":"Finding composite regulatory patterns in DNA sequences","volume":"18","author":"Eskin","year":"2002","journal-title":"Bioinformatics"},{"key":"ref_44","unstructured":"Altman, R., Dunker, A.K., Hunter, L., and Klein, T.E. (2000). ANN-Spec: A method for discovering transcription factor binding sites with improved specificity. Pacific Symposium on Biocomputing, Stanford University."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1126\/science.1102216","article-title":"Environmentally induced foregut remodeling by PHA-4\/FoxA and DAF-12\/NHR","volume":"305","author":"Ao","year":"2004","journal-title":"Science"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ye, T., Yang, Z., and Feng, S. (2017). Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints. Algorithms, 10.","DOI":"10.3390\/a10030100"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1108\/S0276-8976(2013)0000016007","article-title":"Survey of multi-objective portfolio optimization by linear and mixed integer programming","volume":"Volume 16","author":"Lawrence","year":"2013","journal-title":"Applications of Management Science"},{"key":"ref_48","unstructured":"Dash, G.H., and Thomaidis, N. (2013). A Review of Multi-Criteria Portfolio Optimization by Mathematical Programming. Recent Advances in Computational Finance, Nova Science Publishers."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/4\/115\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:45:35Z","timestamp":1760208335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/4\/115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,21]]},"references-count":48,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["info8040115"],"URL":"https:\/\/doi.org\/10.3390\/info8040115","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2017,9,21]]}}}