2024
Learning Integral Operators with Neural Integral Equations
Zappala, E., Fonseca, A. H. D. O., Caro, J. O., & van Dijk, D. (2022). Neural integral equations. arXiv preprint arXiv:2209.15190. (published at Nature Machine Intelligence)Peer-Reviewed Original ResearchBrainLM: A foundation model for brain activity recordings
Ortega Caro, J., Oliveira Fonseca, A. H., Averill, C., Rizvi, S. A., Rosati, M., Cross, J. L., ... & van Dijk, D. (2023). BrainLM: A foundation model for brain activity recordings. bioRxiv, 2023-09. (Published at ICLR 2024)Peer-Reviewed Original ResearchCell2sentence: Teaching large language models the language of biology
Levine, D., Lévy, S., Rizvi, S. A., Pallikkavaliyaveetil, N., Chen, X., Zhang, D., ... & van Dijk, D. (2023). Cell2sentence: Teaching large language models the language of biology. bioRxiv, 2023-09. (Published at ICML 2024)Peer-Reviewed Original Research
2023
scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.Peer-Reviewed Original ResearchLongitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1
Tejwani L, Ravindra N, Lee C, Cheng Y, Nguyen B, Luttik K, Ni L, Zhang S, Morrison L, Gionco J, Xiang Y, Yoon J, Ro H, Haidery F, Grijalva R, Bae E, Kim K, Martuscello R, Orr H, Zoghbi H, McLoughlin H, Ranum L, Shakkottai V, Faust P, Wang S, van Dijk D, Lim J. Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1. Neuron 2023, 112: 362-383.e15. PMID: 38016472, PMCID: PMC10922326, DOI: 10.1016/j.neuron.2023.10.039.Peer-Reviewed Original ResearchCausal identification of single-cell experimental perturbation effects with CINEMA-OT
Dong M, Wang B, Wei J, de O. Fonseca A, Perry C, Frey A, Ouerghi F, Foxman E, Ishizuka J, Dhodapkar R, van Dijk D. Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nature Methods 2023, 20: 1769-1779. PMID: 37919419, PMCID: PMC10630139, DOI: 10.1038/s41592-023-02040-5.Peer-Reviewed Original ResearchAnatomical Diversity of the Adult Corticospinal Tract Revealed by Single-Cell Transcriptional Profiling
Golan N, Ehrlich D, Bonanno J, O'Brien R, Murillo M, Kauer S, Ravindra N, Van Dijk D, Cafferty W. Anatomical Diversity of the Adult Corticospinal Tract Revealed by Single-Cell Transcriptional Profiling. Journal Of Neuroscience 2023, 43: 7929-7945. PMID: 37748862, PMCID: PMC10669816, DOI: 10.1523/jneurosci.0811-22.2023.Peer-Reviewed Original ResearchConceptsCorticospinal tract neuronsCorticospinal tractAdult corticospinal tractIrreversible functional deficitsCortical layer 2/3Lumbar spinal cordSpinal cord injuryLayer 5 neuronsClasses of neuronsMolecular heterogeneityAxon collateralizationTract neuronsCord injuryRetrograde labelingMotor pathwaysFemale miceFunctional deficitsLayer 2/3Primary axonsSpinal cordTerminal fieldsNeuron subtypesAdult tractTherapeutic interventionsSingle-cell RNA sequencingDistinguishing features of long COVID identified through immune profiling
