2024
Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms
Tan A, Gonçalves R, Yuan W, Brat G, Gentleman R, Kohane I, Masino A, Makoudjou A, Albayrak A, Gutiérrez-Sacristán A, Zambelli A, Malovini A, Carmona A, Hoffmann A, Gramfort A, Geva A, Blanco-Martínez A, Tan A, Terriza-Torres A, Spiridou A, Prunotto A, South A, Vallejos A, Atz A, Burgun A, Alloni A, Cattelan A, Jannot A, Neuraz A, Bellasi A, Maram A, Dagliati A, Sandrin A, Serret-Larmande A, Mensch A, Pfaff A, Batugo A, Krishnamurthy A, Adam A, Dionne A, Devkota B, Moal B, He B, Beaulieu-Jones B, Beaulieu-Jones B, Ostasiewski B, Aronow B, Tan B, Tan B, Torti C, Sáez C, Neto C, Sonday C, Caucheteux C, Mao C, Zucco C, Daniel C, Haverkamp C, Hong C, Bonzel C, Moraleda C, Leprovost D, Key D, Zöller D, Pillion D, Mowery D, Amendola D, Henderson D, Hanauer D, Taylor D, Wassermann D, Hazard D, Kraska D, Mazzotti D, Silvio D, Bell D, Murad D, Salamanca E, Bucholz E, Getzen E, Pfaff E, Schriver E, Toh E, Parimbelli E, Trecarichi E, Ashraf F, Vidorreta F, Bourgeois F, Sperotto F, Angoulvant F, Brat G, Varoquaux G, Omenn G, Agapito G, Albi G, Weber G, Verdy G, Lemaitre G, Roig-Domínguez G, Prokosch H, Zhang H, Estiri H, Krantz I, Kohane I, Honerlaw J, Cruz-Rojo J, Norman J, Balshi J, Cimino J, Aaron J, Santos J, Newburger J, Zahner J, Moore J, Marwaha J, Craig J, Klann J, Morris J, Obeid J, Vie J, Chen J, Son J, Zachariasse J, Booth J, Holmes J, Bernal-Sobrino J, Cruz-Bermúdez J, Leblanc J, Schuettler J, Dubiel J, Champ J, Olson K, Moshal K, Kernan K, Kirchoff K, Wagholikar K, Ngiam K, Cho K, Mandl K, Huling K, Chen K, Lynch K, Sanchez-Pinto L, Garmire L, Han L, Patel L, Waitman L, Lenert L, Anthony L, Esteve L, Chiudinelli L, Chiovato L, Scudeller L, Samayamuthu M, Martins M, Minicucci M, Menezes M, Vella M, Mazzitelli M, Savino M, Milano M, Okoshi M, Cannataro M, Alessiani M, Keller M, Hilka M, Wolkewitz M, Boeker M, Raskin M, Bucalo M, Hutch M, Bernaux M, Beraghi M, Morris M, Vitacca M, Pedrera-Jiménez M, Daniar M, Shah M, Liu M, Maripuri M, Kainth M, Yehya N, Santhanam N, Palmer N, Loh N, Sebire N, Romero-Garcia N, Brown N, Paris N, Griffon N, Gehlenborg N, Orlova N, García-Barrio N, Grisel O, Rojo P, Serrano-Balazote P, Sacchi P, Tippmann P, Martel P, Serre P, Avillach P, Azevedo P, Rubio-Mayo P, Schubert P, Guzzi P, Sliz P, Das P, Long Q, Ramoni R, Goh R, Badenes R, Bruno R, Kavuluru R, Bellazzi R, Issitt R, Follett R, Bradford R, Prudente R, Bey R, Griffier R, Duan R, Mahmood S, Mousavi S, Lozano-Zahonero S, Pizzimenti S, Maidlow S, Wong S, DuVall S, Cossin S, L'Yi S, Murphy S, Fan S, Visweswaran S, Rieg S, Bosari S, Makwana S, Bréant S, Bhatnagar S, Tanni S, Cormont S, Ahooyi T, Priya T, Naughton T, Ganslandt T, Colicchio T, Cai T, Gradinger T, González T, Zuccaro V, Tibollo V, Jouhet V, Quirós-González V, Panickan V, Benoit V, Njoroge W, Bryant W, Yuan W, Xiong X, Wang X, Ye Y, Luo Y, Ho Y, Strasser Z, Abad Z, Xia Z, Kate K, Hernández-Arango A, Schwamm E. Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms. JAMIA Open 2024, 7: ooae118. PMID: 39559493, PMCID: PMC11570992, DOI: 10.1093/jamiaopen/ooae118.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record datasetInternational Classification of Diseases codesInternational Classification of DiseasesAlignment of ontologiesMedical Language SystemHuman Phenotype OntologyData annotationBiomedical entitiesUMLData integrationElectronic health record dataInternational ClassificationHuman Phenotype Ontology termsHealth recordsOntologyCodeAnnotated phenotypesClinical diagnosis codesClassification of diseasesLanguage systemDatasetResearch ontologyMap coverageDiagnosis codesA Study of Biomedical Relation Extraction Using GPT Models.
