2025
The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering
Carlson D, Chavarriaga R, Liu Y, Lotte F, Lu B. The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering. Journal Of Neural Engineering 2025, 22: 021002. PMID: 40073450, PMCID: PMC11948487, DOI: 10.1088/1741-2552/adbfbd.Peer-Reviewed Original ResearchConnectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors
Ben-Zion Z, Simon A, Rosenblatt M, Korem N, Duek O, Liberzon I, Shalev A, Hendler T, Levy I, Harpaz-Rotem I, Scheinost D. Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors. JAMA Network Open 2025, 8: e250331. PMID: 40063028, PMCID: PMC11894499, DOI: 10.1001/jamanetworkopen.2025.0331.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingPosttraumatic stress disorderClinician-Administered PTSD Scale for DSM-5Months post-traumaFunctional magnetic resonance imagingTrauma survivorsDSM-5Post-traumaPosttraumatic stress disorder symptom clustersPosttraumatic stress disorder symptom severityPosttraumatic stress disorder symptom trajectoriesPosttraumatic stress disorder severityTask-based fMRI dataAnterior default modePredictive of symptomsFollow-up assessmentDevelopment of effective personalized treatmentsComprehensive clinical assessmentClinician-AdministeredNeurobiological indicesPosttraumatic psychopathologyHyperarousal symptomsCentral executiveTrauma exposureSalience networkViral genomic features predict Orthopoxvirus reservoir hosts
Tseng K, Koehler H, Becker D, Gibb R, Carlson C, Pilar Fernandez M, Seifert S. Viral genomic features predict Orthopoxvirus reservoir hosts. Communications Biology 2025, 8: 309. PMID: 40000824, PMCID: PMC11862092, DOI: 10.1038/s42003-025-07746-0.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsDisease ReservoirsGenome, ViralGenomicsHumansMachine LearningOrthopoxvirusPoxviridae InfectionsConceptsHost speciesViral genomic featuresHost ecological traitsPotential host speciesGenomic featuresHistorical rangeEcological traitsParts of Southeast AsiaWildlife surveillanceHuman populationCausative agent of smallpoxHost predictionAgent of smallpoxSpeciesSoutheast AsiaCausative agentGeographic regionsOrthopoxvirusesRecent Advances in Mass Spectrometry-Based Bottom-Up Proteomics
Movassaghi C, Sun J, Jiang Y, Turner N, Chang V, Chung N, Chen R, Browne E, Lin C, Schweppe D, Malaker S, Meyer J. Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics. Analytical Chemistry 2025, 97: 4728-4749. PMID: 40000226, DOI: 10.1021/acs.analchem.4c06750.Peer-Reviewed Original ResearchLearning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain
Kim H, Karaman B, Zhao Q, Wang A, Sabuncu M, Initiative F, Weiner M, Aisen P, Petersen R, Jack C, Jagust W, Landau S, Rivera-Mindt M, Okonkwo O, Shaw L, Lee E, Toga A, Beckett L, Harvey D, Green R, Saykin A, Nho K, Perrin R, Tosun D, Sachdev P, Green R, Drake E, Montine T, Conti C, Weiner M, Nosheny R, Sacrey D, Fockler J, Miller M, Kwang W, Jin C, Diaz A, Ashford M, Flenniken D, Kormos A, Petersen R, Aisen P, Rafii M, Raman R, Jimenez G, Donohue M, Salazar J, Fidell A, Boatwright V, Robison J, Zimmerman C, Cabrera Y, Walter S, Clanton T, Shaffer E, Webb C, Hergesheimer L, Smith S, Ogwang S, Adegoke O, Mahboubi P, Pizzola J, Jenkins C, Beckett L, Harvey D, Donohue M, Saito