{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T07:18:22Z","timestamp":1781680702744,"version":"3.54.5"},"reference-count":86,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"publisher","award":["2013\/07375-0"],"award-info":[{"award-number":["2013\/07375-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350\u20130.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.<\/jats:p>","DOI":"10.1093\/bib\/bbab434","type":"journal-article","created":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T22:50:08Z","timestamp":1633560608000},"source":"Crossref","is-referenced-by-count":78,"title":["MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors"],"prefix":"10.1093","volume":"23","author":[{"given":"Robson P","family":"Bonidia","sequence":"first","affiliation":[{"name":"Institute of Mathematics and Computer Sciences, University of S\u00e3o Paulo, S\u00e3o Carlos 13566-590, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas S","family":"Domingues","sequence":"additional","affiliation":[{"name":"Group of Genomics and Transcriptomes in Plants, Institute of Biosciences, S\u00e3o Paulo State University (UNESP), Rio Claro 13506-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danilo S","family":"Sanches","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Technology - Paran\u00e1, UTFPR, Corn\u00e9lio Proc\u00f3pio 86300-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9 C P L F","family":"de Carvalho","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Computer Sciences, University of S\u00e3o Paulo, S\u00e3o Carlos 13566-590, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"issue":"1","key":"2022011921335741900_ref1","article-title":"Bioinformatics: an overview and its applications","volume":"16","author":"da Silva Diniz","year":"2017","journal-title":"Genet Mol Res"},{"issue":"6","key":"2022011921335741900_ref2","doi-asserted-by":"crossref","first-page":"2116","DOI":"10.1093\/bib\/bby072","article-title":"Machine learning meets genome assembly","volume":"20","author":"de Souza","year":"2018","journal-title":"Brief Bioinform"},{"issue":"1","key":"2022011921335741900_ref3","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.jtbi.2010.12.024","article-title":"Some remarks on protein attribute prediction and pseudo amino acid composition","volume":"273","author":"Chou","year":"2011","journal-title":"J Theor Biol"},{"issue":"W1","key":"2022011921335741900_ref4","doi-asserted-by":"crossref","first-page":"W65","DOI":"10.1093\/nar\/gkv458","article-title":"Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences","volume":"43","author":"Liu","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2022011921335741900_ref5","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/978-3-030-33904-3_44","article-title":"Feature extraction of long non-coding rnas: A fourier and numerical mapping approach","volume-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications","author":"Bonidia","year":"2019"},{"issue":"20","key":"2022011921335741900_ref6","doi-asserted-by":"crossref","first-page":"e127","DOI":"10.1093\/nar\/gkz740","article-title":"BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches","volume":"47","author":"Liu","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"2022011921335741900_ref7","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1093\/bib\/bbz041","article-title":"iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data","volume":"21","author":"Chen","year":"2019","journal-title":"Brief Bioinform"},{"issue":"3","key":"2022011921335741900_ref8","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1093\/bioinformatics\/btz629","article-title":"Pengaroo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"2022011921335741900_ref9","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"},{"issue":"2","key":"2022011921335741900_ref10","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1093\/bib\/bbaa170","article-title":"A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19","volume":"22","author":"Naeem","year":"2020","journal-title":"Brief Bioinform"},{"issue":"1","key":"2022011921335741900_ref11","doi-asserted-by":"crossref","DOI":"10.3390\/proceedings2021074020","article-title":"Machine learning methods for covid-19 prediction using human genomic data","volume":"74","author":"Arslan","year":"2021","journal-title":"Proceedings"},{"issue":"4","key":"2022011921335741900_ref12","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":"2017","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022011921335741900_ref13","article-title":"Lncfinder: an integrated platform for long non-coding rna identification utilizing sequence intrinsic composition, structural information and physicochemical property","author":"Han","year":"2018","journal-title":"Brief Bioinform"},{"key":"2022011921335741900_ref14","doi-asserted-by":"crossref","first-page":"181683","DOI":"10.1109\/ACCESS.2020.3028039","article-title":"A novel decomposing model with evolutionary algorithms for feature selection in long non-coding rnas","volume":"8","author":"Bonidia","year":"2020","journal-title":"IEEE