Zhang J, Wibert M, Zhou H, Peng X, Chen Q, Keloth V, Hu Y, Zhang R, Xu H, Raja K. A Study of Biomedical Relation Extraction Using GPT Models. AMIA Joint Summits On Translational Science Proceedings 2024, 2024: 391-400. PMID: 38827097, PMCID: PMC11141827.Peer-Reviewed Original ResearchEnsemble pretrained language models to extract biomedical knowledge from literature
Li Z, Wei Q, Huang L, Li J, Hu Y, Chuang Y, He J, Das A, Keloth V, Yang Y, Diala C, Roberts K, Tao C, Jiang X, Zheng W, Xu H. Ensemble pretrained language models to extract biomedical knowledge from literature. Journal Of The American Medical Informatics Association 2024, 31: 1904-1911. PMID: 38520725, PMCID: PMC11339500, DOI: 10.1093/jamia/ocae061.Peer-Reviewed Original ResearchNatural language processingNatural language processing systemsLanguage modelExpansion of biomedical literatureZero-shot settingManually annotated corpusKnowledge graph developmentTask-specific modelsDomain-specific modelsZero-ShotEntity recognitionBillion parametersEnsemble learningLocation informationKnowledge basesBiomedical entitiesLanguage processingFree textGraph developmentBiomedical conceptsAutomated techniqueBiomedical literatureDetection methodPredictive performanceBiomedical knowledgeAdvancing entity recognition in biomedicine via instruction tuning of large language models
Keloth V, Hu Y, Xie Q, Peng X, Wang Y, Zheng A, Selek M, Raja K, Wei C, Jin Q, Lu Z, Chen Q, Xu H. Advancing entity recognition in biomedicine via instruction tuning of large language models. Bioinformatics 2024, 40: btae163. PMID: 38514400, PMCID: PMC11001490, DOI: 10.1093/bioinformatics/btae163.Peer-Reviewed Original ResearchNamed Entity RecognitionSequence labeling taskNatural language processingBiomedical NER datasetsLanguage modelNER datasetsEntity recognitionLabeling taskText generationField of natural language processingBiomedical NERFew-shot learning capabilityReasoning tasksMulti-domain scenariosDomain-specific modelsEnd-to-endMinimal fine-tuningSOTA performanceF1 scoreHealthcare applicationsBiomedical entitiesBiomedical domainLanguage processingMulti-taskingPubMedBERT model
2023
AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning
Luo L, Wei C, Lai P, Leaman R, Chen Q, Lu Z. AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning. Bioinformatics 2023, 39: btad310. PMID: 37171899, PMCID: PMC10212279, DOI: 10.1093/bioinformatics/btad310.Peer-Reviewed Original ResearchConceptsDeep learningEntity recognitionTraining dataEntity typesLabeling training dataNatural language textText mining tasksSignificant domain expertiseMulti-task learningMining tasksInformation extractionBioNER taskDomain expertiseBiomedical entitiesIndependent tasksSource codeBenchmark tasksLanguage textBiomedical textArt approachesAccurate annotationExternal dataData scarcityTaskLearning
2022
Biomedical Literature Mining and Its Components
Raja K. Biomedical Literature Mining and Its Components. Methods In Molecular Biology 2022, 2496: 1-16. PMID: 35713856, DOI: 10.1007/978-1-0716-2305-3_1.Peer-Reviewed Original ResearchConceptsMining protocolInformation retrieval approachBiomedical literaturePublished biomedical articlesUser queriesMining tasksInformation retrievalInformation extractionKnowledge discoveryBiomedical textBiomedical entitiesBiomedical articlesRetrieval approachAutomatic extractionRetrieving informationMining approachRelevant documentsPubMed titlesManual extractionMiningSource of knowledgeDrug mentionsInformationExponential ratePopulation information
2020
Named Entity Recognition from Table Headers in Randomized Controlled Trial Articles
Wei Q, Zhou Y, Zhao B, Hu X, Mei Q, Tao C, Xu H. Named Entity Recognition from Table Headers in Randomized Controlled Trial Articles. 2020, 00: 1-2. DOI: 10.1109/ichi48887.2020.9374323.Peer-Reviewed Original ResearchTable headersEntity recognitionDeep learning-based approachBiomedical text miningLearning-based approachNamed Entity RecognitionInformation extractionBiomedical entitiesF1 scoreText miningUnstructured natureBiomedical articlesContextual informationComputational applicationsHeaderSemantic complexityBetter performanceCorpusRecognitionInformationMiningApplicationsImportant informationComplexityBiomedical research
