Identifying genes that contribute most to good classification in microarrays. - Wikidata - awgldk - TriplyDB

Identifying genes that contribute most to good classification in microarrays.

Identifying genes that contribute most to good classification in microarrays.

http://www.wikidata.org/entity/Q33256760

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A feature selection approach for identification of signature genes from SAGE data.Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets.A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model.A weighted average difference method for detecting differentially expressed genes from microarray data.Paradoxes in carcinogenesis: new opportunities for research directions.Stratification bias in low signal microarray studies Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments Outcome prediction based on microarray analysis: a critical perspective on methods Simpler evaluation of predictions and signature stability for gene expression data.Simple and flexible classification of gene expression microarrays via Swirls and Ripples.Analysis of differentially expressed proteins in colorectal cancer using hydroxyapatite column and SDS-PAGE.Supervised Bayesian latent class models for high-dimensional data.Designing a randomized clinical trial to evaluate personalized medicine: a new approach based on risk prediction.Using the optimal robust receiver operating characteristic (ROC) curve for predictive genetic tests.Algebraic stability indicators for ranked lists in molecular profiling.Systems biology and cancer: promises and perils Bayesian variable selection for binary outcomes in high-dimensional genomic studies using non-local priors Using microarrays to study the microenvironment in tumor biology: the crucial role of statistics.Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method.Improving the biomarker pipeline to develop and evaluate cancer screening tests Penalized model-based clustering with unconstrained covariance matrices.Feature selection for predicting tumor metastases in microarray experiments using paired design.A jackknife and voting classifier approach to feature selection and classification.Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables Prediction of relapse in paediatric pre-B acute lymphoblastic leukaemia using a three-gene risk index.Cell-Free DNA Methylation of Selected Genes Allows for Early Detection of the Major Cancers in Women

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P2860

Identifying genes that contribute most to good classification in microarrays.

http://www.wikidata.org/entity/Q33256760

description

2006 nî lūn-bûn

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2006 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած

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2006 թվականի սեպտեմբերին հրատարակված գիտական հոդված

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2006年の論文

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2006年論文

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2006年論文

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2006年論文

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2006年論文

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2006年論文

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2006年论文

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name

Identifying genes that contribute most to good classification in microarrays.

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Identifying genes that contribute most to good classification in microarrays.

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Identifying genes that contribute most to good classification in microarrays.

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Identifying genes that contribute most to good classification in microarrays.

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Identifying genes that contribute most to good classification in microarrays.

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Identifying genes that contribute most to good classification in microarrays.

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Identifying genes that contribute most to good classification in microarrays.

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Identifying genes that contribute most to good classification in microarrays.

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Identifying genes that contribute most to good classification in microarrays.

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1471-2105-7-407

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BMC Bioinformatics

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Identifying genes that contribute most to good classification in microarrays.

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Barnett S Kramer

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Stuart G Baker

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P2860

Zyxin, a regulator of actin filament assembly, targets the mitotic apparatus by interacting with h-warts/LATS1 tumor suppressor

Localization of serglycin in human neutrophil granulocytes and their precursors

Desmin is essential for the tensile strength and integrity of myofibrils but not for myogenic commitment, differentiation, and fusion of skeletal muscle

Gene selection algorithms for microarray data based on least squares support vector machine.

Gene expression profiling predicts clinical outcome of breast cancer

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

The human serglycin gene. Nucleotide sequence and methylation pattern in human promyelocytic leukemia HL-60 cells and T-lymphoblast Molt-4 cells

Prediction of central nervous system embryonal tumour outcome based on gene expression

Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays

Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data.

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