Access"},{"key":"2022011921335741900_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"},{"issue":"1","key":"2022011921335741900_ref16","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1093\/bioinformatics\/btu602","article-title":"PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions","volume":"31","author":"Chen","year":"2014","journal-title":"Bioinformatics"},{"key":"2022011921335741900_ref17","doi-asserted-by":"crossref","first-page":"W32","DOI":"10.1093\/nar\/gkl305","article-title":"PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence","volume":"34","author":"Li","year":"2006","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"2022011921335741900_ref18","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.ab.2007.10.012","article-title":"Pseaac: A flexible web server for generating various kinds of protein pseudo amino acid composition","volume":"373","author":"Shen","year":"2008","journal-title":"Anal Biochem"},{"issue":"7","key":"2022011921335741900_ref19","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1093\/bioinformatics\/btt072","article-title":"propy: a tool to generate various modes of Chou\u2019s PseAAC","volume":"29","author":"Cao","year":"2013","journal-title":"Bioinformatics"},{"issue":"1","key":"2022011921335741900_ref20","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1186\/1471-2105-15-93","article-title":"Spice: a web-based tool for sequence-based protein classification and exploration","volume":"15","author":"van den Berg","year":"2014","journal-title":"BMC bioinformatics"},{"issue":"11","key":"2022011921335741900_ref21","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1093\/bioinformatics\/btv042","article-title":"protr\/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences","volume":"31","author":"Xiao","year":"2015","journal-title":"Bioinformatics"},{"issue":"21","key":"2022011921335741900_ref22","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1093\/bioinformatics\/btv345","article-title":"ProFET: Feature engineering captures high-level protein functions","volume":"31","author":"Ofer","year":"2015","journal-title":"Bioinformatics"},{"issue":"8","key":"2022011921335741900_ref23","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1093\/bioinformatics\/btu820","article-title":"repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects","volume":"31","author":"Liu","year":"2014","journal-title":"Bioinformatics"},{"issue":"8","key":"2022011921335741900_ref24","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1093\/bioinformatics\/btv735","article-title":"DNAshapeR: an R\/Bioconductor package for DNA shape prediction and feature encoding","volume":"32","author":"Chiu","year":"2015","journal-title":"Bioinformatics"},{"issue":"1","key":"2022011921335741900_ref25","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s00438-015-1078-7","article-title":"reprna: a web server for generating various feature vectors of rna sequences","volume":"291","author":"Liu","year":"2016","journal-title":"Mol Genet Genomics"},{"issue":"4","key":"2022011921335741900_ref26","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1093\/bib\/bbx165","article-title":"Bioseq-analysis: a platform for dna, rna and protein sequence analysis based on machine learning approaches","volume":"20","author":"Liu","year":"2017","journal-title":"Brief Bioinform"},{"issue":"14","key":"2022011921335741900_ref27","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"},{"issue":"1","key":"2022011921335741900_ref28","article-title":"Pybiomed: a python library for various molecular representations of chemicals, proteins and dnas and their interactions","volume":"10","author":"Dong","year":"2018","journal-title":"J Chem"},{"issue":"22","key":"2022011921335741900_ref29","doi-asserted-by":"crossref","first-page":"4797","DOI":"10.1093\/bioinformatics\/btz432","article-title":"Seq2Feature: a comprehensive web-based feature extraction tool","volume":"35","author":"Nikam","year":"2019","journal-title":"Bioinformatics"},{"issue":"19","key":"2022011921335741900_ref30","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1093\/bioinformatics\/btz165","article-title":"PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences","volume":"35","author":"Muhammod","year":"2019","journal-title":"Bioinformatics"},{"key":"2022011921335741900_ref31","doi-asserted-by":"crossref","DOI":"10.12688\/f1000research.51143.1","article-title":"periodicdna: an r\/bioconductor package to investigate k-mer periodicity in dna","author":"Serizay","year":"2021","journal-title":"F1000Research"},{"key":"2022011921335741900_ref32","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkab122","article-title":"iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization","author":"Chen","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2022011921335741900_ref33","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1016\/j.nonrwa.2011.05.013","article-title":"Shannon, r\u00e9nyie and tsallis entropy analysis of dna using phase