N, Diaz A, Hussen K, Okonkwo O, Rivera-Mindt M, Amaza H, Thao M, Parkins S, Ayo O, Glittenberg M, Hoang I, Germano K, Strong J, Weisensel T, Magana F, Thomas L, Guzman V, Ajayi A, Di Benedetto J, Talavera S, Jack C, Felmlee J, Fox N, Thompson P, DeCarli C, Forghanian-Arani A, Borowski B, Reyes C, Hedberg C, Ward C, Schwarz C, Reyes D, Gunter J, Moore-Weiss J, Kantarci K, Matoush L, Senjem M, Vemuri P, Reid R, Malone I, Thomopoulos S, Nir T, Jahanshad N, Knaack A, Fletcher E, Harvey D, Tosun-Turgut D, Chen S, Choe M, Crawford K, Yushkevich P, Das S, Jagust W, Landau S, Koeppe R, Rabinovici G, Villemagne V, LoPresti B, Perrin R, Morris J, Franklin E, Bernhardt H, Cairns N, Taylor-Reinwald L, Shaw L, Lee E, Lee V, Korecka M, Brylska M, Wan Y, Trojanowki J, Toga A, Crawford K, Neu S, Saykin A, Nho K, Foroud T, Jo T, Risacher S, Craft H, Apostolova L, Nudelman K, Faber K, Potter Z, Lacy K, Kaddurah-Daouk R, Shen L, Karlawish J, Erickson C, Grill J, Largent E, Harkins K, Weiner M, Thal L, Kachaturian Z, Frank R, Snyder P, Buckholtz N, Hsiao J, Ryan L, Molchan S, Khachaturian Z, Carrillo M, Potter W, Barnes L, Bernard M, González H, Ho C, Hsiao J, Jackson J, Masliah E, Masterman D, Okonkwo O, Perrin R, Ryan L, Silverberg N, Silbert L, Kaye J, White S, Pierce A, Thomas A, Clay T, Schwartz D, Devereux G, Taylor J, Ryan J, Nguyen M, DeCapo M, Shang Y, Schneider L, Munoz C, Ferman D, Conant C, Martin K, Oleary K, Pawluczyk S, Trejo E, Dagerman K, Teodoro L, Becerra M, Fairooz M, Garrison S, Boudreau J, Avila Y, Brewer J, Jacobson A, Gama A, Kim C, Little E, Frascino J, Ferng N, Trujillo S, Heidebrink J, Koeppe R, MacDonald S, Malyarenko D, Ziolkowski J, O’Connor J, Robert N, Lowe S, Rogers V, Clinic M, Petersen R, Hackenmiller B, Boeve B, Albers C, Kreuger C, Jones D, Knopman D, Botha H, Magnuson J, Graff-Radford J, Crawley K, Schumacher M, McKinzie S, Smith S, Helland T, Lowe V, Ramanan V, Pavlik V, Faircloth J, Bishop J, Nath J, Chaudhary M, Kataki M, Yu M, Pacini N, Barker R, Brooks R, Aggarwal R, Honig L, Stern Y, Mintz A, Cordona J, Hernandez M, Long J, Arnold A, Groves A, Middleton A, Vogler B, McCurry C, Mayo C, Raji C, Amtashar F, Klemp H, Ruszkiewicz J, Kusuran J, Stewart J, Horenkamp J, Greeson J, Wever K, Vo K, Larkin K, Rao L, Schoolcraft L, Gallagher L, Paczynski M, McMillan M, Holt M, Gagliano N, Henson R, LaBarge R, Swarm R, Munie S, Cepeda S, Winterton S, Hegedus S, Wilson T, Harte T, Bonacorsi Z, Geldmacher D, Watkins A, Barger B, Smelser B, Bates C, Stover C, McKinley E, Ikner G, Hendrix H, Cooper H, Mahaffey J, Robbins L, Ashley L, Natelson-Love M, Carter, Solomon V, Grossman H, Groome A, Ardolino A, Kaplan A, Sheppard F, Burgos-Rivera G, Garcia-Camilo G, Lim J, Neugroschl J, Jackson K, Evans K, Soleimani L, Sano M, Ghesani N, Binder S, Apuango X, Sood A, Troutman A, Blanchard K, Richards A, Nelson G, Hendrickson K, Yurko E, Plenge J, Rufo V, Shah R, Duara R, Lynch B, Chirinos C, Dittrich C, Campbell D, Mejia D, Perez G, Colvee H, Gonzalez J, Gondrez J, Knaack J, Acevedo M, Cereijo M, Greig-Custo M, Villar M, Wishnia M, Detling S, Barker W, Albert M, Moghekar A, Rodzon B, Demsky C, Pontone G, Pekar J, Farrington L, Pomper M, Johnson N, Alo T, Sadowski M, Ulysse A, Masurkar A, Marti B, Mossa D, Geesey