Klein J, Wood J, Jaycox J, Dhodapkar R, Lu P, Gehlhausen J, Tabachnikova A, Greene K, Tabacof L, Malik A, Silva Monteiro V, Silva J, Kamath K, Zhang M, Dhal A, Ott I, Valle G, Peña-Hernández M, Mao T, Bhattacharjee B, Takahashi T, Lucas C, Song E, McCarthy D, Breyman E, Tosto-Mancuso J, Dai Y, Perotti E, Akduman K, Tzeng T, Xu L, Geraghty A, Monje M, Yildirim I, Shon J, Medzhitov R, Lutchmansingh D, Possick J, Kaminski N, Omer S, Krumholz H, Guan L, Dela Cruz C, van Dijk D, Ring A, Putrino D, Iwasaki A. Distinguishing features of long COVID identified through immune profiling. Nature 2023, 623: 139-148. PMID: 37748514, PMCID: PMC10620090, DOI: 10.1038/s41586-023-06651-y.Peer-Reviewed Original ResearchConceptsLong COVIDSARS-CoV-2Infection syndromeExaggerated humoral responseSoluble immune mediatorsEpstein-Barr virusPost-exertional malaiseCross-sectional studyHigher antibody responseImmune mediatorsImmune phenotypingImmune profilingHumoral responseAntibody responseLymphocyte populationsCOVID statusUnbiased machineCortisol levelsLC statusRelevant biomarkersViral pathogensSyndromeCOVIDFuture studiesBiological featuresContinuous Spatiotemporal Transformer
Fonseca, A. H. D. O., Zappala, E., Caro, J. O., & Van Dijk, D. (2023, July). Continuous Spatiotemporal Transformer. In International Conference on Machine Learning (pp. 7343-7365). PMLR.Peer-Reviewed Original ResearchNeural Integro-Differential Equations
Zappala E, de O. Fonseca A, Moberly A, Higley M, Abdallah C, Cardin J, Van Dijk D. Neural Integro-Differential Equations. Proceedings Of The AAAI Conference On Artificial Intelligence 2023, 37: 11104-11112. DOI: 10.1609/aaai.v37i9.26315.Peer-Reviewed Original ResearchIntegro-differential equationsIntegral operatorsDifferential equationsContinuous dynamical systemsNon-local dynamicsDynamical systemsInitial conditionsEquationsNeural networkTime extrapolationOperatorsIntegralsFundamental problemSuch dynamicsLatent spaceDynamicsNon-local processesBrain activity recordingsBrain dynamicsData scienceDifferential componentsIntegrandGeneralizationTheoryNetworkA machine learning method for the identification and characterization of novel COVID-19 drug targets.
Schultz B, DeLong LN, Masny A, Lentzen M, Raschka T, van Dijk D, Zaliani A, COPERIMOplus, Fröhlich H. A machine learning method for the identification and characterization of novel COVID-19 drug targets. Sci Rep 2023, 13: 7159. PMID: 37137934, DOI: 10.1038/s41598-023-34287-5.Peer-Reviewed Original ResearchNasal host response-based screening for undiagnosed respiratory viruses: a pathogen surveillance and detection study
Cheemarla N, Hanron A, Fauver J, Bishai J, Watkins T, Brito A, Zhao D, Alpert T, Vogels C, Ko A, Schulz W, Landry M, Grubaugh N, van Dijk D, Foxman E. Nasal host response-based screening for undiagnosed respiratory viruses: a pathogen surveillance and detection study. The Lancet Microbe 2023, 4: e38-e46. PMID: 36586415, PMCID: PMC9835789, DOI: 10.1016/s2666-5247(22)00296-8.Peer-Reviewed Original ResearchConceptsRespiratory virus panelPg/mLCXCL10 concentrationsSARS-CoV-2Bacterial pathobiontsRespiratory virusesSARS-CoV-2 negative samplesViral respiratory infectionsSARS-CoV-2 positive samplesClinical virology laboratoryHealth care systemVirus-positive samplesQuantitative RT-PCRInfluenza C virusSymptomatic patientsRespiratory infectionsSeasonal coronavirusesNasopharyngeal swabsVirus panelC virusCommon virusesCXCL10Host responseInterferon responseVirology laboratory
2022
Interspecies commensal interactions have nonlinear impacts on host immunity