plane","volume":"12","author":"Machado","year":"2011","journal-title":"Nonlinear Analysis: Real World Applications"},{"issue":"3\u20134","key":"2022011921335741900_ref34","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.ygeno.2016.08.002","article-title":"Numerical encoding of dna sequences by chaos game representation with application in similarity comparison","volume":"108","author":"Hoang","year":"2016","journal-title":"Genomics"},{"issue":"3","key":"2022011921335741900_ref35","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0173288","article-title":"On dna numerical representations for genomic similarity computation","volume":"12","author":"Mendizabal-Ruiz","year":"2017","journal-title":"PloS one"},{"key":"2022011921335741900_ref36","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab011","article-title":"Feature extraction approaches for biological sequences: a comparative study of mathematical features","author":"Bonidia","year":"2021","journal-title":"Brief Bioinform"},{"issue":"8","key":"2022011921335741900_ref37","doi-asserted-by":"crossref","first-page":"4343","DOI":"10.1039\/C9CP06554G","article-title":"A review of mathematical representations of biomolecular data","volume":"22","author":"Nguyen","year":"2020","journal-title":"Phys Chem Chem Phys"},{"key":"2022011921335741900_ref38","volume-title":"Feature extraction: foundations and applications","author":"Guyon","year":"2008"},{"issue":"1","key":"2022011921335741900_ref39","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1111\/cbdd.13617","article-title":"Physicochemical n-grams tool: A tool for protein physicochemical descriptor generation via chou\u2019s 5-step rule","volume":"95","author":"Vishnoi","year":"2020","journal-title":"Chem Biol Drug Des"},{"key":"2022011921335741900_ref40","doi-asserted-by":"crossref","DOI":"10.1016\/j.csbj.2021.01.028","article-title":"Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring","author":"Ghannam","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"key":"2022011921335741900_ref41","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1145\/2382936.2383060","article-title":"Feature extraction in protein sequences classification: a new stability measure","volume-title":"Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine","author":"Saidi","year":"2012"},{"issue":"1","key":"2022011921335741900_ref42","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1093\/bib\/bbz177","article-title":"Design powerful predictor for mrna subcellular location prediction in homo sapiens","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"issue":"4","key":"2022011921335741900_ref43","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1080\/07391102.1994.10508031","article-title":"Z curves, an intutive tool for visualizing and analyzing the dna sequences","volume":"11","author":"Zhang","year":"1994","journal-title":"Journal of Biomolecular Structure and Dynamics"},{"issue":"4","key":"2022011921335741900_ref44","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/79.939833","article-title":"Genomic signal processing","volume":"18","author":"Anastassiou","year":"2001","journal-title":"IEEE Signal Processing Magazine"},{"issue":"2","key":"2022011921335741900_ref45","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1111\/j.1582-4934.2002.tb00196.x","article-title":"Conversion of nucleotides sequences into genomic signals","volume":"6","author":"Cristea","year":"2002","journal-title":"J Cell Mol Med"},{"issue":"25","key":"2022011921335741900_ref46","doi-asserted-by":"crossref","first-page":"3805","DOI":"10.1103\/PhysRevLett.68.3805","article-title":"Voss. Evolution of long-range fractal correlations and 1\/f noise in dna base sequences","volume":"68","year":"1992","journal-title":"Phys Rev Lett"},{"key":"2022011921335741900_ref47","first-page":"2004","article-title":"Autoregressive modeling and feature analysis of dna sequences","volume":"13\u201328","author":"Chakravarthy","year":"2004","journal-title":"EURASIP Journal on Applied Signal Processing"},{"issue":"6","key":"2022011921335741900_ref48","first-page":"197","article-title":"A coding measure scheme employing electron-ion interaction pseudopotential (eiip)","volume":"1","author":"Nair","year":"2006","journal-title":"Bioinformation"},{"key":"2022011921335741900_ref49","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/0-387-26288-1_9","article-title":"Analyzing protein sequences using signal analysis techniques","volume-title":"Computational and Statistical Approaches to Genomics","author":"Bloch","year":"2006"},{"issue":"3","key":"2022011921335741900_ref50","doi-asserted-by":"crossref","first-page":"191","DOI":"10.26599\/BDMA.2018.9020018","article-title":"Survey on encoding schemes for genomic data representation and feature learning\u2013from signal processing to machine learning","volume":"1","author":"Yu","year":"2018","journal-title":"Big Data Mining and Analytics"},{"key":"2022011921335741900_ref51","doi-asserted-by":"crossref","first-page":"669417","DOI":"10.1117\/12.732283","article-title":"Atcg nucleotide fluctuation of deinococcus radiodurans radiation