E, Petrocca E, Schulze E, Wong J, Boonsiri J, Kenowsky S, Martinez T, Briglall V, Doraiswamy P, Nwosu A, Adhikari A, Hellegers C, Petrella J, James O, Wong T, Hawk T, Vaishnavi S, McCoubrey H, Nasrallah I, Rovere R, Maneval J, Robinson E, Rivera F, Uffelman J, Combs M, O’Donnell P, Manning S, King R, Nieto A, Glueck A, Mandal A, Swain A, Gamble B, Meacham B, Forenback D, Ross D, Cheatham E, Hartman E, Cornell G, Harp J, Ashe L, Goins L, Watts L, Yazell M, Mandal P, Buckler R, Vincent S, Rudd T, Lopez O, Malia A, Chiado C, Zik C, Ruszkiewicz J, Savage K, Fenice L, Oakley M, Tacey P, Berman S, Bowser S, Hegedus S, Saganis X, Porsteinsson A, Mathewson A, Widman A, Holvey B, Clark E, Morales E, Young I, Ruszkiewicz J, Hopkins K, Martin K, Kowalski N, Hunt R, Calzavara R, Kurvach R, D’Ambrosio S, Thai G, Vides B, Lieb B, McAdams-Ortiz C, Toso C, Mares I, Moorlach K, Liu L, Corona M, Nguyen M, Tallakson M, McDonnell M, Rangel M, Basheer N, Place P, Romero R, Tam S, Nguyen T, Thomas A, Frolov A, Khera A, Browning A, Kelley B, Dawson C, Mathews D, Most E, Phillips E, Nguyen L, Nunez M, Miller M, Jones M, Martinez N, Logan R, McColl R, Pham S, Fox T, Moore T, Levey A, Brown A, Kippels A, Ellison A, Lyons C, Hales C, Parry C, Williams C, McCorkle E, Harris G, Rose H, Jooma I, Al-Amin J, Lah J, Webster J, Swiniarski J, Chapman L, Donnelly L, Mariotti L, Locke M, Vaughn P, Penn R, Carpentier S, Yeboah S, Basadre S, Malakauskas S, Lyron S, Villinger T, Burney T, Burns J, Abusalim A, Dahlgren A, Montero A, Arthur A, Dooly H, Kreszyn K, Berner K, Gillen L, Scanlan M, Madison M, Mathis N, Switzer P, Townley R, Fikru S, Sullivan S, Wright E, Beigi M, Daley A, Ko A, Luong B, Nyborg G, Morales J, Durbin K, Garcia L, Parand L, Macias L, Monserratt L, Farchi M, Wu P, Hernandez R, Rodriguez T, Clinic M, Graff-Radford N, Marolt A, Thomas A, Aloszka D, Moncayo E, Westerhold E, Day G, Chrestensen K, Imhansiemhonehi M, McKinzie S, Stephens S, Grant S, Brosch J, Perkins A, Saunders A, Kovac D, Polson H, Mwaura I, Mejia K, Britt K, King K, Nichols K, Lawrence K, Rankin L, Farlow M, Wiesenauer P, Bryant R, Herring S, Lynch S, Wilson S, Day T, Korst W, van Dyck C, Mecca A, Miller A, Brennan A, Khan A, Ruan A, Gunnoud C, Mendonca C, Raynes-Goldfinger D, Salardini E, Hidalgo E, Cooper E, Singh E, Murphy E, May J, Stanhope J, Lam J, Waszak J, Nelsen K, Sacaza K, Hasbani M, Donahue M, Chen M, Barcelos N, Eigenberger P, Bonomi R, O’Dell R, Jefferson S, Khasnavis S, Smilowitz S, DeStefano S, Good S, Camarro T, Clayton V, Cavrel Y, Lu Y, Chertkow H, Bergman H, Hosein C, Black S, Kapadia A, Bhan A, Lam B, Scott C, Gabriel G, Bray J, Zotovic L, Gutierrez M, Masellis M, Farshadi M, Gui M, Mitchell M, Taylor R, Endre R, Taghi-Zada Z, Hsiung R, English C, Kim E, Yau E, Tong H, Barlow L, Jennings L, Assaly M, Nunes P, Marian T, Kertesz A, Rogers J, Trost D, Wint D, Bernick C, Munic D, Grant I, Korkoyah A, Raja A, Lapins A, Ryan C, Pejic J, Basham K, Lukose L, Haddad L, Quinlan L, Houghtaling N, Sadowsky C, Martinez W, Villena T, Reynolds B, Forero A, Ward C, Brennan E, Figueroa E, Esposito G, Mallory J, Johnson K, Turner K, Seidenberg K, McCann K, Bassett M, Chadwick M, Turner R, Bean R, Sharma S, Marshall G, Haviari A, Pietras A, Wallace B, Munro