Rice TA, Bielecka AA, Nguyen MT, Rosen CE, Song D, Sonnert ND, Yang Y, Cao Y, Khetrapal V, Catanzaro JR, Martin AL, Rashed SA, Leopold SR, Hao L, Yu X, van Dijk D, Ring AM, Flavell RA, de Zoete MR, Palm NW. Interspecies commensal interactions have nonlinear impacts on host immunity. Cell Host & Microbe 2022, 30: 988-1002.e6. PMID: 35640610, PMCID: PMC9283318, DOI: 10.1016/j.chom.2022.05.004.Peer-Reviewed Original ResearchConceptsImmunological outcomesCell activationIntestinal epithelial cell activationInflammatory bowel disease patientsBowel disease patientsDendritic cell activationMesenteric lymph nodesSystemic antibody responsesEpithelial cell activationImmunological milieuLymph nodesAntibody responseDisease patientsAkkermansia muciniphilaGnotobiotic miceHost immunityCommensal microbesHuman cohortsHuman gut bacteriaGut bacteriaMiceAllobaculumMuciniphilaDiseaseIncomplete penetranceSingle-cell multiomics reveals persistence of HIV-1 in expanded cytotoxic T cell clones
Collora JA, Liu R, Pinto-Santini D, Ravindra N, Ganoza C, Lama JR, Alfaro R, Chiarella J, Spudich S, Mounzer K, Tebas P, Montaner LJ, van Dijk D, Duerr A, Ho YC. Single-cell multiomics reveals persistence of HIV-1 in expanded cytotoxic T cell clones. Immunity 2022, 55: 1013-1031.e7. PMID: 35320704, PMCID: PMC9203927, DOI: 10.1016/j.immuni.2022.03.004.Peer-Reviewed Original ResearchConceptsHIV-1 eradicationHIV-1 RNAHIV-1Effector memory Th1 cellsHIV-1-infected individualsHIV-1-infected CD4Clonal expansionCell clonesMemory Th1 cellsCell clonal expansionPersistent antigenAntiretroviral therapyViral suppressionCytotoxic CD4Cytotoxic TTh1 cellsAntigen stimulationClonal expansion dynamicsUninfected individualsSurface protein expressionTNF responseUnstimulated conditionsTCR sequencesProtein expressionCD4Prdm6 controls heart development by regulating neural crest cell differentiation and migration
Hong L, Li N, Gasque V, Mehta S, Ye L, Wu Y, Li J, Gewies A, Ruland J, Hirschi KK, Eichmann A, Hendry C, van Dijk D, Mani A. Prdm6 controls heart development by regulating neural crest cell differentiation and migration. JCI Insight 2022, 7: e156046. PMID: 35108221, PMCID: PMC8876496, DOI: 10.1172/jci.insight.156046.Peer-Reviewed Original ResearchConceptsCardiac NCCNeural crest cell fateNeural crest cell differentiationSingle-cell RNA-seq analysisRNA-seq analysisDorsal neural tubeG1-S progressionFate-mapping approachCNCC migrationSpecification genesH4K20 monomethylationCell fateTranscriptomic analysisEpigenetic modifiersHeart developmentRegulated networkTranscript levelsKey regulatorMolecular mechanismsCell differentiationNeural tubePRDM6Ductus arteriosusPotential targetDifferentiationSingle-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19
Unterman A, Sumida TS, Nouri N, Yan X, Zhao AY, Gasque V, Schupp JC, Asashima H, Liu Y, Cosme C, Deng W, Chen M, Raredon MSB, Hoehn KB, Wang G, Wang Z, DeIuliis G, Ravindra NG, Li N, Castaldi C, Wong P, Fournier J, Bermejo S, Sharma L, Casanovas-Massana A, Vogels CBF, Wyllie AL, Grubaugh ND, Melillo A, Meng H, Stein Y, Minasyan M, Mohanty S, Ruff WE, Cohen I, Raddassi K, Niklason L, Ko A, Montgomery R, Farhadian S, Iwasaki A, Shaw A, van Dijk D, Zhao H, Kleinstein S, Hafler D, Kaminski N, Dela Cruz C. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nature Communications 2022, 13: 440. PMID: 35064122, PMCID: PMC8782894, DOI: 10.1038/s41467-021-27716-4.