2025
Benchmarking large language models for biomedical natural language processing applications and recommendations
Chen Q, Hu Y, Peng X, Xie Q, Jin Q, Gilson A, Singer M, Ai X, Lai P, Wang Z, Keloth V, Raja K, Huang J, He H, Lin F, Du J, Zhang R, Zheng W, Adelman R, Lu Z, Xu H. Benchmarking large language models for biomedical natural language processing applications and recommendations. Nature Communications 2025, 16: 3280. PMID: 40188094, PMCID: PMC11972378, DOI: 10.1038/s41467-025-56989-2.Peer-Reviewed Original ResearchConceptsLanguage modelNatural language processing applicationsBiomedical natural language processingMedical question answeringLanguage processing applicationsNatural language processingGrowth of biomedical literatureMissing informationFew-shotQuestion AnsweringZero-ShotKnowledge curationLanguage processingProcessing applicationsBioNLPBART modelPerformance gapBiomedical literatureGeneral domainTaskBenchmarksBERTInformationPerformanceLLMmFollowIR: A Multilingual Benchmark for Instruction Following in Retrieval
Weller O, Chang B, Yang E, Yarmohammadi M, Barham S, MacAvaney S, Cohan A, Soldaini L, Van Durme B, Lawrie D. mFollowIR: A Multilingual Benchmark for Instruction Following in Retrieval. Lecture Notes In Computer Science 2025, 15573: 295-310. DOI: 10.1007/978-3-031-88711-6_19.Peer-Reviewed Original ResearchA Multidisciplinary Multimodal Aligned Dataset for Academic Data Processing
Song H, Xu H, Wang Z, Wang Y, Li J. A Multidisciplinary Multimodal Aligned Dataset for Academic Data Processing. Scientific Data 2025, 12: 172. PMID: 39881139, PMCID: PMC11779955, DOI: 10.1038/s41597-025-04415-z.Peer-Reviewed Original ResearchData processingResearch trend analysisCitation recommendationCaption generationModel-based techniquesTextual dataAlignment datasetsProcessing capabilitiesDatasetVisual elementsScholarly articlesValidation methodPeer-reviewed scholarly articlesScientometricsSCImagoBibliometricsMetadataBenchmarksCitationsCountry/region distributionVisualizationResearch endeavorsAccuracyResearch prospectsCapability
2024
A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability
King R, Krueger C, Veselka E, Yang T, Mortazavi B. A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability. 2024, 00: 1-8. DOI: 10.1109/bhi62660.2024.10913656.Peer-Reviewed Original ResearchElastic weight consolidationIncremental learning methodData replayMachine learning modelsLearning methodsLearning modelsInput data distributionParameter regularization methodsMachine learning models' abilityLearning benchmarksData distributionStored examplesLearning problemsComputational powerMachine learningData sourcesReplayModel capacityRegularization methodOriginal domainBenchmarksMachineModel transferabilityModel's abilityAccurate resultsBioCoder: a benchmark for bioinformatics code generation with large language models
Tang X, Qian B, Gao R, Chen J, Chen X, Gerstein M. BioCoder: a benchmark for bioinformatics code generation with large language models. Bioinformatics 2024, 40: i266-i276. PMID: 38940140, PMCID: PMC11211839, DOI: 10.1093/bioinformatics/btae230.Peer-Reviewed Original ResearchConceptsCode generationLanguage modelAmount of domain knowledgeDomain-specific knowledgeJava methodsDomain knowledgeClass declarationsPerformance gainsData operationsPython functionsTraining datasetSuccess modelIntricate taskTest benchmarksDocker imageBenchmarksCodeSmall modelsDatasetGlobal variablesBioCodeFunctional dependenceEvaluate various modelsIncreasing needCodeGenODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection.
