2025
The application of irreversible genomic states to define and trace ancient cell type homologies
Simakov O, Wagner G. The application of irreversible genomic states to define and trace ancient cell type homologies. EvoDevo 2025, 16: 5. PMID: 40319312, PMCID: PMC12049793, DOI: 10.1186/s13227-025-00242-w.Peer-Reviewed Original ResearchImpact gene expressionGenomic stateCell typesGene expressionGene regulatory networksHomologous cell typesBranching animalsGenomic charactersRegulatory elementsRegulatory networksRegulatory signaturesMorphological traitsHomologyPhenotypic levelMolecular signaturesOntogenetic originGenesMorphological featuresNon-functionalCellsHypothesis articleGenomeExpressionTraitsType
2024
Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features
Lanino L, D'Amico S, Maggioni G, Al Ali N, Wang Y, Gurnari C, Gagelmann N, Bewersdorf J, Ball S, Guglielmelli P, Meggendorfer M, Hunter A, Kubasch A, Travaglino E, Campagna A, Ubezio M, Russo A, Todisco G, Tentori C, Buizza A, Sauta E, Zampini M, Riva E, Asti G, Delleani M, Ficara F, Santoro A, Sala C, Dall'Olio D, Dall'Olio L, Kewan T, Casetti I, Awada H, Xicoy B, Vucinic V, Hou H, Chou W, Yao C, Lin C, Tien H, Consagra A, Sallman D, Kern W, Bernardi M, Chiusolo P, Borin L, Voso M, Pleyer L, Palomo L, Quintela D, Jerez A, Cornejo E, Martin P, Díaz-Beyá M, Pita A, Roldan V, Suarez D, Velasco E, Calabuig M, Garcia-Manero G, Loghavi S, Platzbecker U, Sole F, Diez-Campelo M, Maciejewski J, Kröger N, Fenaux P, Fontenay M, Santini V, Haferlach T, Germing U, Padron E, Robin M, Passamonti F, Solary E, Vannucchi A, Castellani G, Zeidan A, Komrokji R, Della Porta M. Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features. Blood 2024, 144: 1005. DOI: 10.1182/blood-2024-204826.Peer-Reviewed Original ResearchGenomic featuresSplicing mutationBiallelic inactivationAnalysis of genomic profilesBiallelic inactivation of TP53Clinical phenotypeGene expression profilesCNV analysisMorphological featuresInactivation of TP53Myeloid neoplasmsGenomic characterizationRNAseq dataMorphological dataMutation screeningExpression profilesMutationsJAK/STATGenomic profilingGenomeHierarchical importanceHeterogeneous phenotypesIntegrated analysisPhenotypeHematological phenotypeArtificial Intelligence-Powered Digital Pathology to Improve Diagnosis and Personalized Prognostic Assessment in Patient with Myeloid Neoplasms
Asti G, Curti N, Maggioni G, Carlini G, Lanino L, Campagna A, D'Amico S, Sauta E, Delleani M, Bonometti A, Lancellotti C, Rahal D, Ubezio M, Todisco G, Tentori C, Russo A, Crespi A, Figini G, Buizza A, Riva E, Zampini M, Brindisi M, Ficara F, Crisafulli L, Ventura D, Pinocchio N, Zazzetti E, Bicchieri M, Grondelli M, Forcina Barrero A, Savevski V, Santoro A, Santini V, Sole F, Platzbecker U, Fenaux P, Diez-Campelo M, Komrokji R, Haferlach T, Kordasti S, Di Tommaso L, Zeidan A, Loghavi S, Garcia-Manero G, Castellani G, Della Porta M. Artificial Intelligence-Powered Digital Pathology to Improve Diagnosis and Personalized Prognostic Assessment in Patient with Myeloid Neoplasms. Blood 2024, 144: 3598-3598. DOI: 10.1182/blood-2024-206248.Peer-Reviewed Original ResearchLeukemia-free survivalMyeloid neoplasmsOverall survivalConcordance indexGenomic informationBone marrowPredictive of overall survivalMD Anderson Cancer CenterCell typesProportion of patientsHarrell's concordance indexSomatic gene mutationsMorphological featuresHumanitas Research HospitalGenomic dataMGG smearsPersonalized risk assessmentRUNX1 mutationsBM aspiratesClinically relevant informationClinical entityBiopsy dataMN patientsPrognostic assessmentWhole slide imagesMulti-modal deep learning from imaging genomic data for schizophrenia classification
Kanyal A, Mazumder B, Calhoun V, Preda A, Turner J, Ford J, Ye D. Multi-modal deep learning from imaging genomic data for schizophrenia classification. Frontiers In Psychiatry 2024, 15: 1384842. PMID: 39006822, PMCID: PMC11239396, DOI: 10.3389/fpsyt.2024.1384842.Peer-Reviewed Original ResearchSingle nucleotide polymorphismsGenomic dataGenetic markersGenomic markersBrains of individualsNucleotide polymorphismsEtiology of SZFunctional magnetic resonance imagingStructural magnetic resonance imagingMorphological featuresLayerwise relevance propagationHereditary aspectsHealthy controlsMarkersSenNet recommendations for detecting senescent cells in different tissues