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAgedAntibodies, Monoclonal, HumanizedCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCells, CulturedCOVID-19COVID-19 Drug TreatmentFemaleGene Expression ProfilingGene Expression RegulationHumansImmunity, InnateMaleReceptors, Antigen, B-CellReceptors, Antigen, T-CellRNA-SeqSARS-CoV-2Single-Cell AnalysisConceptsProgressive COVID-19B cell clonesSingle-cell analysisT cellsImmune responseMulti-omics single-cell analysisCOVID-19Cell clonesAdaptive immune interactionsSevere COVID-19Dynamic immune responsesGene expressionSARS-CoV-2 virusAdaptive immune systemSomatic hypermutation frequenciesCellular effectsProtein markersEffector CD8Immune signaturesProgressive diseaseHypermutation frequencyProgressive courseClassical monocytesClonesImmune interactions
2021
Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference
Amodio M, Shung D, Burkhardt DB, Wong P, Simonov M, Yamamoto Y, van Dijk D, Wilson FP, Iwasaki A, Krishnaswamy S. Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference. Patterns 2021, 2: 100288. PMID: 34286302, PMCID: PMC8276014, DOI: 10.1016/j.patter.2021.100288.Peer-Reviewed Original ResearchA method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.
Schultz B, Zaliani A, Ebeling C, Reinshagen J, Bojkova D, Lage-Rupprecht V, Karki R, Lukassen S, Gadiya Y, Ravindra NG, Das S, Baksi S, Domingo-Fernández D, Lentzen M, Strivens M, Raschka T, Cinatl J, DeLong LN, Gribbon P, Geisslinger G, Ciesek S, van Dijk D, Gardner S, Kodamullil AT, Fröhlich H, Peitsch M, Jacobs M, Hoeng J, Eils R, Claussen C, Hofmann-Apitius M. A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization. Scientific Reports 2021, 11: 11049. PMID: 34040048, PMCID: PMC8155020, DOI: 10.1038/s41598-021-90296-2.Peer-Reviewed Original ResearchGaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
Sehanobish A, Ravindra N, Van Dijk D. Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. Proceedings Of The AAAI Conference On Artificial Intelligence 2021, 35: 4864-4873. DOI: 10.1609/aaai.v35i6.16619.Peer-Reviewed Original ResearchSARS-CoV-2 infectionSingle-cell omics dataCOVID-19 severitySingle-cell RNA sequencing datasetsCell typesRNA sequencing datasetsSARS-CoV-2Transcriptomic patternsSequencing datasetsOmics dataCellular determinantsCellular understandingIndividual cellsBronchoalveolar lavage fluid samplesInfected cellsSevere COVID-19Lavage fluid samplesCOVID-19Lung organoidsDisease statesInfectionTranscriptomeCellsSeverityFluid samplesA phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST)
Oikonomou EK, Van Dijk D, Parise H, Suchard MA, de Lemos J, Antoniades C, Velazquez EJ, Miller EJ, Khera R. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). European Heart Journal 2021, 42: 2536-2548. PMID: 33881513, PMCID: PMC8488385, DOI: 10.1093/eurheartj/ehab223.Peer-Reviewed Original ResearchConceptsStable chest painChest painPrimary endpointMajor adverse cardiovascular eventsNon-fatal myocardial infarctionAdverse cardiovascular eventsStudy's primary endpointCoronary artery diseaseClinical trial populationsCox regression modelParticipant-level dataSCOT-HEARTCardiovascular eventsCause mortalityHazard ratioPatients 5Artery diseaseFunctional testingPROMISE trialTrial populationMyocardial infarctionLower incidenceStudy populationPainCollected variables