2025
A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases
Zheng Y, Schupp J, Adams T, Clair G, Justet A, Ahangari F, Yan X, Hansen P, Carlon M, Cortesi E, Vermant M, Vos R, De Sadeleer L, Rosas I, Pineda R, Sembrat J, Königshoff M, McDonough J, Vanaudenaerde B, Wuyts W, Kaminski N, Ding J. A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases. Nature Biomedical Engineering 2025, 1-26. PMID: 40542107, DOI: 10.1038/s41551-025-01423-7.Peer-Reviewed Original ResearchComplex cellular dynamicsCellular dynamicsSingle-cell transcriptomic dataIn silico drug discoverySingle-cell transcriptomicsTranscriptome dataPotential therapeutic drug candidateComplex diseasesHuman diseasesIdiopathic pulmonary fibrosisTherapeutic drug candidateCell embeddingDrug discoveryPulmonary fibrosisDrug candidatesDisease progressionHuman tissuesHuman precision-cut lung slicesDynamic analysisPrecision-cut lung slicesPathological landscapeComputational toolsAnti-fibrotic effectsUnagiTranscriptomeConstraint Optimisation Approaches for Designing Group-Living Captive Breeding Programmes
Forshaw M, Gray R, VonHoldt B, Ochoa A, Miller J, Brzeski K, Caccone A, Jensen E. Constraint Optimisation Approaches for Designing Group-Living Captive Breeding Programmes. Proceedings Of The AAAI Conference On Artificial Intelligence 2025, 39: 27989-27997. DOI: 10.1609/aaai.v39i27.35016.Peer-Reviewed Original ResearchCaptive breeding programsBreeding program designsBreeding programsCaptive populationsEffective population sizeCaptive breeding programmesGalapagos giant tortoisesMultiple malesPrevent inbreedingCaptive breeding centresSpecies ecologyBreeding pairsReintroduction effortsGenetic healthBreeding programmesGiant tortoisesBreed groupsBreeding centresEndangered speciesBiodiversity crisisPopulation sizeBreedingRelatednessSpeciesComputational tools
2024
Hemodynamics and Wall Mechanics of Vascular Graft Failure
Szafron J, Heng E, Boyd J, Humphrey J, Marsden A. Hemodynamics and Wall Mechanics of Vascular Graft Failure. Arteriosclerosis Thrombosis And Vascular Biology 2024, 44: 1065-1085. PMID: 38572650, PMCID: PMC11043008, DOI: 10.1161/atvbaha.123.318239.Peer-Reviewed Original ResearchConceptsVascular graftsTissue-engineered vascular graftsWall mechanicsSolid mechanicsVascular graft failureLoad magnitudeMechanobiological processesLoadMechanobiological stimuliMechanosensitive signaling pathwaysBiomechanical stateWallGraft failureBiomechanical loadingCongenital heart surgeryCoronary Artery Bypass GraftingEnd-organ dysfunctionGraft materialArtery Bypass GraftingFeedback loopComputational tools
2023
Automated AJCC Restaging for Oropharyngeal Cancer Research
Safranek C, Wilkins S, Shah R, Mehra S. Automated AJCC Restaging for Oropharyngeal Cancer Research. Otolaryngology 2023, 170: 627-629. PMID: 37855637, DOI: 10.1002/ohn.558.Peer-Reviewed Original ResearchOropharyngeal squamous cell carcinomaComputational toolsData setsLarge data setsOpen-source designSimilar toolsHuman papillomavirus-positive oropharyngeal squamous cell carcinomaAmerican Joint CommitteeHPV-negative counterpartsSquamous cell carcinomaGuideline updateCell carcinomaAJCC editionToolEdition criteriaClinical tumorsJoint CommitteeSetCancer typesAlgorithmAccurate analysisAJCCCancer research
2022
Multiscale PHATE identifies multimodal signatures of COVID-19
