{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:02:29Z","timestamp":1775325749390,"version":"3.50.1"},"reference-count":72,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Key Research Project of Colleges and Universities of Henan Province","award":["22A520013"],"award-info":[{"award-number":["22A520013"]}]},{"name":"Key Research Project of Colleges and Universities of Henan Province","award":["23B520004"],"award-info":[{"award-number":["23B520004"]}]},{"name":"Key Science and Technology Development Program of Henan Province","award":["202102210144"],"award-info":[{"award-number":["202102210144"]}]},{"name":"Training Program of Young Backbone Teachers in Colleges and Universities of Henan Province","award":["2019GGJS132"],"award-info":[{"award-number":["2019GGJS132"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Lysine glutarylation (Kglu) is a newly discovered post-translational modification of proteins with important roles in mitochondrial functions, oxidative damage, etc. The established biological experimental methods to identify glutarylation sites are often time-consuming and costly. Therefore, there is an urgent need to develop computational methods for efficient and accurate identification of glutarylation sites. Most of the existing computational methods only utilize handcrafted features to construct the prediction model and do not consider the positive impact of the pre-trained protein language model on the prediction performance. Based on this, we develop an ensemble deep-learning predictor Deepro-Glu that combines convolutional neural network and bidirectional long short-term memory network using the deep learning features and traditional handcrafted features to predict lysine glutaryation sites. The deep learning features are generated from the pre-trained protein language model called ProtBert, and the handcrafted features consist of sequence-based features, physicochemical property-based features and evolution information-based features. Furthermore, the attention mechanism is used to efficiently integrate the deep learning features and the handcrafted features by learning the appropriate attention weights. 10-fold cross-validation and independent tests demonstrate that Deepro-Glu achieves competitive or superior performance than the state-of-the-art methods. The source codes and data are publicly available at https:\/\/github.com\/xwanggroup\/Deepro-Glu.<\/jats:p>","DOI":"10.1093\/bib\/bbac631","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T04:54:02Z","timestamp":1674104042000},"source":"Crossref","is-referenced-by-count":29,"title":["Deepro-Glu: combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sites"],"prefix":"10.1093","volume":"24","author":[{"given":"Xiao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Zhengzhou University of Light Industry , No. 136, Science Avenue, 450002, Zhengzhou , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyuan","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Zhengzhou University of Light Industry , No. 136, Science Avenue, 450002, Zhengzhou , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Zhengzhou University of Light Industry , No. 136, Science Avenue, 450002, Zhengzhou , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Lin","sequence":"additional","affiliation":[{"name":"Instiute of Artificial Intelligence, Xiamen University , No.4221, Xiang\u2019an South Road, 361000, Xiamen , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"101336","DOI":"10.1016\/j.arr.2021.101336","article-title":"Post-translational modifications: regulators of neurodegenerative proteinopathies","volume":"68","author":"Gupta","year":"2021","journal-title":"Ageing Res Rev"},{"issue":"17","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"4051","DOI":"10.1007\/s00216-018-1021-y","article-title":"Proteomic approaches beyond expression profiling and PTM analysis","volume":"410","author":"Fu","year":"2018","journal-title":"Anal Bioanal Chem"},{"issue":"10","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1073\/pnas.1717664115","article-title":"Identification of the YEATS domain of GAS41 as a pH-dependent reader of histone succinylation","volume":"115","author":"Wang","year":"2018","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-05567-w","article-title":"Lysine benzoylation is a histone mark regulated by SIRT2","volume":"9","author":"Huang","year":"2018","journal-title":"Nat Commun"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"107553","DOI":"10.1016\/j.compbiolchem.2021.107553","article-title":"predForm-site: formylation site prediction by incorporating multiple features and resolving data imbalance","volume":"94","author":"Islam","year":"2021","journal-title":"Comput Biol Chem"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"bbab376","DOI":"10.1093\/bib\/bbab376","article-title":"STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction","volume":"23","author":"Basith","year":"2022","journal-title":"Brief Bioinform"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/TCBB.2020.3006144","article-title":"SSKM-Succ: a novel Succinylation sites prediction method incorporating K-means clustering with a new semi-supervised learning algorithm","volume":"19","author":"Ning","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"2","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"bbac037","DOI":"10.1093\/bib\/bbac037","article-title":"Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture","volume":"23","author":"Li","year":"2022","journal-title":"Brief