{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:22:17Z","timestamp":1780496537514,"version":"3.54.1"},"reference-count":81,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T00:00:00Z","timestamp":1571616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Science Strength Promotion Programme of UESTC"},{"DOI":"10.13039\/501100001809","name":"National Nature Scientific Foundation of China","doi-asserted-by":"crossref","award":["61861036"],"award-info":[{"award-number":["61861036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Scientific Foundation of China","doi-asserted-by":"crossref","award":["31771471"],"award-info":[{"award-number":["31771471"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Scientific Foundation of China","doi-asserted-by":"crossref","award":["61772119"],"award-info":[{"award-number":["61772119"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Meiotic recombination is one of the most important driving forces of biological evolution, which is initiated by double-strand DNA breaks. Recombination has important roles in genome diversity and evolution. This review firstly provides a comprehensive survey of the 15 computational methods developed for identifying recombination hotspots in Saccharomyces cerevisiae. These computational methods were discussed and compared in terms of underlying algorithms, extracted features, predictive capability and practical utility. Subsequently, a more objective benchmark data set was constructed to develop a new predictor iRSpot-Pse6NC2.0 (http:\/\/lin-group.cn\/server\/iRSpot-Pse6NC2.0). To further demonstrate the generalization ability of these methods, we compared iRSpot-Pse6NC2.0 with existing methods on the chromosome XVI of S. cerevisiae. The results of the independent data set test demonstrated that the new predictor is superior to existing tools in the identification of recombination hotspots. The iRSpot-Pse6NC2.0 will become an important tool for identifying recombination hotspot.<\/jats:p>","DOI":"10.1093\/bib\/bbz123","type":"journal-article","created":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T11:33:08Z","timestamp":1566905588000},"page":"1568-1580","source":"Crossref","is-referenced-by-count":75,"title":["A comparison and assessment of computational method for identifying recombination hotspots in<i>Saccharomyces cerevisiae<\/i>"],"prefix":"10.1093","volume":"21","author":[{"given":"Hui","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wuritu","family":"Yang","sequence":"additional","affiliation":[{"name":"Development and Planning Department, Inner Mongolia University, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fu-Ying","family":"Dao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Lv","sequence":"additional","affiliation":[{"name":"Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Ding","sequence":"additional","affiliation":[{"name":"Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Lin","sequence":"additional","affiliation":[{"name":"Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,10,21]]},"reference":[{"key":"2021031107570262300_ref1","doi-asserted-by":"crossref","first-page":"11383","DOI":"10.1073\/pnas.97.21.11383","article-title":"Global mapping of meiotic recombination hotspots and coldspots in the yeast Saccharomyces cerevisiae","volume":"97","author":"Gerton","year":"2000","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2021031107570262300_ref2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/7050_2007_026","article-title":"Spo11 and the formation of DNA double-strand breaks in meiosis","volume":"2","author":"Keeney","year":"2008","journal-title":"Genome Dyn Stab"},{"key":"2021031107570262300_ref3","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1126\/science.1117196","article-title":"A fine-scale map of recombination rates and hotspots across the human genome","volume":"310","author":"Myers","year":"2005","journal-title":"Science"},{"key":"2021031107570262300_ref4","doi-asserted-by":"crossref","first-page":"5213","DOI":"10.1073\/pnas.94.10.5213","article-title":"Clustering of meiotic double-strand breaks on yeast chromosome III","volume":"94","author":"Baudat","year":"1997","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2021031107570262300_ref5","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0168-9525(02)02669-0","article-title":"Human SNP variability and mutation rate are higher in regions of high recombination","volume":"18","author":"Lercher","year":"2002","journal-title":"Trends Genet"},{"key":"2021031107570262300_ref6","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1093\/genetics\/159.2.907","article-title":"GC-content evolution in mammalian genomes: the biased gene conversion hypothesis","volume":"159","author":"Galtier","year":"2001","journal-title":"Genetics"},{"key":"2021031107570262300_ref7","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.tig.2011.11.002","article-title":"Direct and indirect consequences of meiotic recombination: implications