Proteomic mass spectra classification using decision tree based ensemble methods.
about
Using simple artificial intelligence methods for predicting amyloidogenesis in antibodiesIdentification of biomarkers from mass spectrometry data using a "common" peak approachAdaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data.On the analysis of glycomics mass spectrometry data via the regularized area under the ROC curveClassification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles.On consensus biomarker selection.Random subwindows and extremely randomized trees for image classification in cell biology.Is bagging effective in the classification of small-sample genomic and proteomic data?Common peak approach using mass spectrometry data sets for predicting the effects of anticancer drugs on breast cancerA comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data.Challenges for biomarker discovery in body fluids using SELDI-TOF-MS.To aggregate or not to aggregate high-dimensional classifiersMass-Up: an all-in-one open software application for MALDI-TOF mass spectrometry knowledge discovery.Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils.LDL-apheresis depletes apoE-HDL and pre-β1-HDL in familial hypercholesterolemia: relevance to atheroprotection.Plasma proteomic profiles from disease-discordant monozygotic twins suggest that molecular pathways are shared in multiple systemic autoimmune diseases.MAPPING ABNORMAL SUBCORTICAL BRAIN MORPHOMETRY IN AN ELDERLY HIV+ COHORT.Bioinformatics approaches in clinical proteomics.Circulating microRNA-based screening tool for breast cancer.New biomarkers of Crohn's disease: serum biomarkers and development of diagnostic tools.Biomarkers of inflammation and innate immunity in atrophic nonunion fracture.Toward digital staining using imaging mass spectrometry and random forests.Machine learning applications in proteomics research: how the past can boost the future.A jackknife and voting classifier approach to feature selection and classification.High-density lipoprotein proteome dynamics in human endotoxemia.Identification of protein biomarkers associated with cardiac ischemia by a proteomic approach.Identification of immune signatures predictive of clinical protection from malaria.A metabonomics investigation of multiple sclerosis by nuclear magnetic resonance.Feature selection and classification of leukocytes using random forest.Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformaticsAn Optimal Classification Method for Biological and Medical DataSmall-Sample Error Estimation for Bagged Classification Rules
P2860
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P2860
Proteomic mass spectra classification using decision tree based ensemble methods.
description
2005 nî lūn-bûn
@nan
2005 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2005 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2005年の論文
@ja
2005年学术文章
@wuu
2005年学术文章
@zh-cn
2005年学术文章
@zh-hans
2005年学术文章
@zh-my
2005年学术文章
@zh-sg
2005年學術文章
@yue
name
Proteomic mass spectra classification using decision tree based ensemble methods.
@ast
Proteomic mass spectra classification using decision tree based ensemble methods.
@en
type
label
Proteomic mass spectra classification using decision tree based ensemble methods.
@ast
Proteomic mass spectra classification using decision tree based ensemble methods.
@en
prefLabel
Proteomic mass spectra classification using decision tree based ensemble methods.
@ast
Proteomic mass spectra classification using decision tree based ensemble methods.
@en
P2093
P2860
P356
P1433
P1476
Proteomic mass spectra classification using decision tree based ensemble methods.
@en
P2093
Dominique de Seny
Louis Wehenkel
Marianne Fillet
Marie-Paule Merville
Michel Malaise
Pierre Geurts
P2860
P304
P356
10.1093/BIOINFORMATICS/BTI494
P407
P577
2005-05-12T00:00:00Z