Classification of multiple cancer types by multicategory support vector machines using gene expression data.
about
A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolorMulti-class cancer classification by total principal component regression (TPCR) using microarray gene expression dataPredictors of primary breast cancers responsiveness to preoperative epirubicin/cyclophosphamide-based chemotherapy: translation of microarray data into clinically useful predictive signaturesProtein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines.Gene selection and classification of microarray data using random forest.Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray dataBayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data.Data-driven analysis approach for biomarker discovery using molecular-profiling technologies.Three methods for optimization of cross-laboratory and cross-platform microarray expression data.Gene selection for classification of microarray data based on the Bayes error.Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data.A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors.A simple method to combine multiple molecular biomarkers for dichotomous diagnostic classification.Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.Extending bicluster analysis to annotate unclassified ORFs and predict novel functional modules using expression data.Predictive response-relevant clustering of expression data provides insights into disease processes.Diagnostic Utility of Gene Expression Profiles.A hybrid BPSO-CGA approach for gene selection and classification of microarray data.Optimization based tumor classification from microarray gene expression data.A unified computational model for revealing and predicting subtle subtypes of cancers.Prognostic transcriptional association networks: a new supervised approach based on regression trees.Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype predictionNBA-Palm: prediction of palmitoylation site implemented in Naïve Bayes algorithm.Kernel machine approach to testing the significance of multiple genetic markers for risk prediction.Gene selection for multiclass prediction by weighted Fisher criterionMachine learning algorithms to classify spinal muscular atrophy subtypes.Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.Approaches to analyse dynamic microbial communities such as those seen in cystic fibrosis lung.A simple and exact Laplacian clustering of complex networking phenomena: application to gene expression profiles.Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray.Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.SAMSVM: A tool for misalignment filtration of SAM-format sequences with support vector machine.Gene expression based leukemia sub-classification using committee neural networksDevelopment of a Nomogram to Predict N2 or N3 Stage in T1-2 Invasive Breast Cancer Patients with No Palpable Lymphadenopathy.Multiclass molecular cancer classification by kernel subspace methods with effective kernel parameter selection.Exploring functional relationships between components of the gene expression machinery.Classifying clinical notes with pain assessment using machine learning.Cancer classification from the gene expression profiles by Discriminant Kernel-PLS.
P2860
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P2860
Classification of multiple cancer types by multicategory support vector machines using gene expression data.
description
2003 nî lūn-bûn
@nan
2003 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
name
Classification of multiple can ...... es using gene expression data.
@ast
Classification of multiple can ...... es using gene expression data.
@en
type
label
Classification of multiple can ...... es using gene expression data.
@ast
Classification of multiple can ...... es using gene expression data.
@en
prefLabel
Classification of multiple can ...... es using gene expression data.
@ast
Classification of multiple can ...... es using gene expression data.
@en
P356
P1433
P1476
Classification of multiple can ...... es using gene expression data.
@en
P2093
Cheol-Koo Lee
Yoonkyung Lee
P304
P356
10.1093/BIOINFORMATICS/BTG102
P407
P577
2003-06-01T00:00:00Z