C, Rivera-Delpin G, Hustead H, Levesque I, Ramirez J, Nolan K, Glennon K, Palou M, Erkkinen M, DaSilva N, Friedman P, Silver R, Salazar R, Polleys R, McGinnis S, Gale S, Hall T, Luu T, Chao S, Lin E, Coleman J, Epperson K, Vasanawala M, Atri A, Rangel A, Evans B, Monarrez C, Cline C, Liebsack C, Bandy D, Goldfarb D, Intorcia D, Olgin J, Clark K, King K, York K, Reade M, Callan M, Glass M, Johnson M, Gutierrez M, Goddard M, Trncic N, Choudhury P, Reyes P, Lowery S, Hall S, Olgin S, de Santiago S, Alosco M, Ton A, Jimenez A, Ellison A, Tran A, Anderson B, Carter D, Veronelli D, Lenio S, Steinberg E, Mez J, Weller J, Johns J, Mez J, Harkins J, Puleio A, Hoti I, Mwicigi J, Puleio A, Alosco M, Schultz O, Lauture M, Steinberg E, Denis R, Killiany R, Singh S, Lenio S, Qiu W, Devis Y, Obisesan T, Stone A, Ordor D, Udodong I, Okonkwo I, Khan J, Turner J, Hughes K, Kadiri O, Duffy C, Moss A, Stapleton K, Toth M, Sanders M, Ayres M, Hamski M, Fatica P, Ogrocki P, Ash S, Pot S, Chen D, Soto A, Tanase C, Bissig D, Vanya H, Russell H, Patel H, Zhang H, Wallace K, Ayers K, Gallegos M, Forloines M, Sinn M, Kahulugan Q, Isip R, Calderon S, Hamm T, Borrie M, Lee T, Bartha R, Johnson S, Asthana S, Carlsson C, Perrin A, Tariot P, Fleisher A, Reeder S, Capote H, Emborsky A, Mattle A, Ajtai B, Wagner B, Myers B, Slazyk D, Fragale D, Fransen E, Macnamara H, Falletta J, Hirtreiter J, Mechtler L, King M, Asbach M, Rainka M, Zawislak R, Wisniewski S, O’Malley S, Jimenez-Knight T, Peehler T, Aladeen T, Bates V, Wenner V, Elmalik W, Scharre D, Ramamurthy A, Bouchachi S, Kataki M, Tarawneh R, Kelley B, Celmins D, Leader A, Figueroa C, Bauerle H, Patterson K, Reposa M, Presto S, Ahmed T, Stewart W, Hosp H, Pearlson G, Blank K, Anderson K, Santulli R, Schwartz E, Williamson J, Jessup A, Williams A, Duncan C, O’Connell A, Gagnon K, Zamora E, Bateman J, Crawford F, Thompson D, Walker E, Rowell J, White M, Ledford P, Bohlman S, Henkle S, Bottoms J, Moretz L, Hoover B, Shannon M, Rogers S, Baker W, Harrison W, Wu C, DeMarco A, Stipanovich A, Arcuri D, Clark J, Davis J, Doyon K, Amoyaw M, Acosta M, Bailey R, Warren S, Fogerty T, Sanborn V, Riddle M, Salloway S, Malloy P, Correia S, Windon C, Blackburn M, Rosen H, Miller B, Smith A, Mba I, Echevarria J, Janavs J, Roglaski E, Yong M, Devine R, Okhravi H, Rivera E, Kalowsky T, Smith C, Rosario C, Masdeu J, Le R, Gurung M, Sabbagh M, Garcia A, Slaughter M, Elayan N, Acothley S, Pomara N, Hernando R, Pomara V, Reichert C, Brawman-Mintzer O, Acree A, Williams A, Long C, Long R, Newhouse P, Hill S, Boegel A, Seshadri S, Saklad A, Jones F, Hu W, Sotelo V, Rojas Y, Mintzer J, Longmire C, Spicer K. Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2411492122. PMID: 39977323, PMCID: PMC11873959, DOI: 10.1073/pnas.2411492122.Peer-Reviewed Original ResearchMeSH KeywordsAgingBrainEmbryonic DevelopmentHumansImage Processing, Computer-AssistedLongitudinal StudiesMachine LearningWound HealingConceptsFully connected layerLearning-based methodsMachine learning-based methodsSignal-of-interestImage dataSiamese architectureLongitudinal imaging dataBaseline methodsFeature pairsTime-varying signalsImage differencesSquare errorPatient monitoringRoot mean square errorTraditional methodsOrder accuracyTime differenceCustom pipelineIrrelevant changesTemporal orderEmpirical resultsImagesArchitectureIndividual-level changeMachineUnbiased CSF Proteomics in Patients With Idiopathic Normal Pressure Hydrocephalus to Identify Molecular Signatures and Candidate Biomarkers