for genome evolution","volume":"28","author":"Webster","year":"2012","journal-title":"Trends Genet"},{"key":"2021031107570262300_ref8","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1146\/annurev.genom.4.070802.110217","article-title":"Variation in human meiotic recombination","volume":"5","author":"Lynn","year":"2004","journal-title":"Annu Rev Genomics Hum Genet"},{"key":"2021031107570262300_ref9","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/nature07135","article-title":"High-resolution mapping of meiotic crossovers and non-crossovers in yeast","volume":"454","author":"Mancera","year":"2008","journal-title":"Nature"},{"key":"2021031107570262300_ref10","article-title":"Transcription factors-DNA interactions in rice: identification and verification","author":"Shen","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021031107570262300_ref11","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1016\/j.cell.2011.02.009","article-title":"A hierarchical combination of factors shapes the genome-wide topography of yeast meiotic recombination initiation","volume":"144","author":"Pan","year":"2011","journal-title":"Cell"},{"key":"2021031107570262300_ref12","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1186\/1471-2105-7-223","article-title":"Support vector machine for classification of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae based on codon composition","volume":"7","author":"Zhou","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2021031107570262300_ref13","doi-asserted-by":"crossref","first-page":"W47","DOI":"10.1093\/nar\/gkm217","article-title":"RF-DYMHC: detecting the yeast meiotic recombination hotspots and coldspots by random forest model using gapped dinucleotide composition features","volume":"35","author":"Jiang","year":"2007","journal-title":"Nucleic Acids Res"},{"key":"2021031107570262300_ref14","doi-asserted-by":"crossref","first-page":"33483","DOI":"10.1038\/srep33483","article-title":"iRSpot-DACC: a computational predictor for recombination hot\/cold spots identification based on dinucleotide-based auto-cross covariance","volume":"6","author":"Liu","year":"2016","journal-title":"Sci Rep"},{"key":"2021031107570262300_ref15","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.ab.2014.04.001","article-title":"PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition","volume":"456","author":"Chen","year":"2014","journal-title":"Anal Biochem"},{"key":"2021031107570262300_ref16","doi-asserted-by":"crossref","first-page":"e68","DOI":"10.1093\/nar\/gks1450","article-title":"iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition","volume":"41","author":"Chen","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2021031107570262300_ref17","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1186\/1471-2105-15-340","article-title":"Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM","volume":"15","author":"Li","year":"2014","journal-title":"BMC Bioinformatics"},{"key":"2021031107570262300_ref18","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.3390\/ijms15021746","article-title":"iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components","volume":"15","author":"Qiu","year":"2014","journal-title":"Int J Mol Sci"},{"key":"2021031107570262300_ref19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2174\/1574893608999140109121444","article-title":"Predicting recombination hotspots in yeast based on DNA sequence and chromatin structure","volume":"9","author":"Zhang","year":"2014","journal-title":"Curr Bioinforma"},{"key":"2021031107570262300_ref20","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jtbi.2015.06.030","article-title":"Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae","volume":"382","author":"Liu","year":"2015","journal-title":"J Theor Biol"},{"key":"2021031107570262300_ref21","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1039\/C6MB00374E","article-title":"Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots","volume":"12","author":"Dong","year":"2016","journal-title":"Mol BioSyst"},{"key":"2021031107570262300_ref22","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1093\/bioinformatics\/btw539","article-title":"iRSpot-EL: identify recombination spots with an ensemble learning approach","volume":"33","author":"Liu","year":"2017","journal-title":"Bioinformatics"},{"key":"2021031107570262300_ref23","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s00438-015-1108-5","article-title":"iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou\u2019s PseAAC to formulate DNA samples","volume":"291","author":"Kabir","year":"2016","journal-title":"Mol Gen Genomics"},{"key":"2021031107570262300_ref24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jtbi.2017.12.025","article-title":"iRSpot-ADPM: identify recombination spots by incorporating the associated dinucleotide product model into Chou\u2019s pseudo components","volume":"441","author":"Zhang","year":"2018","journal-title":"J Theor Biol"},{"key":"2021031107570262300_ref25","article-title":"Identification