Bioinform"},{"issue":"4","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.cmet.2014.03.014","article-title":"Lysine Glutarylation is a protein posttranslational modification regulated by SIRT5","volume":"19","author":"Tan","year":"2014","journal-title":"Cell Metab"},{"key":"2023032004263687600_","first-page":"9","article-title":"Roles of negatively charged histone lysine Acylations in regulating nucleosome structure and dynamics","author":"Jing","year":"2022","journal-title":"Front Mol Biosci"},{"issue":"4","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1021\/acs.jproteome.5b00917","article-title":"Proteome-wide lysine Glutarylation profiling of the mycobacterium tuberculosis H37Rv","volume":"15","author":"Xie","year":"2016","journal-title":"J Proteome Res"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ab.2018.04.005","article-title":"Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection","volume":"550","author":"Ju","year":"2018","journal-title":"Anal Biochem"},{"issue":"4","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TNB.2018.2848673","article-title":"iGlu-Lys: a predictor for lysine Glutarylation through amino acid pair order features","volume":"17","author":"Xu","year":"2018","journal-title":"IEEE Trans Nanobioscience"},{"issue":"3","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1039\/C9MO00028C","article-title":"RF-GlutarySite: a random forest based predictor for glutarylation sites","volume":"15","author":"AL-barakati","year":"2019","journal-title":"Molecular omics"},{"issue":"13","key":"2023032004263687600_","first-page":"13","article-title":"Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites","volume":"19","author":"Huang","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","DOI":"10.1109\/ICECTE48615.2019.9303533","article-title":"Improved performance of Lysine Glutarylation PTM using Peptide Evolutionary Features","volume-title":"Proceedings of 2019 3rd International Conference on Electrical, Computer &Telecommunication Engineering (ICECTE)","author":"Ahmad"},{"issue":"17","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","DOI":"10.1109\/ICECTE48615.2019.9303538","article-title":"Predicting lysine Glutarylation sites by combining multiple feature selection methods","volume-title":"Proceedings of 2019 3rd International Conference on Electrical, Computer &Telecommunication Engineering (ICECTE)","author":"Yeasmin"},{"issue":"9","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.3390\/genes11091023","article-title":"Accurately predicting Glutarylation sites using sequential bi-peptide-based evolutionary features","volume":"11","author":"Arafat","year":"2020","journal-title":"Genes"},{"issue":"3","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"204","DOI":"10.2174\/1389202921666200511072327","article-title":"Computational identification of lysine Glutarylation sites using positive-Unlabeled learning","volume":"21","author":"Ju","year":"2020","journal-title":"Curr Genomics"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","DOI":"10.1109\/TENSYMP50017.2020.9230866","article-title":"DeepGlut: A Deep Learning Framework for Prediction of Glutarylation Sites in Proteins","volume-title":"Proceedings of 2020 IEEE Region 10 Symposium (TENSYMP)","author":"Sen"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","DOI":"10.1109\/ICICT4SD50815.2021.9396995","article-title":"Improved Prediction of Glutarylation PTM Site using Evolutionary Features with LightGBM Resolving Data Imbalance Issue","volume-title":"Proceedings of 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)","author":"Shovan"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1021\/acs.jproteome.0c00314","article-title":"iGlu-AdaBoost: identification of lysine Glutarylation using the AdaBoost classifier","volume":"20","author":"Dou","year":"2021","journal-title":"J Proteome Res"},{"key":"2023032004263687600_","first-page":"1","article-title":"A novel method for identification of Glutarylation sites combining borderline-SMOTE with Tomek links technique in imbalanced data","author":"Ning","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"5","key":"2023032004263687600_","first-page":"2632","article-title":"iGluK-deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions","volume":"19","author":"Naseer","year":"2022","journal-title":"J Biomol Struct Dyn"},{"issue":"8","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.3390\/life12081213","article-title":"Deep neural network framework based on word embedding for protein Glutarylation sites prediction","volume":"12","author":"Liu","year":"2022","journal-title":"Life"},{"issue":"6","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1918","DOI":"10.1109\/TCBB.2019.2911677","article-title":"Amino acid encoding methods for protein sequences: a comprehensive review and assessment","volume":"17","author":"Jing","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023032004263687600_","first-page":"161","article-title":"Natural language processing","volume":"6","author":"Jain","year":"2018","journal-title":"Int J Comput Sci Eng"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/978-981-15-5971-6_83","article-title":"A survey on transfer