2019
LitSense: making sense of biomedical literature at sentence level
Allot A, Chen Q, Kim S, Alvarez R, Comeau D, Wilbur W, Lu Z. LitSense: making sense of biomedical literature at sentence level. Nucleic Acids Research 2019, 47: w594-w599. PMID: 31020319, PMCID: PMC6602490, DOI: 10.1093/nar/gkz289.Peer-Reviewed Original ResearchConceptsFirst web-based systemFilter search resultsNeural embedding approachBiomedical literatureUser-friendly interfaceWeb-based systemTerm-weighting approachUser queriesQuery formulationUnified accessKeyword matchesBiomedical entitiesSentence retrievalResults visualizationSearch resultsEmbedding approachCurrent toolsQueriesRetrievalSentence levelRare termsRelevant resultsSignificant effortsPrevious knowledgePubTator
2018
Mining protein phosphorylation information from biomedical literature using NLP parsing and Support Vector Machines
Raja K, Natarajan J. Mining protein phosphorylation information from biomedical literature using NLP parsing and Support Vector Machines. Computer Methods And Programs In Biomedicine 2018, 160: 57-64. PMID: 29728247, DOI: 10.1016/j.cmpb.2018.03.022.Peer-Reviewed Original ResearchConceptsPhosphorylation informationCross-corpus evaluationBiomedical literatureSupport vector machineGeneral datasetsSVM classificationBiomedical entitiesF-scoreVector machineTraining datasetCorpus evaluationSub-formsSpecific datasetsPerformance analysisIProLINKTest datasetNLPParsingCross-validation testDatasetPhosphorylated entitiesSVMBiological processesPhosphorylationCorpus
2017
CNN-based ranking for biomedical entity normalization
Li H, Chen Q, Tang B, Wang X, Xu H, Wang B, Huang D. CNN-based ranking for biomedical entity normalization. BMC Bioinformatics 2017, 18: 385. PMID: 28984180, PMCID: PMC5629610, DOI: 10.1186/s12859-017-1805-7.Peer-Reviewed Original ResearchConceptsBiomedical entity normalizationEntity normalizationSemantic informationCNN architectureNovel convolutional neural network architectureConvolutional neural network architectureTraditional rule-based methodsNeural network architectureRule-based systemRanking methodRule-based methodNetwork architectureBiomedical entitiesBenchmark datasetsArt performanceEntity mentionsRanking problemCNNNormalization systemArchitectureMorphological informationComparison resultsInformationDatasetSystemInformatics Support for Basic Research in Biomedicine.
Rindflesch TC, Blake CL, Fiszman M, Kilicoglu H, Rosemblat G, Schneider J, Zeiss CJ. Informatics Support for Basic Research in Biomedicine. ILAR Journal 2017, 58: 80-89. PMID: 28838071, PMCID: PMC5886329, DOI: 10.1093/ilar/ilx004.Peer-Reviewed Original ResearchConceptsSemantic MEDLINEText mining techniquesDocument retrievalHigh-level connectionsMining techniquesBiomedical research literatureUse casesBiomedical entitiesRelation identificationComputer-assisted techniquesInformatics supportPubMed queryUsersInformatics methodologiesBiomedical researchersInformatics methodsResearch areaHypothesis formationBiomedical research areasIterative processLarge amountQueriesLarge numberRetrievalCurrent developments
2007
Gene symbol disambiguation using knowledge-based profiles
Xu H, Fan J, Hripcsak G, Mendonça E, Markatou M, Friedman C. Gene symbol disambiguation using knowledge-based profiles. Bioinformatics 2007, 23: 1015-1022. PMID: 17314123, DOI: 10.1093/bioinformatics/btm056.Peer-Reviewed Original ResearchConceptsKnowledge sourcesSimilarity scoresInformation retrieval methodsGene symbol disambiguationText mining systemKnowledge-based profilesTesting data setsBiomedical entitiesBiomedical domainMEDLINE abstractsHigh similarity scoresRetrieval methodAmbiguous genesEntrez GeneGene symbolsDisambiguation taskTesting set
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