of recombination spots by incorporating dinucleotide property diversity information into Chou\u2019s pseudo components","author":"Zhang","year":"2018","journal-title":"Genomics"},{"key":"2021031107570262300_ref26","article-title":"iRSpot-SF prediction of recombination hotspots by incorporating sequence based features into Chou\u2019s Pseudo components","author":"Al Maruf","year":"2018","journal-title":"Genomics"},{"key":"2021031107570262300_ref27","doi-asserted-by":"crossref","first-page":"883","DOI":"10.7150\/ijbs.24616","article-title":"iRSpot-Pse6NC: identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC","volume":"14","author":"Yang","year":"2018","journal-title":"Int J Biol Sci"},{"key":"2021031107570262300_ref28","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"CD-HIT: accelerated for clustering the next-generation sequencing data","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"2021031107570262300_ref29","article-title":"Sequence clustering in bioinformatics: an empirical study","author":"Zou","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021031107570262300_ref30","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1093\/bioinformatics\/bty140","article-title":"iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"2021031107570262300_ref31","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btz358","article-title":"A computational tool for identifying D modification sites in RNA sequence","author":"Xu","year":"2019","journal-title":"Bioinformatics"},{"key":"2021031107570262300_ref32","article-title":"Function determinants of TET proteins: the arrangements of sequence motifs with specific codes","author":"Liu","year":"2018","journal-title":"Brief Bioinform"},{"key":"2021031107570262300_ref33","doi-asserted-by":"crossref","first-page":"286419","DOI":"10.1155\/2014\/286419","article-title":"iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels","volume":"2014","author":"Ding","year":"2014","journal-title":"Biomed Res Int"},{"key":"2021031107570262300_ref34","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1093\/bioinformatics\/btw564","article-title":"PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition","volume":"33","author":"Zuo","year":"2017","journal-title":"Bioinformatics"},{"key":"2021031107570262300_ref35","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.knosys.2018.10.007","article-title":"Predicting protein structural classes for low-similarity sequences by evaluating different features","volume":"163","author":"Zhu","year":"2019","journal-title":"Knowl-Based Syst"},{"key":"2021031107570262300_ref36","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1039\/C5MB00883B","article-title":"Identification of immunoglobulins using Chou\u2019s pseudo amino acid composition with feature selection technique","volume":"12","author":"Tang","year":"2016","journal-title":"Mol BioSyst"},{"key":"2021031107570262300_ref37","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1046\/j.1365-2958.1999.01370.x","article-title":"Identification of putative chromosomal origins of replication in Archaea","volume":"32","author":"Lopez","year":"1999","journal-title":"Mol Microbiol"},{"key":"2021031107570262300_ref38","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"2021031107570262300_ref39","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neucom.2014.12.123","article-title":"A novel features ranking metric with application to scalable visual and bioinformatics data classification","volume":"173","author":"Zou","year":"2016","journal-title":"Neurocomputing"},{"key":"2021031107570262300_ref40","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2174\/1574893613666181113131415","article-title":"A brief survey of machine learning methods in protein sub-Golgi localization","volume":"14","author":"Yang","year":"2019","journal-title":"Curr Bioinforma"},{"key":"2021031107570262300_ref41","doi-asserted-by":"crossref","first-page":"7794","DOI":"10.1109\/ACCESS.2018.2889809","article-title":"Transcriptome comparisons of multi-species identify differential genome activation of mammals embryogenesis","volume":"7","author":"Long","year":"2019","journal-title":"Ieee Access"},{"key":"2021031107570262300_ref42","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","article-title":"Support vector machines combined with feature selection for breast cancer diagnosis","volume":"36","author":"Akay","year":"2009","journal-title":"Expert Syst Appl"},{"key":"2021031107570262300_ref43","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach Learn"},{"key":"2021031107570262300_ref44","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ab.2014.06.022","article-title":"iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition","volume":"462","author":"Chen","year":"2014","journal-title":"Anal Biochem"},{"key":"2021031107570262300_ref45","first-page":"623149","article-title":"iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition","volume":"2014","author":"Chen","year":"2014","journal-title":"Biomed