Kuchroo M, Huang J, Wong P, Grenier JC, Shung D, Tong A, Lucas C, Klein J, Burkhardt DB, Gigante S, Godavarthi A, Rieck B, Israelow B, Simonov M, Mao T, Oh JE, Silva J, Takahashi T, Odio CD, Casanovas-Massana A, Fournier J, Farhadian S, Dela Cruz C, Ko A, Hirn M, Wilson F, Hussin J, Wolf G, Iwasaki A, Krishnaswamy S. Multiscale PHATE identifies multimodal signatures of COVID-19. Nature Biotechnology 2022, 40: 681-691. PMID: 35228707, PMCID: PMC10015653, DOI: 10.1038/s41587-021-01186-x.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencingTransposase-accessible chromatinSingle-cell sequencingRNA sequencingBiological insightsPopulation groupingsSophisticated computational toolsBiological featuresSequencingFlow cytometryComputational toolsChromatinBiomedical communityDifferent data typesCell responsesCellsPhate
2020
Rethinking Causation for Data‐intensive Biology: Constraints, Cancellations, and Quantized Organisms
Brash DE. Rethinking Causation for Data‐intensive Biology: Constraints, Cancellations, and Quantized Organisms. BioEssays 2020, 42: e1900135. PMID: 32484248, PMCID: PMC7518294, DOI: 10.1002/bies.201900135.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsNew mathematical toolNew mathematical approachComputational toolsMathematical toolsData-intensive biologyMathematical approachNonlinear dependenciesOptimal stateQuantized statesSingle control pointPhysical hierarchyComplex organismsCausal propertiesOrganismsSignal cancellationControl pointsExperimental strategiesBiologyConstraintsPhysicsCancellationEccentricityRecent refinementsBiologistsBiological behaviorBringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations
Comess S, Akbay A, Vasiliou M, Hines RN, Joppa L, Vasiliou V, Kleinstreuer N. Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations. Frontiers In Artificial Intelligence 2020, 3: 31. PMID: 33184612, PMCID: PMC7654840, DOI: 10.3389/frai.2020.00031.Peer-Reviewed Original ResearchBig dataArtificial InteligenceSkilled data scientistsComplex data setsData scientistsScientific computingCyber infrastructureMachine learningAI approachesReusable dataFAIR principlesData curationComputational toolsData setsEnvironmental public health researchResearch hubData collectionActionable recommendationsBroader public health communityComputingSharingInteligenceSufficient informationCurationParamount importance
2018
On the Evaluation and Validation of Off-the-Shelf Statistical Shape Modeling Tools: A Clinical Application
Goparaju A, Csecs I, Morris A, Kholmovski E, Marrouche N, Whitaker R, Elhabian S. On the Evaluation and Validation of Off-the-Shelf Statistical Shape Modeling Tools: A Clinical Application. Lecture Notes In Computer Science 2018, 11167: 14-27. PMID: 30805571, PMCID: PMC6385871, DOI: 10.1007/978-3-030-04747-4_2.Peer-Reviewed Original ResearchPrediction of RNA-protein interactions with distributed feature representations and a hybrid deep model
Zhang K, Xiao Y, Pan X, Yang Y. Prediction of RNA-protein interactions with distributed feature representations and a hybrid deep model. 2018, 1-5. DOI: 10.1145/3240876.3240912.Peer-Reviewed Original ResearchPrediction of RNA-protein interactionsRNA sequencingProtein-RNA interactionsRNA-protein interactionsComputational prediction toolsRNA-binding-proteinBiological processesOne-hot vectorDeep learning architectureHybrid deep modelBiological experimentsRNAMachine learning modelsSequenceBenchmark datasetsDeep modelsLearning architectureDistributed representationClassification modelComputational toolsStatistical featuresLearning modelsProteinPrediction toolsPredictive performanceEstimating a Separably Markov Random Field from Binary Observations