Suryadevara V, Hudgins A, Rajesh A, Pappalardo A, Karpova A, Dey A, Hertzel A, Agudelo A, Rocha A, Soygur B, Schilling B, Carver C, Aguayo-Mazzucato C, Baker D, Bernlohr D, Jurk D, Mangarova D, Quardokus E, Enninga E, Schmidt E, Chen F, Duncan F, Cambuli F, Kaur G, Kuchel G, Lee G, Daldrup-Link H, Martini H, Phatnani H, Al-Naggar I, Rahman I, Nie J, Passos J, Silverstein J, Campisi J, Wang J, Iwasaki K, Barbosa K, Metis K, Nernekli K, Niedernhofer L, Ding L, Wang L, Adams L, Ruiyang L, Doolittle M, Teneche M, Schafer M, Xu M, Hajipour M, Boroumand M, Basisty N, Sloan N, Slavov N, Kuksenko O, Robson P, Gomez P, Vasilikos P, Adams P, Carapeto P, Zhu Q, Ramasamy R, Perez-Lorenzo R, Fan R, Dong R, Montgomery R, Shaikh S, Vickovic S, Yin S, Kang S, Suvakov S, Khosla S, Garovic V, Menon V, Xu Y, Song Y, Suh Y, Dou Z, Neretti N. SenNet recommendations for detecting senescent cells in different tissues. Nature Reviews Molecular Cell Biology 2024, 25: 1001-1023. PMID: 38831121, PMCID: PMC11578798, DOI: 10.1038/s41580-024-00738-8.Peer-Reviewed Original ResearchSenescent cellsDetect senescent cellsIrreversible cell cycle arrestCellular senescenceCell cycle arrestSenescence markersBiomarker Working GroupCycle arrestCellular senescence markersBiological processesCell biologyPostmitotic cellsSenescent phenotypeCirculating MarkersTissue culture studiesSenescence signatureSenescenceCellsMorphological featuresDetrimental roleTissueMarkersSeasonal investigation
2023
Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification
Kanyal A, Kandula S, Calhoun V, Ye D. Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193352.Peer-Reviewed Original ResearchSingle nucleotide polymorphismsSZ patientsFunctional network connectivityFunctional MRIStructural MRIFunctional brain connectivityGenetic markersChronic mental conditionsNucleotide polymorphismsBrain connectivity featuresDecreased hippocampalGenetic featuresSZ subjectsMorphological changesThalamic volumeBrain connectivitySZ diagnosisGenetic illnessMental conditionLayer-wise relevance propagationMorphological featuresBrainConnectivity featuresSZ
2018
The 7q11.23 Protein DNAJC30 Interacts with ATP Synthase and Links Mitochondria to Brain Development
Tebbenkamp ATN, Varela L, Choi J, Paredes MI, Giani AM, Song JE, Sestan-Pesa M, Franjic D, Sousa AMM, Liu ZW, Li M, Bichsel C, Koch M, Szigeti-Buck K, Liu F, Li Z, Kawasawa YI, Paspalas CD, Mineur YS, Prontera P, Merla G, Picciotto MR, Arnsten AFT, Horvath TL, Sestan N. The 7q11.23 Protein DNAJC30 Interacts with ATP Synthase and Links Mitochondria to Brain Development. Cell 2018, 175: 1088-1104.e23. PMID: 30318146, PMCID: PMC6459420, DOI: 10.1016/j.cell.2018.09.014.Peer-Reviewed Original ResearchConceptsCopy number variationsATP synthase dimersOxidative phosphorylation supercomplexesHuman neurodevelopmental disordersATP synthaseWS pathogenesisGene contributionMitochondrial featuresBrain developmentWilliams syndromeMitochondrial dysfunctionNeocortical pyramidal neuronsNeural phenotypesMitochondriaPyramidal neuronsMachineryMorphological featuresNeurodevelopmental disordersDysfunctionSupercomplexesPhenotype
2014
Automated quantitative multiplex immunofluorescence in situ imaging identifies phospho-S6 and phospho-PRAS40 as predictive protein biomarkers for prostate cancer lethality
Shipitsin M, Small C, Giladi E, Siddiqui S, Choudhury S, Hussain S, Huang YE, Chang H, Rimm DL, Berman DM, Nifong TP, Blume-Jensen P. Automated quantitative multiplex immunofluorescence in situ imaging identifies phospho-S6 and phospho-PRAS40 as predictive protein biomarkers for prostate cancer lethality. Proteome Science 2014, 12: 40. PMID: 25075204, PMCID: PMC4114438, DOI: 10.1186/1477-5956-12-40.Peer-Reviewed Original ResearchProtein levelsPost-translational modificationsProtein-based approachGene-based approachesIntact tissue specimensProtein activityProtein signaturesHuman prostate cancerMolecular informationPredictive protein biomarkersProtein biomarker levelsPhospho-PRAS40Phospho-S6Prostate cancer lethalityTissue lysisSPP1PTENIntact tissueSmad4Cancer lethalityPhenotypeCCND1Morphological featuresProtein biomarkersFunctional activity
2001
Automated measurement of latent morphological features in the human corpus callosum
Peterson B, Feineigle P, Staib L, Gore J. Automated measurement of latent morphological features in the human corpus callosum. Human Brain Mapping 2001, 12: 232-245. PMID: 11241874, PMCID: PMC6871880, DOI: 10.1002/1097-0193(200104)12:4<232::aid-hbm1018>3.0.co;2-j.Peer-Reviewed Original ResearchConceptsCorpus callosumSubject characteristicsYears of ageHuman corpus callosumPatient groupCallosum sizeHealthy subjectsMRI scansVentricular volumeCallosumMidsagittal planeFactor scoresNeural correlatesConstruct validityFactor-based analysisConventional measuresFuture studiesMorphological featuresAgePredictive validitySubjectsScoresNormal developmentVarimax rotationFactors
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