2017
Mixture Modeling of 2-D Gel Electrophoresis Spots Enhances the Performance of Spot Detection
Marczyk M. Mixture Modeling of 2-D Gel Electrophoresis Spots Enhances the Performance of Spot Detection. IEEE Transactions On NanoBioscience 2017, 16: 91-99. PMID: 28278480, DOI: 10.1109/tnb.2017.2676725.Peer-Reviewed Original Research
2016
Improved Detection of 2D Gel Electrophoresis Spots by Using Gaussian Mixture Model
Marczyk M. Improved Detection of 2D Gel Electrophoresis Spots by Using Gaussian Mixture Model. Lecture Notes In Computer Science 2016, 9683: 284-294. DOI: 10.1007/978-3-319-38782-6_24.Peer-Reviewed Original ResearchParallel computing capabilitiesSpot detectionGaussian mixture modelGel imagesComputing capabilitiesAutomatic methodEfficient implementationComputational timeBest overall performanceMixture modelAbstract2D gel electrophoresisOverall performanceImagesTrue positionDetectionAlgorithmDatasetSoftwareImplementationCapabilityMethodThousandsAccurate estimates
2015
Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry
Polanski A, Marczyk M, Pietrowska M, Widlak P, Polanska J. Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry. PLOS ONE 2015, 10: e0134256. PMID: 26230717, PMCID: PMC4521892, DOI: 10.1371/journal.pone.0134256.Peer-Reviewed Original ResearchConceptsGaussian mixture modelingMixture modelGaussian mixture modelPeak detectionProteomic mass spectraFeature extractionPartitioning algorithmEfficient algorithmSignal compressionSoftware packageAlgorithmMain ideaMixture modelingModeling approachModelingDatasetApplicationsDetectionPreliminary resultsDetection efficiencyProteomic datasetsMixture modeling approachModelDifferent typesHigh resolution