Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction.
about
Gene selection algorithms for microarray data based on least squares support vector machine.MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and DevelopmentComparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression dataProbabilistic non-negative matrix factorization: theory and application to microarray data analysis.Differential and trajectory methods for time course gene expression data.Regularized binormal ROC method in disease classification using microarray data.Independent component analysis-based penalized discriminant method for tumor classification using gene expression data.Kernel-based distance metric learning for microarray data classification.A fuzzy based feature selection from independent component subspace for machine learning classification of microarray dataIdentifying Cancer Biomarkers From Microarray Data Using Feature Selection and Semisupervised Learning.LS-NMF: a modified non-negative matrix factorization algorithm utilizing uncertainty estimates.Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluationK-OPLS package: kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space.Selecting informative genes for discriminant analysis using multigene expression profiles.Gene expression data classification using consensus independent component analysis.A kernel-based integration of genome-wide data for clinical decision support.Improved microarray-based decision support with graph encoded interactome dataNew bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems.New dimensionality reduction methods for the representation of high dimensional 'omics' data.Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries.Gene selection and classification for cancer microarray data based on machine learning and similarity measures.Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries.Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method.Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients.MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics.Discovery of novel dual VEGFR2 and Src inhibitors using a multistep virtual screening approach.Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualizationClassification algorithms for phenotype prediction in genomics and proteomics.Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice.Sparse representation for tumor classification based on feature extraction using latent low-rank representation.Analysis of bypass signaling in EGFR pathway and profiling of bypass genes for predicting response to anticancer EGFR tyrosine kinase inhibitors.Modeling Laterality of the Globus Pallidus Internus in Patients With Parkinson's Disease.Feature Import Vector Machine: A General Classifier with Flexible Feature Selection.Prediction of cancer outcome using DNA microarray technology: past, present and future.Computing molecular signatures as optima of a bi-objective function: method and application to prediction in oncogenomics.Accurate and fast feature selection workflow for high-dimensional omics data.A genetic programming-based approach to the classification of multiclass microarray datasets.Cancer classification from the gene expression profiles by Discriminant Kernel-PLS.Gas Chromatography Data Classification Based on Complex Coefficients of an Autoregressive ModelForecasting Models for Hydropower Unit Stability Using LS-SVM
P2860
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P2860
Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction.
description
2004 nî lūn-bûn
@nan
2004 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
name
Systematic benchmarking of mic ...... and dimensionality reduction.
@ast
Systematic benchmarking of mic ...... and dimensionality reduction.
@en
type
label
Systematic benchmarking of mic ...... and dimensionality reduction.
@ast
Systematic benchmarking of mic ...... and dimensionality reduction.
@en
prefLabel
Systematic benchmarking of mic ...... and dimensionality reduction.
@ast
Systematic benchmarking of mic ...... and dimensionality reduction.
@en
P2093
P356
P1433
P1476
Systematic benchmarking of mic ...... and dimensionality reduction.
@en
P2093
Bart L R De Moor
Frank De Smet
Johan A K Suykens
Nathalie Pochet
P304
P356
10.1093/BIOINFORMATICS/BTH383
P407
P577
2004-07-01T00:00:00Z