Kwon S, Wang X, Liu W, Druhl E, Sung M, Reisman J, Li W, Kerns R, Becker W, Yu H. ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection. Annual Meeting 2024, 2024: 4338-4359. PMID: 39224833, PMCID: PMC11368170.Peer-Reviewed Original ResearchBenchmark datasetsState-of-the-art natural language processing modelsNatural language processing modelsState-of-the-artLanguage processing modelsAberrant behaviorDetection datasetMacro-average areaBehavior detectionPrecision recall curveDiagnosed opioid dependencePerformance improvementRecall curveDatasetProcess modelExperimental resultsCentral nervous system-relatedOpioid dependenceNervous system-relatedMedication changesOpioidBenchmarksCalibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
You C, Min Y, Dai W, Sekhon J, Staib L, Duncan J. Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 2024, 00: 26140-26150. PMID: 39640960, PMCID: PMC11620289, DOI: 10.1109/cvpr52733.2024.02470.Peer-Reviewed Original ResearchDiverse downstream tasksVision-language modelsPre-trained modelsRepresentation of samplesContrastive learningDownstream tasksFeature reweightingTraining dataFeature patternsModel generalizationGroup annotationsPain pointsGroup labelsAnnotationRobustnessClassifierClipsFeaturesDeepDeploymentBenchmarksTime-intensiveCodeTaskLearningA survey of generative AI for de novo drug design: new frontiers in molecule and protein generation
Tang X, Dai H, Knight E, Wu F, Li Y, Li T, Gerstein M. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation. Briefings In Bioinformatics 2024, 25: bbae338. PMID: 39007594, PMCID: PMC11247410, DOI: 10.1093/bib/bbae338.Peer-Reviewed Original ResearchArtificial Intelligence for Cardiovascular Care—Part 1: Advances JACC Review Topic of the Week
Elias P, Jain S, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein A, Avram R, Tison G, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva J, Maddox T. Artificial Intelligence for Cardiovascular Care—Part 1: Advances JACC Review Topic of the Week. Journal Of The American College Of Cardiology 2024, 83: 2472-2486. PMID: 38593946, DOI: 10.1016/j.jacc.2024.03.400.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsEnhanced image qualityHuman expertsLeverage AIEvaluation benchmarkArtificial intelligenceAI modelsAI advancementsDetect diseaseTraining methodsImage qualityReduced ejection fractionEvolving technologyValvular heart diseaseReal-world efficacyEjection fractionProvider experienceHeart diseaseTechnologyCardiovascular carePatient careUnique characteristicsIntelligenceBenchmarksKernel-elastic autoencoder for molecular design
Li H, Shee Y, Allen B, Maschietto F, Morgunov A, Batista V. Kernel-elastic autoencoder for molecular design. PNAS Nexus 2024, 3: pgae168. PMID: 38689710, PMCID: PMC11059255, DOI: 10.1093/pnasnexus/pgae168.Peer-Reviewed Original ResearchMaximum mean discrepancyMean discrepancyTransformer architectureCondition generatorWeighted reconstructionTraining datasetGenerative modelGeneration approachDocking applicationsMolecular designAutoencoderAccurate reconstructionVAESpectrum of applicationsAutoDock VinaEnhanced performanceDesignDatasetArchitectureGeneration performanceBenchmarksApplicationsGlide scoreReconstructionGeneration behavior
2023
Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report
Meysman P, Barton J, Bravi B, Cohen-Lavi L, Karnaukhov V, Lilleskov E, Montemurro A, Nielsen M, Mora T, Pereira P, Postovskaya A, Martínez M, Fernandez-de-Cossio-Diaz J, Vujkovic A, Walczak A, Weber A, Yin R, Eugster A, Sharma V. Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report. ImmunoInformatics 2023, 9: 100024. DOI: 10.1016/j.immuno.2023.100024.Peer-Reviewed Original Research
2022
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?