genes","volume-title":"Instruments, Methods, and Missions for Astrobiology X","author":"Holden","year":"2007"},{"key":"2022011921335741900_ref52","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jtbi.2014.05.043","article-title":"A measure of dna sequence similarity by fourier transform with applications on hierarchical clustering","volume":"359","author":"Yin","year":"2014","journal-title":"J Theor Biol"},{"issue":"8","key":"2022011921335741900_ref53","first-page":"2163","article-title":"Jeffrey","volume":"18","author":"Joel","year":"1990","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"2022011921335741900_ref54","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1093\/bioinformatics\/17.5.429","article-title":"Analysis of genomic sequences by chaos game representation","volume":"17","author":"Almeida","year":"2001","journal-title":"Bioinformatics"},{"key":"2022011921335741900_ref55","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1038\/srep01033","article-title":"Applying shannon\u2019s information theory to bacterial and phage genomes and metagenomes","volume":"3","author":"Akhter","year":"2013","journal-title":"Sci Rep"},{"issue":"4","key":"2022011921335741900_ref56","doi-asserted-by":"crossref","first-page":"046105","DOI":"10.1103\/PhysRevE.63.046105","article-title":"Information theory based on nonadditive information content","volume":"63","author":"Yamano","year":"2001","journal-title":"Physical Review E"},{"issue":"3\u20134","key":"2022011921335741900_ref57","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/S0378-4371(98)00437-3","article-title":"The role of constraints within generalized nonextensive statistics","volume":"261","author":"Tsallis","year":"1998","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"1","key":"2022011921335741900_ref58","doi-asserted-by":"crossref","DOI":"10.1186\/1756-0381-4-10","article-title":"Using graph theory to analyze biological networks","volume":"4","author":"Pavlopoulos","year":"2011","journal-title":"BioData Min"},{"issue":"3","key":"2022011921335741900_ref59","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1093\/bib\/bbl022","article-title":"Graph-based methods for analysing networks in cell biology","volume":"7","author":"Aittokallio","year":"2006","journal-title":"Brief Bioinformatics"},{"key":"2022011921335741900_ref60","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gky462","article-title":"Basinet\u2013biological sequences network: a case study on coding and non-coding rnas identification","author":"Ito","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"2022011921335741900_ref61","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1093\/nar\/22.3.419","article-title":"Context effects on n 6-adenosine methylation sites in prolactin mrna","volume":"22","author":"Narayan","year":"1994","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"2022011921335741900_ref62","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1093\/bioinformatics\/btw663","article-title":"KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies","volume":"33","author":"Mapleson","year":"2016","journal-title":"Bioinformatics"},{"issue":"6","key":"2022011921335741900_ref63","doi-asserted-by":"crossref","first-page":"e74","DOI":"10.1093\/nar\/gkt006","article-title":"Cpat: Coding-potential assessment tool using an alignment-free logistic regression model","volume":"41","author":"Wang","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"10","key":"2022011921335741900_ref64","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s10822-020-00323-z","article-title":"Meta-ipvp: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation","volume":"34","author":"Charoenkwan","year":"2020","journal-title":"J Comput Aided Mol Des"},{"issue":"D1","key":"2022011921335741900_ref65","doi-asserted-by":"crossref","first-page":"D482","DOI":"10.1093\/nar\/gkw1065","article-title":"Virus Variation Resource \u2013 improved response to emergent viral outbreaks","volume":"45","author":"Hatcher","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2022011921335741900_ref66","doi-asserted-by":"crossref","first-page":"892","DOI":"10.3389\/fbioe.2020.00892","article-title":"Prediction of anticancer peptides using a low-dimensional feature model","volume":"8","author":"Li","year":"2020","journal-title":"Front Bioeng Biotechnol"},{"key":"2022011921335741900_ref67","doi-asserted-by":"crossref","first-page":"14244","DOI":"10.1109\/ACCESS.2020.2966592","article-title":"Identification of protein lysine crotonylation sites by a deep learning framework with convolutional neural networks","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Access"},{"issue":"3","key":"2022011921335741900_ref68","first-page":"1","article-title":"Plncrna-hdeep: plant long noncoding rna prediction using hybrid deep learning based on two encoding styles","volume":"22","author":"Meng","year":"2021","journal-title":"BMC bioinformatics"},{"key":"2022011921335741900_ref69","doi-asserted-by":"crossref","first-page":"107489","DOI":"10.1016\/j.compbiolchem.2021.107489","article-title":"Subfeat: