2023
Proteomic-based stratification of intermediate-risk prostate cancer patients
Zhong Q, Sun R, Aref A, Noor Z, Anees A, Zhu Y, Lucas N, Poulos R, Lyu M, Zhu T, Chen G, Wang Y, Ding X, Rutishauser D, Rupp N, Rueschoff J, Poyet C, Hermanns T, Fankhauser C, Martínez M, Shao W, Buljan M, Neumann J, Beyer A, Hains P, Reddel R, Robinson P, Aebersold R, Guo T, Wild P. Proteomic-based stratification of intermediate-risk prostate cancer patients. Life Science Alliance 2023, 7: e202302146. PMID: 38052461, PMCID: PMC10698198, DOI: 10.26508/lsa.202302146.Peer-Reviewed Original ResearchConceptsGleason grade groupBiochemical recurrenceRisk of biochemical recurrenceIntermediate-risk patientsNon-aggressive diseaseProstate cancer managementProstate cancer patientsMultivariate Cox regressionHigh-risk groupPatient treatment decisionsProstatic adenocarcinomaGleason gradePrognostic indicatorMatched tumorCancer managementCox regressionCancer patientsGrade groupTreatment decisionsPatientsSurvival analysisRisk scoreClinical applicationGleasonProstate
2021
Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial
Čuklina J, Lee C, Williams E, Sajic T, Collins B, Rodríguez Martínez M, Sharma V, Wendt F, Goetze S, Keele G, Wollscheid B, Aebersold R, Pedrioli P. Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial. Molecular Systems Biology 2021, 17: msb202110240. PMID: 34432947, PMCID: PMC8447595, DOI: 10.15252/msb.202110240.Peer-Reviewed Original ResearchConceptsBatch effectsProteomic studiesLarge-scale proteomic studiesCorrection of batch effectsMass spectrometry-based proteomicsStep-by-step protocolProteomic datasetsProteomic dataSystems biologyBatch correctionMultiple experimental designsProteomic ChallengeR packageProteomicsClinical proteomicsBiological signalsTechnical variabilityStatistical powerIntensity driftBiologyOn the feasibility of deep learning applications using raw mass spectrometry data
Cadow J, Manica M, Mathis R, Reddel R, Robinson P, Wild P, Hains P, Lucas N, Zhong Q, Guo T, Aebersold R, Martínez M. On the feasibility of deep learning applications using raw mass spectrometry data. Bioinformatics 2021, 37: i245-i253. PMID: 34252933, PMCID: PMC8275322, DOI: 10.1093/bioinformatics/btab311.Peer-Reviewed Original ResearchConceptsRaw mass spectrometry dataDeep learning modelsRaw MS dataMass spectrometry dataClassification performanceDeep learningMS dataMass spectrometryLearning modelsSpectrometry dataApplication of deep learningMS imagesNatural image classificationDeep learning applicationsPrivacy of individualsTransfer learning techniqueData-independent-acquisitionMS2 spectraClassification taskData processing pipelinesClassification labelsImage classificationFeature vectorTransfer learningSample sparsity
2020
Convergent network effects along the axis of gene expression during prostate cancer progression
Charmpi K, Guo T, Zhong Q, Wagner U, Sun R, Toussaint N, Fritz C, Yuan C, Chen H, Rupp N, Christiansen A, Rutishauser D, Rüschoff J, Fankhauser C, Saba K, Poyet C, Hermanns T, Oehl K, Moore A, Beisel C, Calzone L, Martignetti L, Zhang Q, Zhu Y, Martínez M, Manica M, Haffner M, Aebersold R, Wild P, Beyer A. Convergent network effects along the axis of gene expression during prostate cancer progression. Genome Biology 2020, 21: 302. PMID: 33317623, PMCID: PMC7737297, DOI: 10.1186/s13059-020-02188-9.Peer-Reviewed Original ResearchConceptsHigh-throughput genomic measurementsProstate cancer progressionGene expressionMolecular networksCopy number alterationsCancer progressionComplex genomic alterationsTumor phenotypePrediction of recurrence-free survivalGenomic measurementsRecurrence-free survivalProstate cancer patientsProteomic alterationsGenomic aberrationsAggressive tumor phenotypeGenomic alterationsDownstream proteinsGenomic effectsNetwork-based approachProstate samplesTumor siteBiochemical stateMalignant tumorsProtein levelsTumor tissues