[1] Nicholas M Luscombe, Dov Greenbaum, and Mark Gerstein. What is bioinformatics? a proposed definition and overview of the field. Methods of information in medicine, 40(04):346–358, 2001.
[2] Lachlan Coff, Jeffrey Chan, Paul A Ramsland, and Andrew J Guy. Identifying glycan motifs using a novel subtree mining approach. BMC bioinformatics, 21(1):42, 2020.
[3] Martin Tompa, Nan Li, Timothy L Bailey, George M Church, Bart De Moor, Eleazar Eskin, Alexander V Favorov, Martin C Frith, Yutao Fu, W James Kent, et al. Assessing computational tools for the discovery of transcription factor binding sites. Nature biotechnology, 23(1): 137–144, 2005.
[4] Federico Zambelli, Graziano Pesole, and Giulio Pavesi. Motif discovery and transcription factor binding sites before and after the next-generation sequencing era. Briefings in bioinformatics, 14 (2):225–237, 2013.
[5] Jaime Davila, Sudha Balla, and Sanguthevar Rajasekaran. Fast and practical algorithms for planted (l, d) motif search. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(4):544–552, 2007.
[6] Yanju Zhang, Sha Yu, Ruopeng Xie, Jiahui Li, Andr´e Leier, Tatiana T Marquez-Lago, Tatsuya Akutsu, A Ian Smith, Zongyuan Ge, Jiawei Wang, et al. Pengaroo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins. Bioinformatics, 36(3):704–712, 2020.
[7] Mehmet Eren Ahsen, Robert Vogel, and Gustavo A Stolovitzky. R/py-summa: An r/python package for unsupervised ensemble learning for binary classification problems in bioinformatics. Journal of Computational Biology, 27(9):1337– 1340, 2020.
[8] Kanica Sachdev and Manoj K Gupta. Predicting drug target interactions using dimensionality reduction with ensemble learning. In Proceedings of ICRIC 2019, pages 79–89. Springer, 2020.
[9] Juho Kim, Seunghak Yu, and Sungroh Yoon. Ensemble algorithms for dna motif finding. In 2014 International Conference on Electronics, Information and Communications (ICEIC), pages 1–2. IEEE, 2014.
[10] Pengyi Yang, Yee Hwa Yang, Bing B Zhou, and Albert Y Zomaya. A review of ensemble methods in bioinformatics. Current Bioinformatics, 5(4): 296–308, 2010.
[11] Hsien-Da Huang, Jorng-Tzong Horng, Yi-Ming Sun, Ann-Ping Tsou, and Shir-Ly Huang. Identifying transcriptional regulatory sites in the human genome using an integrated system. Nucleic acids research, 32(6):1948–1956, 2004.
[12] Charles E Lawrence, Stephen F Altschul, Mark S Boguski, Jun S Liu, Andrew F Neuwald, and John C Wootton. Detecting subtle sequence signals: a gibbs sampling strategy for multiple alignment. science, 262(5131):208–214, 1993.
[13] Timothy L Bailey, Charles Elkan, et al. Fitting a mixture model by expectation maximization to discover motifs in bipolymers. 1994.
[14] Jason D Hughes, Preston W Estep, Saeed Tavazoie, and George M Church. Computational identification of cis-regulatory elements associated with groups of functionally related genes in saccharomyces cerevisiae. Journal of molecular biology, 296(5):1205–1214, 2000.
[15] Bertrand R Huber and Martha L Bulyk. Metaanalysis discovery of tissue-specific dna sequence motifs from mammalian gene expression data. BMC bioinformatics, 7(1):1–25, 2006.
[16] X Shirley Liu, Douglas L Brutlag, and Jun S Liu. An algorithm for finding protein–dna binding sites with applications to chromatinimmunoprecipitation microarray experiments. Nature biotechnology, 20(8):835–839, 2002. [17] Xiaole Liu, Douglas L Brutlag, and Jun S Liu. Bioprospector: discovering conserved dna motifs in upstream regulatory regions of co-expressed genes. In Biocomputing 2001, pages 127–138. World Scientific, 2000.
[18] Frederick P Roth, Jason D Hughes, Preston W Estep, and George M Church. Finding dna regulatory motifs within unaligned noncoding sequences clustered by whole-genome mrna quantitation. Nature biotechnology, 16(10):939–945, 1998.
[19] Jianjun Hu, Yifeng D Yang, and Daisuke Kihara. Emd: an ensemble algorithm for discovering regulatory motifs in dna sequences. BMC bioinformatics, 7(1):1–13, 2006.
[20] Katherine A Romer, Guy-Richard Kayombya, and Ernest Fraenkel. Webmotifs: automated discovery, filtering and scoring of dna sequence motifs using multiple programs and bayesian approaches. Nucleic acids research, 35(suppl 2): W217–W220, 2007.
[21] Bartek Wilczynski, Milosz Darzynkiewicz, and Jerzy Tiuryn. Memofinder: combining de novo motif prediction methods with a database of known motifs. Nature Precedings, pages 1–1, 2008.
[22] Edward Wijaya, Siu-Ming Yiu, Ngo Thanh Son, Rajaraman Kanagasabai, and Wing-Kin Sung. Motifvoter: a novel ensemble method for finegrained integration of generic motif finders. Bioinformatics, 24(20):2288–2295, 2008.