Zhuang C, Xiang V, Bai Y, Jia X, Turk-Browne N, Norman K, DiCarlo J, Yamins D. How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? Advances In Neural Information Processing Systems 2022, 35: 22628-22642. PMID: 38435074, PMCID: PMC10906807.Peer-Reviewed Original ResearchSelf-supervised algorithmLearning algorithmsReal-timeStreams of visual inputNeural network modelHuman learning abilitiesMoCo v2Catastrophic forgettingLearning benchmarksLearning capabilityVisual inputReal worldHuman learnersNetwork modelVisual knowledgeLeverage memoryPerformance of modelsAlgorithmHuman performanceBenchmarksNegative samplesContext-sensitiveLearning abilityLearningVision setting
2021
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Khashabi D, Cohan A, Shakeri S, Hosseini P, Pezeshkpour P, Alikhani M, Aminnaseri M, Bitaab M, Brahman F, Ghazarian S, Gheini M, Kabiri A, Mahabagdi R, Memarrast O, Mosallanezhad A, Noury E, Raji S, Rasooli M, Sadeghi S, Azer E, Samghabadi N, Shafaei M, Sheybani S, Tazarv A, Yaghoobzadeh Y. ParsiNLU: A Suite of Language Understanding Challenges for Persian. Transactions Of The Association For Computational Linguistics 2021, 9: 1147-1162. DOI: 10.1162/tacl_a_00419.Peer-Reviewed Original ResearchNatural language understandingNatural language understanding tasksPersian languageMultilingual pre-trained language modelsLanguage understandingUnderstanding ChallengeResource-rich languagesPre-trained language modelsState-of-the-artNative speakersSpoken languageLanguageLanguage modelTextual entailmentEvaluation datasetManual annotationHuman performancePersianDatasetBenchmarksEnglishSpeakersTaskComprehensionEntailmentDomain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
Liu X, Hu B, Jin L, Han X, Xing F, Ouyang J, Lu J, El Fakhri G, Woo J. Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference. 2021, 881-887. DOI: 10.24963/ijcai.2021/122.Peer-Reviewed Original ResearchDomain generalizationDomain-invariant feature learningVariational Bayesian inference frameworkLabel shiftCross-domain accuracyLabeled source domainVariational Bayesian inferenceFeature learningBayesian inference frameworkLatent spaceSource domainTarget domainDistribution matchingTransfer knowledgeInference frameworkSuperior performanceP(x|yBayesian inferenceLabelingDomainFrameworkGeneralizationBenchmarksPosterior alignmentLearningSimplified Data Wrangling with ir_datasets
MacAvaney S, Yates A, Feldman S, Downey D, Cohan A, Goharian N. Simplified Data Wrangling with ir_datasets. 2021, 2429-2436. DOI: 10.1145/3404835.3463254.Peer-Reviewed Original ResearchIdentity-aware Facial Expression Recognition in Compressed Video
Liu X, Jin L, Han X, Lu J, You J, Kong L. Identity-aware Facial Expression Recognition in Compressed Video. 2021, 00: 7508-7514. DOI: 10.1109/icpr48806.2021.9412820.Peer-Reviewed Original ResearchResidual framesFacial expression recognitionFacial expression representationApex frameVideo domainCompressed videoI framesExpression recognitionCompressed formatRGB imagesExpressive representationIdentity eliminationExpression featuresIdentity labelsVideoMuscle movementMarginal independenceFrameExpression samplesRGBInter-subject variationDatasetExpression predictionNetworkBenchmarks
2019
CEDR
MacAvaney S, Yates A, Cohan A, Goharian N. CEDR. 2019, 1101-1104. DOI: 10.1145/3331184.3331317.Peer-Reviewed Original ResearchLarge expert-curated database for benchmarking document similarity detection in biomedical literature search