Zhang Y, Malem-Shinitski N, Allsop S, Tye K, Ba D. Estimating a Separably Markov Random Field from Binary Observations. Neural Computation 2018, 30: 1046-1079. PMID: 29381446, DOI: 10.1162/neco_a_01059.Peer-Reviewed Original ResearchConceptsNeural spiking dataMarkov random fieldLimitation of current methodsLoss of informationSpike dataNeural spikesConditional intensity functionRandom fieldState sequenceRF modelLearning of fearLearningTrial-to-trial dynamicsConditioned stimulusEstimate state-space modelsNeuronsPrefrontal cortexNeural underpinningsAssociative learningBinary observationsComputational toolsNeural activityIntensity functionTrialsCurrent methods
2017
Social eye gaze in human-robot interaction
Admoni H, Scassellati B. Social eye gaze in human-robot interaction. ACM Transactions On Human-Robot Interaction 2017, 6: 25-63. DOI: 10.5898/jhri.6.1.admoni.Peer-Reviewed Original Research
2015
Computational Approach to Annotating Variants of Unknown Significance in Clinical Next Generation Sequencing
Schulz WL, Tormey CA, Torres R. Computational Approach to Annotating Variants of Unknown Significance in Clinical Next Generation Sequencing. Lab Medicine 2015, 46: 285-289. PMID: 26489672, DOI: 10.1309/lmwzh57brwopr5rq.Peer-Reviewed Original ResearchConceptsNext-generation sequencingClinical significanceUnknown clinical significanceMalignant neoplasmsHematologic malignanciesClinical next-generation sequencingSoftware algorithmsGeneration sequencingUnknown significanceBenign variantsConflicting resultsClinical laboratoriesComputational toolsCommon technologyAlgorithm
2014
Conditional random fields for morphological analysis of wireless ECG signals
Natarajan A, Gaiser E, Angarita G, Malison R, Ganesan D, Marlin B. Conditional random fields for morphological analysis of wireless ECG signals. 2014, 2014: 370-379. PMID: 26726321, PMCID: PMC4697765, DOI: 10.1145/2649387.2649414.Peer-Reviewed Original ResearchECG signalsConditional random field modelOpen-source toolkitConditional Random FieldsMobile sensing technologiesIndependent prediction modelsRandom field modelSensor dataNew computational toolsSensing technologyRandom fieldsComputational toolsNovel approachField modelPrediction modelDiverse applicationsSame featuresNon-stationary signalsLab-based studiesUsersToolkitProblemTechnologyFrameworkCapability
2013
Convergence Platforms: Human-Scale Convergence and the Quality of Life
MacGregor D, Baba M, Oliva A, McLaughlin A, Scacchi W, Scassellati B, Rubin P, Mason R, Spohrer J. Convergence Platforms: Human-Scale Convergence and the Quality of Life. Science Policy Reports 2013, 53-93. DOI: 10.1007/978-3-319-02204-8_2.Peer-Reviewed Original ResearchInformation technologyConvergence of information technologyGlobal information infrastructureCommunity of usersOpen-source softwareConsumer robotsInformation infrastructureConnection of information technologiesImproving human qualitySimulation of dataHuman-scaleConvergenceTechnologyInformationComputational toolsRobotUsersInternetSoftwareCitizen scienceCo-evolutionParticipatory communityInfrastructureVisualizationConnection
2012
Introducing the Forensic Research/Reference on Genetics knowledge base, FROG-kb