de Geus M, Wu C, Dodge H, Leslie S, Wang W, Lam T, Kahle K, Chan D, Kivisäkk P, Nairn A, Arnold S, Carlyle B. Unbiased CSF Proteomics in Patients With Idiopathic Normal Pressure Hydrocephalus to Identify Molecular Signatures and Candidate Biomarkers. Neurology 2025, 104: e213375. PMID: 39951680, PMCID: PMC11837848, DOI: 10.1212/wnl.0000000000213375.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAlzheimer DiseaseBiomarkersFemaleHumansHydrocephalus, Normal PressureMachine LearningMaleMiddle AgedProteomicsConceptsNeuronal pentraxin receptorIdiopathic normal pressure hydrocephalusTranscriptome dataAlzheimer's diseaseDifferential expression of proteinsGene ontology analysisDifferential expression analysisGene set enrichment analysisDownregulation of proteinsDifferentially expressed proteinsNormal pressure hydrocephalusBiological process enrichmentExpression of proteinsPotential disease biomarkersOntology analysisProteomic analysis of CSFPathophysiology of idiopathic normal pressure hydrocephalusProteomic analysisProteomic studiesProcess of immune responseEnrichment analysisExpression analysisPressure hydrocephalusDifferential expressionDiagnosis of idiopathic normal pressure hydrocephalusClassification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression
Aboharb F, Davoudian P, Shao L, Liao C, Rzepka G, Wojtasiewicz C, Indajang J, Dibbs M, Rondeau J, Sherwood A, Kaye A, Kwan A. Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression. Nature Communications 2025, 16: 1590. PMID: 39939591, PMCID: PMC11822132, DOI: 10.1038/s41467-025-56850-6.Peer-Reviewed Original ResearchConceptsAcute fluoxetinePsychoactive drugsMarkers of neural plasticityImmediate early gene expressionC-fos expressionChronic fluoxetineNative brain tissueBehavioral effectsPsychedelic propertiesBrain regionsChance levelEarly gene expressionPsychoactive compoundsNeural plasticityFluoxetinePsilocybinMDMAMeasuring drug actionTested malesPsychedelicsPreclinical assaysDrug actionKetamineFemale miceDrug classificationEvaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems
Aminorroaya A, Dhingra L, Oikonomou E, Khera R. Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems. Circulation Genomic And Precision Medicine 2025, 18: e004632. PMID: 39846171, PMCID: PMC11835527, DOI: 10.1161/circgen.124.004632.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAtherosclerosisElectronic Health RecordsFemaleHumansLipoprotein(a)Machine LearningMaleMass ScreeningMiddle AgedRisk FactorsConceptsYale New Haven Health SystemHealth systemVanderbilt University Medical CenterHealth system electronic health recordUniversity Medical CenterCoronary Artery Risk DevelopmentMulti-Ethnic Study of AtherosclerosisElectronic health recordsMedical CenterUS health systemHealth system patientsAssociated with significantly higher oddsMulti-Ethnic StudyUS-based cohortStudy of AtherosclerosisSignificantly higher oddsHealth recordsUK BiobankAtherosclerosis RiskRisk DevelopmentHigher oddsElevated Lp(aUniversal screeningSystem patientsStudy cohortPrediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis
Nishibe T, Iwasa T, Matsuda S, Kano M, Akiyama S, Fukuda S, Koizumi J, Nishibe M, Dardik A. Prediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis. Journal Of Surgical Research 2025, 306: 197-202. PMID: 39793306, DOI: 10.1016/j.jss.2024.11.049.Peer-Reviewed Original ResearchConceptsAneurysm sac shrinkageEndovascular aortic repairAbdominal aortic aneurysmSac shrinkageType II endoleakAortic repairII endoleakUnivariate analysisElective endovascular aortic repairTokyo Medical University HospitalMedical University HospitalDecision tree analysisPulse wave velocityAortic aneurysmUniversity HospitalCurrent smokingAneurysmPatientsStratification modelEndoleakLow likelihoodVariables of ageSmokingRepairDifferential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females
Heyn S, Keding T, Cisler J, McLaughlin K, Herringa R. Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females. Scientific Reports 2025, 15: 651. PMID: 39753729, PMCID: PMC11698963, DOI: 10.1038/s41598-024-84616-5.Peer-Reviewed Original ResearchConceptsGray matter volumeVoxel-based morphometryInternalizing psychopathologyChildhood abuseAbuse experiencesPrefrontal cortexCingulate cortexAssociated with increased GMVVoxel-based morphometry analysisInterpersonal violenceDorsal prefrontal cortexDevelopment of psychopathologyAnterior cingulate cortexChildhood abuse historyChildhood abuse exposureT1 structural MRIDifferentiating gray matterPredictive of abuseAdolescent femalesSeverity of abuseDegree of overlapSupramarginal gyrusTrauma exposureStudy of interpersonal violenceIndividual psychopathologyMachine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor
Yu G, Wang X, Luo Y, Li G, Ding R, Shi R, Huo X, Yang Y. Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor. Journal Of Chemical Information And Modeling 2025, 65: 312-325. PMID: 39744764, DOI: 10.1021/acs.jcim.4c02120.Peer-Reviewed Original ResearchConceptsAllylic substitutionReaction outcomeDensity functional theory calculationsAllylic substitution reactionsPredictions of reaction outcomesFunctional theory calculationsOrganic synthesis fieldSubstitution reactionMachine learningCatalyst optimizationTheory calculationsReaction mechanismMolecular propertiesSynthesis fieldAtomic levelAllylationGraph matching algorithmReactionExtract essential informationAtomic featuresMainstream descriptorsMatching algorithmSubstrate combinationsAutomatic extractionDescriptors
2024
Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction
Huang X, Arora J, Erzurumluoglu A, Stanhope S, Lam D, Arora J, Erzurumluoglu A, Lam D, Khoueiry P, Jensen J, Cai J, Lawless N, Kriegl J, Ding Z, de Jong J, Zhao H, Ding Z, Wang Z, de Jong J. Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction. Journal Of The American Medical Informatics Association 2024, 32: 435-446. PMID: 39723811, PMCID: PMC11833479, DOI: 10.1093/jamia/ocae297.Peer-Reviewed Original ResearchConceptsElectronic health recordsDisease risk predictionElectronic health record researchFamily health historyGenetic aspects of diseaseRisk predictionInflammatory bowel disease subtypeHealth recordsHealth historyAspects of diseaseFamily relationsHealthcare researchPatient's disease riskInfluence of geneticsDisease riskDiagnosis dataFamily pedigreeEnvironmental exposuresRisk factorsDisease dependencyPatient representation learningClinical profileFamilyDisease subtypesRiskPortable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease
Sorby-Adams A, Guo J, Laso P, Kirsch J, Zabinska J, Garcia Guarniz A, Schaefer P, Payabvash S, de Havenon A, Rosen M, Sheth K, Gomez-Isla T, Iglesias J, Kimberly W. Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease. Nature Communications 2024, 15: 10488. PMID: 39622805, PMCID: PMC11612292, DOI: 10.1038/s41467-024-54972-x.Peer-Reviewed Original ResearchConceptsWhite matter hyperintensitiesMachine learning pipelineMild cognitive impairmentAlzheimer's diseaseWhite matter hyperintensities volumeLearning pipelineAssessment of patientsIncrease accessCognitive impairmentEvaluation of Alzheimer's diseaseDementiaLF-MRIPoint-of-care assessmentMagnetic resonance imagingHippocampal volumeResonance imagingImage qualityDiseaseReduce costsAnisotropic counterpartIncreasing availabilityManual segmentationMachine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines
Kovalchik K, Hamelin D, Kubiniok P, Bourdin B, Mostefai F, Poujol R, Paré B, Simpson S, Sidney J, Bonneil É, Courcelles M, Saini S, Shahbazy M, Kapoor S, Rajesh V, Weitzen M, Grenier J, Gharsallaoui B, Maréchal L, Wu Z, Savoie C, Sette A, Thibault P, Sirois I, Smith M, Decaluwe H, Hussin J, Lavallée-Adam M, Caron E. Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines. Nature Communications 2024, 15: 10316. PMID: 39609459, PMCID: PMC11604954, DOI: 10.1038/s41467-024-54734-9.Peer-Reviewed Original ResearchConceptsT cell epitopesT cellsCD8+ T cell epitopesT cell immunityT cell epitope discoverySARS-CoV-2T-cell-directed vaccinationDesigning effective vaccinesB7 supertypePatient's proteomesSARS-CoV-2 variantsVaccine epitopesViral antigensSpike antigenVaccine developmentEffective vaccineEpitope discoveryCOVID-19 vaccineVaccineEpitopesAntigenic featuresOmicron variantAntigenCOVID-19CD8Brain age prediction and deviations from normative trajectories in the neonatal connectome
Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, Scheinost D. Brain age prediction and deviations from normative trajectories in the neonatal connectome. Nature Communications 2024, 15: 10251. PMID: 39592647, PMCID: PMC11599754, DOI: 10.1038/s41467-024-54657-5.Peer-Reviewed Original ResearchConceptsPostmenstrual agePerinatal periodBrain age predictionFunctional connectomeMonths of postnatal lifeMonths of lifePreterm infantsNormative trajectoryConnectome-based predictive modelingThird trimesterPerinatal exposureBrain age gapPostnatal lifeResting-state fMRIInfantsHuman Connectome ProjectNeonatal connectomeDevelopmental trajectoriesBrainBehavioral outcomesNormative dataMonthsConnectome ProjectDTI dataConnectomeImaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Rahaman A, Garg Y, Iraji A, Fu Z, Kochunov P, Hong L, Van Erp T, Preda A, Chen J, Calhoun V. Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders. Human Brain Mapping 2024, 45: e26799. PMID: 39562310, PMCID: PMC11576332, DOI: 10.1002/hbm.26799.Peer-Reviewed Original ResearchConceptsNeural networkDilated convolutional neural networkJoint learning frameworkAttention scoresState-of-the-artDeep neural networksNeural network decisionsConvolutional neural networkAttention fusionFusion moduleDiverse data sourcesArtificial intelligence modelsLearning frameworkAttention moduleJoint learningMultimodal clusteringNetwork decisionsInput streamMultimodal learningHigh-dimensionalIntermediate fusionFused dataSZ classificationIntelligence modelsContextual patternsA simple but tough-to-beat baseline for fMRI time-series classification