Res Int"},{"key":"2021031107570262300_ref46","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.ab.2013.05.024","article-title":"iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition","volume":"442","author":"Feng","year":"2013","journal-title":"Anal Biochem"},{"key":"2021031107570262300_ref47","doi-asserted-by":"crossref","first-page":"28169","DOI":"10.18632\/oncotarget.15963","article-title":"Sequence-based predictive modeling to identify cancerlectins","volume":"8","author":"Lai","year":"2017","journal-title":"Oncotarget"},{"key":"2021031107570262300_ref48","doi-asserted-by":"crossref","first-page":"5413903","DOI":"10.1155\/2016\/5413903","article-title":"Identification of secretory proteins in mycobacterium tuberculosis using pseudo amino acid composition","volume":"2016","author":"Yang","year":"2016","journal-title":"Biomed Res Int"},{"key":"2021031107570262300_ref49","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1039\/C4MB00645C","article-title":"Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition","volume":"11","author":"Zhu","year":"2015","journal-title":"Mol BioSyst"},{"key":"2021031107570262300_ref50","doi-asserted-by":"crossref","first-page":"476","DOI":"10.3389\/fmicb.2018.00476","article-title":"PVP-SVM: sequence-based prediction of phage virion proteins using a support vector machine","volume":"9","author":"Manavalan","year":"2018","journal-title":"Front Microbiol"},{"key":"2021031107570262300_ref51","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/72.857780","article-title":"The analysis of decomposition methods for support vector machines","volume":"11","author":"Chang","year":"2000","journal-title":"IEEE Trans Neural Netw"},{"key":"2021031107570262300_ref52","volume-title":"Advances in Kernel Methods: Support Vector Learning","author":"Sch","year":"1999"},{"key":"2021031107570262300_ref53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2021031107570262300_ref54","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/0-387-21529-8_16","article-title":"Random forests: finding quasars","author":"Breiman","year":"2003","journal-title":"Statistical Challenges In Astronomy"},{"key":"2021031107570262300_ref55","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1021\/acs.jproteome.9b00250","article-title":"Incorporating distance-based top-n-gram and random forest to identify electron transport proteins","volume":"18","author":"Ru","year":"2019","journal-title":"J Proteome Res"},{"key":"2021031107570262300_ref56","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random forest: a classification and regression tool for compound classification and QSAR modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J Chem Inf Comput Sci"},{"issue":"Nips 2017","key":"2021031107570262300_ref57","first-page":"30","article-title":"LightGBM: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2021031107570262300_ref58","doi-asserted-by":"crossref","first-page":"739","DOI":"10.2174\/092986608785133681","article-title":"Predicting subcellular localization of mycobacterial proteins by using Chou\u2019s pseudo amino acid composition","volume":"15","author":"Lin","year":"2008","journal-title":"Protein Pept Lett"},{"key":"2021031107570262300_ref59","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.jtbi.2008.02.004","article-title":"The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou\u2019s pseudo amino acid composition","volume":"252","author":"Lin","year":"2008","journal-title":"J Theor Biol"},{"key":"2021031107570262300_ref60","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jtbi.2011.10.004","article-title":"Sequence-dependent prediction of recombination hotspots in Saccharomyces cerevisiae","volume":"293","author":"Liu","year":"2012","journal-title":"J Theor Biol"},{"key":"2021031107570262300_ref61","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10994-007-5015-9","article-title":"Structured large margin machines: sensitive to data distributions","volume":"68","author":"Yeung","year":"2007","journal-title":"Mach Learn"},{"key":"2021031107570262300_ref62","doi-asserted-by":"crossref","DOI":"10.3390\/molecules22101732","article-title":"ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network","volume":"22","author":"Cao","year":"2017","journal-title":"Molecules"},{"key":"2021031107570262300_ref63","doi-asserted-by":"crossref","DOI":"10.1186\/s12859-016-1405-y","article-title":"DeepQA: improving the estimation of single protein model quality with deep belief networks","volume":"17","author":"Cao","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2021031107570262300_ref64","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1109\/TCBB.2017.2666141","article-title":"Identifying sigma70 promoters with novel pseudo nucleotide composition","volume":"16","author":"Lin","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2021031107570262300_ref65","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1089\/cmb.2018.0004","article-title":"iRNA-2OM: a sequence-based predictor for identifying 2\u2019-O-methylation sites in Homo sapiens","volume":"25","author":"Yang","year":"2018","journal-title":"J Comput Biol"},{"key":"2021031107570262300_ref66","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1093\/bib\/bby028","article-title":"iProt-sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites","volume":"20","author":"Song","year":"2018","journal-title":"Brief Bioinform"},{"key":"2021031107570262300_ref67","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1093\/bioinformatics\/btx670","article-title":"PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy","volume":"34","author":"Song","year":"2018","journal-title":"Bioinformatics"},{"key":"2021031107570262300_ref68","article-title":"Survey of machine learning techniques in drug discovery","author":"Stephenson","year":"2018","journal-title":"Curr Drug Metab"},{"key":"2021031107570262300_ref69","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.1021\/acs.jproteome.8b00148","article-title":"Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy","volume":"17","author":"Manavalan","year":"2018","journal-title":"J Proteome Res"},{"key":"2021031107570262300_ref70","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.3934\/mbe.2019123","article-title":"Identification of hormone binding proteins based on machine learning methods","volume":"16","author":"Tan","year":"2019","journal-title":"Math Biosci Eng"},{"key":"2021031107570262300_ref71","article-title":"Evaluation of different computational methods on 5-methylcytosine sites identification","author":"Lv","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021031107570262300_ref72","doi-asserted-by":"crossref","first-page":"957","DOI":"10.7150\/ijbs.24174","article-title":"HBPred: a tool to identify growth hormone-binding proteins","volume":"14","author":"Tang","year":"2018","journal-title":"Int J Biol Sci"},{"key":"2021031107570262300_ref73","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1186\/s12864-017-4338-6","article-title":"InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk","volume":"19","author":"Cheng","year":"2018","journal-title":"BMC Genomics"},{"key":"2021031107570262300_ref74","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1093\/bioinformatics\/bty002","article-title":"DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function","volume":"34","author":"Cheng","year":"2018","journal-title":"Bioinformatics"},{"key":"2021031107570262300_ref75","doi-asserted-by":"crossref","first-page":"D140","DOI":"10.1093\/nar\/gky1051","article-title":"LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse","volume":"47","author":"Cheng","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2021031107570262300_ref76","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1186\/s12859-018-2098-1","article-title":"Identifying diseases-related metabolites using random walk","volume":"19","author":"Hu","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2021031107570262300_ref77","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1126\/science.1182363","article-title":"Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic recombination","volume":"327","author":"Myers","year":"2010","journal-title":"Science"},{"key":"2021031107570262300_ref78","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/emboj.2008.257","article-title":"Histone H3 lysine 4 trimethylation marks meiotic recombination initiation sites","volume":"28","author":"Borde","year":"2009","journal-title":"EMBO J"},{"key":"2021031107570262300_ref79","doi-asserted-by":"crossref","first-page":"D209","DOI":"10.1093\/nar\/gkv940","article-title":"CircNet: a database of circular RNAs derived from transcriptome sequencing data","volume":"44","author":"Liu","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2021031107570262300_ref80","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.omtn.2019.05.028","article-title":"A computational predictor for predicting promoter","volume":"17","author":"Lai","year":"2019","journal-title":"Mol Ther Nucleic Acids"},{"key":"2021031107570262300_ref81","first-page":"1654623","article-title":"Identification of bacterial cell wall lyases via pseudo amino acid composition","volume":"2016","author":"Chen","year":"2016","journal-title":"Biomed Res Int"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/21\/5\/1568\/36526965\/bbz123.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/21\/5\/1568\/36526965\/bbz123.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T14:36:15Z","timestamp":1664202975000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/21\/5\/1568\/5593799"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,21]]},"references-count":81,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,10,21]]},"published-print":{"date-parts":[[2020,9,25]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbz123","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,9]]},"published":{"date-parts":[[2019,10,21]]}}}