[23] Lakshmi Kuttippurathu, Michael Hsing, Yongchao Liu, Bertil Schmidt, Douglas L Maskell, Kyungjoon Lee, Aibin He, William T Pu, and Sek Won Kong. Completemotifs: Dna motif discovery platform for transcription factor binding experiments. Bioinformatics, 27(5): 715–717, 2011.
[24] Simon J van Heeringen and Gert Jan C Veenstra. Gimmemotifs: a de novo motif prediction pipeline for chip-sequencing experiments. Bioinformatics, 27(2):270–271, 2011.
[25] Thomas G Dietterich. Ensemble methods in machine learning. In International workshop on multiple classifier systems, pages 1–15. Springer, 2000.
[26] Pengyu Hong, X Shirley Liu, Qing Zhou, Xin Lu, Jun S Liu, and Wing H Wong. A boosting approach for motif modeling using chip-chip data. Bioinformatics, 21(11):2636–2643, 2005.
[27] Yue Fan, Mark A Kon, and Charles DeLisi. Ensemble machine methods for dna binding. In 2008 Seventh International Conference on Machine Learning and Applications, pages 709–716. IEEE, 2008.
[28] Victor X Jin, Jeff Apostolos, Naga Satya Venkateswara Ra Nagisetty, and Peggy J Farnham. W-chipmotifs: a web application tool for de novo motif discovery from chip-based highthroughput data. Bioinformatics, 25(23):3191– 3193, 2009.
[29] Jonathan M Carlson, Arijit Chakravarty, Charles E DeZiel, and Robert H Gross. Scope: a web server for practical de novo motif discovery. Nucleic acids research, 35(suppl 2):W259–W264, 2007.
[30] A Chakravarty, JM Carlson, RS Khetani, and RH Gross. A parameter-free algorithm for improved de novo identification of transcription factor binding sites. BMC Bioinformatics, 8:29, 2007.
[31] Wenxiu Ma, William S Noble, and Timothy L Bailey. Motif-based analysis of large nucleotide data sets using meme-chip. Nature protocols, 9 (6):1428–1450, 2014.
[32] Philip Machanick and Timothy L Bailey. Memechip: motif analysis of large dna datasets. Bioinformatics, 27(12):1696–1697, 2011.
[33] Dongsheng Che, Shane Jensen, Liming Cai, and Jun S Liu. Best: binding-site estimation suite of tools. Bioinformatics, 21(12):2909–2911, 2005.
[34] Christophe Liseron-Monfils, Tim Lewis, Daniel Ashlock, Paul D McNicholas, Fran¸cois Fauteux, Martina Str¨omvik, and Manish N Raizada. Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the maize development atlas. BMC plant biology, 13(1):1–17, 2013.
[35] Ngoc Tam L Tran and Chun-Hsi Huang. Modside: a motif discovery pipeline and similarity detector. BMC genomics, 19(1):1–9, 2018.
[36] K Till´an, M Leoncini, and M Montangero. Ce 3: Customizable and easily extensible ensemble tool for motif discovery. 2012.
[37] Timothy L Bailey, Charles Elkan, et al. Fitting a mixture model by expectation maximization to discover motifs in bipolymers. 1994.
[38] Giulio Pavesi, Giancarlo Mauri, and Graziano Pesole. An algorithm for finding signals of unknown length in dna sequences. Bioinformatics, 17(suppl 1):S207–S214, 2001.
[39] Xiaole Liu, Douglas L Brutlag, and Jun S Liu. Bioprospector: discovering conserved dna motifs in upstream regulatory regions of co-expressed genes. In Biocomputing 2001, pages 127–138. World Scientific, 2000.
[40] Pilib O Broin, Terry J Smith, and Aaron AJ ´ Golden. Alignment-free clustering of transcription factor binding motifs using a genetic-kmedoids approach. BMC bioinformatics, 16(1): 1–12, 2015.
[41] Shaun Mahony, Philip E Auron, and Panayiotis V Benos. Dna familial binding profiles made easy: comparison of various motif alignment and clustering strategies. PLoS Comput Biol, 3(3): e61, 2007.
[42] Ivan V Kulakovskiy, VA Boeva, Alexander V Favorov, and Vsevolod J Makeev. Deep and wide digging for binding motifs in chip-seq data. Bioinformatics, 26(20):2622–2623, 2010.
[43] Timothy L Bailey, Nadya Williams, Chris Misleh, and Wilfred W Li. Meme: discovering and analyzing dna and protein sequence motifs. Nucleic acids research, 34(suppl 2):W369–W373, 2006.
[44] Sebastian Luehr, Holger Hartmann, and Johannes S¨oding. The xxmotif web server for exhaustive, weight matrix-based motif discovery in nucleotide sequences. Nucleic acids research, 40 (W1):W104–W109, 2012.
[45] Morgane Thomas-Chollier, Carl Herrmann, Matthieu Defrance, Olivier Sand, Denis Thieffry, and Jacques van Helden. Rsat peak-motifs: motif analysis in full-size chip-seq datasets. Nucleic acids research, 40(4):e31–e31, 2012.