Rajeevan H, Soundararajan U, Pakstis AJ, Kidd KK. Introducing the Forensic Research/Reference on Genetics knowledge base, FROG-kb. Investigative Genetics 2012, 3: 18. PMID: 22938150, PMCID: PMC3488007, DOI: 10.1186/2041-2223-3-18.Peer-Reviewed Original ResearchFROG-kbUser-provided dataOpen-access web applicationJava ServletsWeb applicationWeb toolKnowledge baseHuman identificationPrediction functionSpecific codeGenetic knowledge baseUseful functionalitiesComputational toolsOnline toolCurrent online versionFront endFunctionalityOrganized mannerForensic researchForensic communityDatabaseServletsJQueryToolJSP
2010
The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
Shi L, Campbell G, Jones W, Campagne F, Wen Z, Walker S, Su Z, Chu T, Goodsaid F, Pusztai L, Shaughnessy J, Oberthuer A, Thomas R, Paules R, Fielden M, Barlogie B, Chen W, Du P, Fischer M, Furlanello C, Gallas B, Ge X, Megherbi D, Symmans W, Wang M, Zhang J, Bitter H, Brors B, Bushel P, Bylesjo M, Chen M, Cheng J, Cheng J, Chou J, Davison T, Delorenzi M, Deng Y, Devanarayan V, Dix D, Dopazo J, Dorff K, Elloumi F, Fan J, Fan S, Fan X, Fang H, Gonzaludo N, Hess K, Hong H, Huan J, Irizarry R, Judson R, Juraeva D, Lababidi S, Lambert C, Li L, Li Y, Li Z, Lin S, Liu G, Lobenhofer E, Luo J, Luo W, McCall M, Nikolsky Y, Pennello G, Perkins R, Philip R, Popovici V, Price N, Qian F, Scherer A, Shi T, Shi W, Sung J, Thierry-Mieg D, Thierry-Mieg J, Thodima V, Trygg J, Vishnuvajjala L, Wang S, Wu J, Wu Y, Xie Q, Yousef W, Zhang L, Zhang X, Zhong S, Zhou Y, Zhu S, Arasappan D, Bao W, Lucas A, Berthold F, Brennan R, Buness A, Catalano J, Chang C, Chen R, Cheng Y, Cui J, Czika W, Demichelis F, Deng X, Dosymbekov D, Eils R, Feng Y, Fostel J, Fulmer-Smentek S, Fuscoe J, Gatto L, Ge W, Goldstein D, Guo L, Halbert D, Han J, Harris S, Hatzis C, Herman D, Huang J, Jensen R, Jiang R, Johnson C, Jurman G, Kahlert Y, Khuder S, Kohl M, Li J, Li L, Li M, Li Q, Li S, Li Z, Liu J, Liu Y, Liu Z, Meng L, Madera M, Martinez-Murillo F, Medina I, Meehan J, Miclaus K, Moffitt R, Montaner D, Mukherjee P, Mulligan G, Neville P, Nikolskaya T, Ning B, Page G, Parker J, Parry R, Peng X, Peterson R, Phan J, Quanz B, Ren Y, Riccadonna S, Roter A, Samuelson F, Schumacher M, Shambaugh J, Shi Q, Shippy R, Si S, Smalter A, Sotiriou C, Soukup M, Staedtler F, Steiner G, Stokes T, Sun Q, Tan P, Tang R, Tezak Z, Thorn B, Tsyganova M, Turpaz Y, Vega S, Visintainer R, von Frese J, Wang C, Wang E, Wang J, Wang W, Westermann F, Willey J, Woods M, Wu S, Xiao N, Xu J, Xu L, Yang L, Zeng X, Zhang J, Zhang L, Zhang M, Zhao C, Puri R, Scherf U, Tong W, Wolfinger R. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology 2010, 28: 827-838. PMID: 20676074, PMCID: PMC3315840, DOI: 10.1038/nbt.1665.Peer-Reviewed Original Research
2007
Towards Systems Biology of Human Pulmonary Fibrosis
Studer SM, Kaminski N. Towards Systems Biology of Human Pulmonary Fibrosis. Annals Of The American Thoracic Society 2007, 4: 85-91. PMID: 17202296, PMCID: PMC2647618, DOI: 10.1513/pats.200607-139jg.Peer-Reviewed Original ResearchConceptsSystems biology approachBiology approachSystems biologyHigh-throughput genotypingHigh-throughput technologiesMicroarray resultsBiological processesQuantitative phenotypingEnvironmental stimuliFibrosis researchMultiple pathwaysLung phenotypeBiologyGlobal analysisHuman pulmonary fibrosisGenetic polymorphismsIdiopathic pulmonary fibrosisComputational toolsPulmonary fibrosisSystemwide viewHigh-resolution profilesPhenotypePathwayLethal lung diseasePolymorphism
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