Feature subspacing ensemble classifier for function prediction of dna, rna and protein sequences","volume":"92","author":"Haque","year":"2021","journal-title":"Comput Biol Chem"},{"issue":"1","key":"2022011921335741900_ref70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2180-5-58","article-title":"Non-classical protein secretion in bacteria","volume":"5","author":"Bendtsen","year":"2005","journal-title":"BMC Microbiol"},{"issue":"8","key":"2022011921335741900_ref71","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.1039\/C4MB00316K","article-title":"Identification of bacteriophage virion proteins by the anova feature selection and analysis","volume":"10","author":"Ding","year":"2014","journal-title":"Mol Biosyst"},{"issue":"2","key":"2022011921335741900_ref72","doi-asserted-by":"crossref","first-page":"353","DOI":"10.3390\/cells9020353","article-title":"Pvpred-scm: improved prediction and analysis of phage virion proteins using a scoring card method","volume":"9","author":"Charoenkwan","year":"2020","journal-title":"Cell"},{"issue":"4","key":"2022011921335741900_ref73","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0232391","article-title":"Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: Covid-19 case study","volume":"15","author":"Randhawa","year":"2020","journal-title":"Plos one"},{"issue":"suppl_2","key":"2022011921335741900_ref74","doi-asserted-by":"crossref","first-page":"W345","DOI":"10.1093\/nar\/gkm391","article-title":"Cpc: assess the protein-coding potential of transcripts using sequence features and support vector machine","volume":"35","author":"Kong","year":"2007","journal-title":"Nucleic Acids Res"},{"issue":"17","key":"2022011921335741900_ref75","doi-asserted-by":"crossref","first-page":"e166","DOI":"10.1093\/nar\/gkt646","article-title":"Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts","volume":"41","author":"Liang","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2022011921335741900_ref76","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1186\/1471-2105-15-311","article-title":"Plek: a tool for predicting long non-coding rnas and messenger rnas based on an improved k-mer scheme","volume":"15","author":"Li","year":"2014","journal-title":"BMC bioinformatics"},{"issue":"W1","key":"2022011921335741900_ref77","doi-asserted-by":"crossref","first-page":"W12","DOI":"10.1093\/nar\/gkx428","article-title":"Cpc2: a fast and accurate coding potential calculator based on sequence intrinsic features","volume":"45","author":"Kang","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"2022011921335741900_ref78","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief Bioinform"},{"key":"2022011921335741900_ref79","doi-asserted-by":"crossref","first-page":"214","DOI":"10.3389\/fgene.2019.00214","article-title":"Recent advances of deep learning in bioinformatics and computational biology","volume":"10","author":"Tang","year":"2019","journal-title":"Front Genet"},{"key":"2022011921335741900_ref80","author":"Chollet","year":"2015"},{"key":"2022011921335741900_ref81","first-page":"9","article-title":"lncrnanet: Long non-coding rna identification using deep learning","volume":"1","author":"Baek","year":"2018","journal-title":"Bioinformatics"},{"key":"2022011921335741900_ref82","article-title":"Lncadeep: An ab initio lncrna identification and functional annotation tool based on deep learning","author":"Cheng","year":"2018","journal-title":"Bioinformatics"},{"issue":"13","key":"2022011921335741900_ref83","doi-asserted-by":"crossref","first-page":"16895","DOI":"10.18632\/oncotarget.7815","article-title":"iacp: a sequence-based tool for identifying anticancer peptides","volume":"7","author":"Chen","year":"2016","journal-title":"Oncotarget"},{"issue":"1","key":"2022011921335741900_ref84","first-page":"1","article-title":"Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian","volume":"10","author":"Wang","year":"2020","journal-title":"Sci Rep"},{"issue":"4","key":"2022011921335741900_ref85","doi-asserted-by":"crossref","first-page":"e00439","DOI":"10.1128\/mSystems.00439-20","article-title":"Benchmarking bacterial promoter prediction tools: Potentialities and limitations","volume":"5","author":"Cassiano","year":"2020","journal-title":"Msystems"},{"issue":"2","key":"2022011921335741900_ref86","doi-asserted-by":"crossref","DOI":"10.3390\/ncrna7020029","article-title":"Post-transcriptional regulation through long non-coding rnas (lncrnas)","volume":"7","author":"Pisignano","year":"2021","journal-title":"Non-Coding RNA"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab434\/42229654\/bbab434.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab434\/42229654\/bbab434.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T05:38:51Z","timestamp":1699594731000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab434\/6423525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,8]]},"references-count":86,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab434","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,11,8]]},"article-number":"bbab434"}}