Popov P, Mahmood U, Fu Z, Yang C, Calhoun V, Plis S. A simple but tough-to-beat baseline for fMRI time-series classification. NeuroImage 2024, 303: 120909. PMID: 39515403, PMCID: PMC11625415, DOI: 10.1016/j.neuroimage.2024.120909.Peer-Reviewed Original ResearchMeSH KeywordsBrainBrain MappingHumansImage Processing, Computer-AssistedMachine LearningMagnetic Resonance ImagingConceptsComplex machine learning modelsBlack-box natureMulti-layer perceptronMachine learning modelsPrediction accuracyBlack-box modelsFMRI classificationComplex classifiersClassification accuracySequential informationHuman fMRI dataLearning modelsBlack-boxRich modelsSuperior performanceComplex model developmentFMRI dataTime-series fMRI dataTime series dataClassifierStand-alone pieceClassificationAccuracyDesign modelSeries dataFeature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022
Fang W, Liu Y, Xu C, Luo X, Wang K. Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022. International Journal Of Environmental Research And Public Health 2024, 21: 1474. PMID: 39595741, PMCID: PMC11594230, DOI: 10.3390/ijerph21111474.Peer-Reviewed Original ResearchConceptsSupport vector machineFeature selectionMachine learningRandom forestCollection of featuresMachine learning approachImbalance dataF1 scoreVector machineML techniquesLearning approachML toolsRelevant featuresPatient Health Questionnaire-4E-cigarette useML modelsML approachesRF algorithmRandom oversampling examplesMachineAlgorithmE-cigarettesSelection operatorLogistic regressionHealth Information National Trends SurveyModel selection to achieve reproducible associations between resting state EEG features and autism
Carson W, Major S, Akkineni H, Fung H, Peters E, Carpenter K, Dawson G, Carlson D. Model selection to achieve reproducible associations between resting state EEG features and autism. Scientific Reports 2024, 14: 25301. PMID: 39455733, PMCID: PMC11511871, DOI: 10.1038/s41598-024-76659-5.Peer-Reviewed Original ResearchMeSH KeywordsAutistic DisorderBrainChildChild, PreschoolElectroencephalographyFemaleHumansMachine LearningMaleReproducibility of ResultsRestConceptsElectroencephalography spectral powerCustom machine learning modelsPredictive performanceGamma powerMachine learning modelsRegularized generalized linear modelModel selectionBiomarker discoverySpectral powerMidline regionMultiple featuresLearning modelsFunctional connectivity featuresPosterior midline regionsSDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Wei H, Justet A, Adams T, Homer R, Amei A, Rosas I, Kaminski N, Wang Z, Yan X. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 2024, 25: 271. PMID: 39402626, PMCID: PMC11475911, DOI: 10.1186/s13059-024-03416-2.Peer-Reviewed Original Research
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