2021
Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes
Durant TJS, Dudgeon SN, McPadden J, Simpson A, Price N, Schulz WL, Torres R, Olson EM. Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes. Clinical Chemistry 2021, 68: 218-229. PMID: 34969114, DOI: 10.1093/clinchem/hvab237.Peer-Reviewed Original Research
2017
Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes
Durant T, Olson EM, Schulz WL, Torres R. Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes. Clinical Chemistry 2017, 63: 1847-1855. PMID: 28877918, DOI: 10.1373/clinchem.2017.276345.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkDeep convolutional neural networkDense shortcut connectionsNeural network designSlow manual processSignificant labor costsClassification of erythrocytesWeb applicationCapable machinesUnseen dataShortcut connectionsManual processEnsemble model predictionsDigital imagesPrecision metricsArchitectural considerationsNetwork designAutomated profilingClassification frequencyMisclassification errorPractical performanceFinal databaseHarmonic meanNetwork
2020
Impact of intra-tumoral heterogeneity detected by next-generation sequencing on acute myeloid leukemia survival
Schulz WL, Rinder HM, Durant TJS, Tormey CA, Torres R, Smith BR, Hager KM, Howe JG, Siddon AJ. Impact of intra-tumoral heterogeneity detected by next-generation sequencing on acute myeloid leukemia survival. Leukemia & Lymphoma 2020, 61: 3269-3271. PMID: 32715805, DOI: 10.1080/10428194.2020.1797016.Peer-Reviewed Original Research
2016
Evaluation of relational and NoSQL database architectures to manage genomic annotations
Schulz WL, Nelson BG, Felker DK, Durant TJ, Torres R. Evaluation of relational and NoSQL database architectures to manage genomic annotations. Journal Of Biomedical Informatics 2016, 64: 288-295. PMID: 27810480, DOI: 10.1016/j.jbi.2016.10.015.Peer-Reviewed Original ResearchConceptsRelational databaseNoSQL databasesQuery efficiencyData managementData storageDocument-oriented NoSQL databaseBiomedical data setsCommon relational databaseQuery retrievalDatabase technologyInformatics infrastructureRelational modelGenomic annotationsIndexingData setsArchitectureRetrievalAnnotationNew technologiesDatabaseTechnologySignificant advantagesRelative advantagesInfrastructureStorageUse of application containers and workflows for genomic data analysis
Schulz WL, Durant TJ, Siddon AJ, Torres R. Use of application containers and workflows for genomic data analysis. Journal Of Pathology Informatics 2016, 7: 53. PMID: 28163975, PMCID: PMC5248400, DOI: 10.4103/2153-3539.197197.Peer-Reviewed Original ResearchBig data processingGenomic data analysisSoftware deploymentApplication virtualizationApplication containersCommon analytic toolSoftware applicationsScalable platformData processingNext-generation sequencing dataComputational experimentsAnalytic toolsRecent technologiesNovel approachBiological dataNGS dataIntensive analysisAmount of timeData analysisVirtualizationDockerSuccessful implementationDevelopersBioinformaticiansWorkflow
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