Brown P, Tan A, El-Esawi M, Liehr T, Blanck O, Gladue D, Almeida G, Cernava T, Sorzano C, Yeung A, Engel M, Chandrasekaran A, Muth T, Staege M, Daulatabad S, Widera D, Zhang J, Meule A, Honjo K, Pourret O, Yin C, Zhang Z, Cascella M, Flegel W, Goodyear C, van Raaij M, Bukowy-Bieryllo Z, Campana L, Kurniawan N, Lalaouna D, Hüttner F, Ammerman B, Ehret F, Cobine P, Tan E, Han H, Xia W, McCrum C, Dings R, Marinello F, Nilsson H, Nixon B, Voskarides K, Yang L, Costa V, Bengtsson-Palme J, Bradshaw W, Grimm D, Kumar N, Martis E, Prieto D, Sabnis S, Amer S, Liew A, Perco P, Rahimi F, Riva G, Zhang C, Devkota H, Ogami K, Basharat Z, Fierz W, Siebers R, Tan K, Boehme K, Brenneisen P, Brown J, Dalrymple B, Harvey D, Ng G, Werten S, Bleackley M, Dai Z, Dhariwal R, Gelfer Y, Hartmann M, Miotla P, Tamaian R, Govender P, Gurney-Champion O, Kauppila J, Zhang X, Echeverría N, Subhash S, Sallmon H, Tofani M, Bae T, Bosch O, Cuív P, Danchin A, Diouf B, Eerola T, Evangelou E, Filipp F, Klump H, Kurgan L, Smith S, Terrier O, Tuttle N, Ascher D, Janga S, Schulte L, Becker D, Browngardt C, Bush S, Gaullier G, Ide K, Meseko C, Werner G, Zaucha J, Al-Farha A, Greenwald N, Popoola S, Rahman S, Xu J, Yang S, Hiroi N, Alper O, Baker C, Bitzer M, Chacko G, Debrabant B, Dixon R, Forano E, Gilliham M, Kelly S, Klempnauer K, Lidbury B, Lin M, Lynch I, Ma W, Maibach E, Mather D, Nandakumar K, Ohgami R, Parchi P, Tressoldi P, Xue Y, Armitage C, Barraud P, Chatzitheochari S, Coelho L, Diao J, Doxey A, Gobet A, Hu P, Kaiser S, Mitchell K, Salama M, Shabalin I, Song H, Stevanovic D, Yadollahpour A, Zeng E, Zinke K, Alimba C, Beyene T, Cao Z, Chan S, Gatchell M, Kleppe A, Piotrowski M, Torga G, Woldesemayat A, Cosacak M, Haston S, Ross S, Williams R, Wong A, Abramowitz M, Effiong A, Lee S, Abid M, Agarabi C, Alaux C, Albrecht D, Atkins G, Beck C, Bonvin A, Bourke E, Brand T, Braun R, Bull J, Cardoso P, Carter D, Delahay R, Ducommun B, Duijf P, Epp T, Eskelinen E, Fallah M, Farber D, Fernandez-Triana J, Feyerabend F, Florio T, Friebe M, Furuta S, Gabrielsen M, Gruber J, Grybos M, Han Q, Heinrich M, Helanterä H, Huber M, Jeltsch A, Jiang F, Josse C, Jurman G, Kamiya H, de Keersmaecker K, Kristiansson E, de Leeuw F, Li J, Liang S, Lopez-Escamez J, Lopez-Ruiz F, Marchbank K, Marschalek R, Martín C, Miele A, Montagutelli X, Morcillo E, Nicoletti R, Niehof M, O’Toole R, Ohtomo T, Oster H, Palma J, Paterson R, Peifer M, Portilla M, Portillo M, Pritchard A, Pusch S, Raghava G, Roberts N, Ross K, Schuele B, Sergeant K, Shen J, Stella A, Sukocheva O, Uversky V, Vanneste S, Villet M, Viveiros M, Vorholt J, Weinstock C, Yamato M, Zabetakis I, Zhao X, Ziegler A, Aizat W, Atlas L, Bridges K, Chakraborty S, Deschodt M, Domingues H, Esfahlani S, Falk S, Guisado J, Kane N, Kueberuwa G, Lau C, Liang D, Liu E, Luu A, Ma C, Ma L, Moyer R, Norris A, Panthee S, Parsons J, Peng Y, Pinto I, Reschke C, Sillanpää E, Stewart C, Uhle F, Yang H, Zhou K, Zhu S, Ashry M, Bergsland N, Berthold M, Chen C, Colella V, 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2017
Benchmarks for measurement of duplicate detection methods in nucleotide databases
Chen Q, Zobel J, Verspoor K. Benchmarks for measurement of duplicate detection methods in nucleotide databases. Database 2017, 2023: baw164. PMID: 28334741, PMCID: PMC10755258, DOI: 10.1093/database/baw164.Peer-Reviewed Original ResearchDuplicate detection methodsNucleotide databaseUniProtKB/Swiss-ProtDetection methodMolecular biology researchData quality challengesPresence of duplicatesUniProt KnowledgebaseDuplicate detectionNucleotide sequenceCoding sequenceDatabase benchmarksDuplication of informationRecord linkage methodsExpert curationBiological researchBiological duplicatesBenchmarksNucleotideDuplicationTest efficiencyLinkage methods
2001
A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions
Dreiseitl S, Ohno-Machado L, Kittler H, Vinterbo S, Billhardt H, Binder M. A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions. Journal Of Biomedical Informatics 2001, 34: 28-36. PMID: 11376540, DOI: 10.1006/jbin.2001.1004.Peer-Reviewed Original ResearchConceptsArtificial neural networkDichotomous problemNearest neighborsDifferent classification tasksSpecific classification problemMachine learning methodsMachine-learning methodsClassification taskClassification problemNeural networkLearning methodsDecision tressPigmented skin lesionsVector machineDecision treeTaskNeighborsSVMMachineNetworkBenchmarksCommon neviMethodExcellent results
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