learning","volume":"194","author":"Panigrahi","year":"2021","journal-title":"Intelligent and Cloud Computing"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3220-8","article-title":"Modeling aspects of the language of life through transfer-learning protein sequences","volume":"20","author":"Heinzinger","year":"2019","journal-title":"BMC Bioinform"},{"issue":"3","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"bbac142","DOI":"10.1093\/bib\/bbac142","article-title":"An analysis of protein language model embeddings for fold prediction","volume":"23","author":"Villegas-Morcillo","year":"2022","journal-title":"Brief Bioinform"},{"issue":"10","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: towards cracking the language of Life\u2019s code through self-supervised learning","volume":"44","author":"Elnaggar","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"D1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"UniProt: a worldwide hub of protein knowledge","volume":"47","author":"The UniProt Consortium","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ab.2007.07.006","article-title":"Recent progress in protein subcellular location prediction","volume":"370","author":"Chou","year":"2007","journal-title":"Anal Biochem"},{"key":"2023032004263687600_","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin"},{"issue":"7","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/s41592-019-0437-4","article-title":"Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold","volume":"16","author":"Steinegger","year":"2019","journal-title":"Nat Methods"},{"issue":"6","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1093\/bioinformatics\/btu739","article-title":"UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches","volume":"31","author":"Suzek","year":"2015","journal-title":"Bioinformatics"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCBB.2021.3114349","article-title":"predML-site: predicting multiple lysine PTM sites with optimal feature representation and data imbalance minimization","author":"Ahmed","year":"2021","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatic"},{"issue":"6","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"872","DOI":"10.3390\/biom11060872","article-title":"MDCAN-Lys: a model for predicting Succinylation sites based on multilane dense convolutional attention network","volume":"11","author":"Wang","year":"2021","journal-title":"Biomolecules"},{"issue":"10","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1089\/omi.2015.0095","article-title":"Harnessing computational biology for exact linear B-cell epitope prediction: a novel amino acid composition-based feature descriptor","volume":"19","author":"Saravanan","year":"2015","journal-title":"OMICS"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"104589","DOI":"10.1016\/j.chemolab.2022.104589","article-title":"ACP-2DCNN: deep learning-based model for improving prediction of anticancer peptides using two-dimensional convolutional neural network","volume":"226","author":"Ghulam","year":"2022","journal-title":"Chemom Intel Lab Syst"},{"issue":"6","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1093\/bib\/bby079","article-title":"Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework","volume":"20","author":"Zhang","year":"2019","journal-title":"Brief Bioinform"},{"issue":"8","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1038\/nbt0804-1035","article-title":"Where did the BLOSUM62 alignment score matrix come from?","volume":"22","author":"Eddy","year":"2004","journal-title":"Nat Biotechnol"},{"issue":"65","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"108867","DOI":"10.18632\/oncotarget.22335","article-title":"CIPPN: computational identification of protein pupylation sites by using neural network","volume":"8","author":"Bao","year":"2017","journal-title":"Oncotarget"},{"key":"2023032004263687600_","article-title":"Evaluation of the signature molecular descriptor with BLOSUM62 and an all-atom description for use in sequence alignment of","volume":"29","author":"Aichinger","year":"2015","journal-title":"Proteins"},{"key":"2023032004263687600_","article-title":"An extension of Wang\u2019s protein design model using Blosum62 substitution matrix","author":"Rahmani","journal-title":"bioRxiv preprint, bioRxiv: 2021.06.07.447415"},{"key":"2023032004263687600_","article-title":"HSEARCH: fast and accurate protein sequence motif search and clustering","author":"Chen","journal-title":"arXiv preprint, arXiv: 1701, 00452"},{"key":"2023032004263687600_","article-title":"Recurrent neural network regularization","author":"Zaremba","journal-title":"arXiv preprint, arXiv: 1409, 2329"},{"issue":"1","key":"2023032004263687600_","first-page":"1","article-title":"Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data","volume":"10","author":"Liu","year":"2019","journal-title":"Nat Commun"},{"issue":"25","key":"2023032004263687600_","first-page":"1","article-title":"Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter","volume":"20","author":"Lu","year":"2019","journal-title":"BMC Bioinform"},{"key":"2023032004263687600_","article-title":"Protein secondary structure prediction using cascaded convolutional and recurrent neural networks","author":"Li","journal-title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI\u201916)"},{"key":"2023032004263687600_","first-page":"1","article-title":"LSTMCNNsucc: a bidirectional LSTM and CNN-based deep learning method for predicting lysine Succinylation sites","volume":"2021","author":"Huang","year":"2021","journal-title":"Biomed Res Int"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys D: Nonlinear Phenom"},{"key":"2023032004263687600_","article-title":"An introduction to convolutional neural networks","author":"Oshea","journal-title":"arXiv preprint, arXiv: 1511, 08458"},{"issue":"2","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"184","DOI":"10.2174\/1574893616666210820095144","article-title":"iAnt: combination of convolutional neural network and random Forest models using PSSM and BERT features to identify antioxidant proteins","volume":"17","author":"Tran","year":"2022","journal-title":"Curr Bioinform"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern recognition"},{"issue":"3","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1109\/TIP.2017.2774041","article-title":"Object-part attention model for fine-grained image classification","volume":"27","author":"Peng","year":"2017","journal-title":"IEEE Trans Image Process"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/W18-3002","article-title":"Hierarchical Convolutional Attention Networks for Text Classification","volume-title":"Proceedings of The Third Workshop on Representation Learning for NLP","author":"Gao"},{"issue":"5","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.3233\/IDA-184311","article-title":"An attention-gated convolutional neural network for sentence classification","volume":"23","author":"Liu","year":"2019","journal-title":"Intelligent Data Analysis"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-022-03445-2","article-title":"PepNN: a deep attention model for the identification of peptide binding sites","volume":"5","author":"Abdin","year":"2022","journal-title":"Commun. Biol"},{"issue":"4","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1093\/bioinformatics\/btz694","article-title":"Identifying enhancer-promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism","volume":"36","author":"Hong","year":"2020","journal-title":"Bioinformatics"},{"issue":"1","key":"2023032004263687600_","first-page":"1","article-title":"Enhancing the interpretability of transcription factor binding site prediction using attention mechanism","volume":"10","author":"Park","year":"2020","journal-title":"Sci Rep"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"714","DOI":"10.3389\/fgene.2018.00714","article-title":"mlDEEPre: multi-functional enzyme function prediction with hierarchical multi-label deep learning","volume":"9","author":"Zou","year":"2019","journal-title":"Front Genet"},{"key":"2023032004263687600_","article-title":"Deep-sentiment: sentiment analysis using ensemble of CNN and bi-LSTM models","author":"Minaee","journal-title":"arXiv preprint, arXiv: 1904, 04206"},{"key":"2023032004263687600_","article-title":"Understanding dropout","author":"Baldi","journal-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2 (NIPS\u201913)"},{"key":"2023032004263687600_","article-title":"Does Adam optimizer keep close to the optimal point","author":"Bae","journal-title":"arXiv preprint, arXiv: 1911, 00289"},{"issue":"16","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"5710","DOI":"10.3390\/ijms21165710","article-title":"DeepPred-SubMito: a novel submitochondrial localization predictor based on Multi-Channel convolutional neural network and dataset balancing treatment","volume":"21","author":"Wang","year":"2020","journal-title":"Int J Mol Sci"},{"issue":"24","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"4668","DOI":"10.1093\/bioinformatics\/btab551","article-title":"PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information","volume":"37","author":"Yang","year":"2021","journal-title":"Bioinformatics"},{"key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1093\/bioinformatics\/btl151","article-title":"Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments","volume":"22","author":"Vacic","year":"2006","journal-title":"Bioinformatics"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"Chicco","year":"2020","journal-title":"BMC Genomics"},{"issue":"5","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.ygeno.2017.10.008","article-title":"iKcr-PseEns: identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier","volume":"110","author":"Qiu","year":"2018","journal-title":"Genomics"},{"issue":"1","key":"2023032004263687600_","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1016\/j.ygeno.2019.05.027","article-title":"Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou\u2019s 5-steps rule and general pseudo components","volume":"112","author":"Ju","year":"2020","journal-title":"Genomics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/2\/bbac631\/49560395\/bbac631.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/2\/bbac631\/49560395\/bbac631.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T18:43:44Z","timestamp":1701629024000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac631\/6991122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,18]]},"references-count":72,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,3,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac631","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,3]]},"published":{"date-parts":[[2023,1,18]]},"article-number":"bbac631"}}