Effect of training-sample size and classification difficulty on the accuracy of genomic predictors - Wikidata - awgldk - TriplyDB

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

http://www.wikidata.org/entity/Q33892880

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Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis A simple but highly effective approach to evaluate the prognostic performance of gene expression signatures Discovering biological connections between experimental conditions based on common patterns of differential gene expression A systematic evaluation of multi-gene predictors for the pathological response of breast cancer patients to chemotherapy.An 18-gene signature for vascular invasion is associated with aggressive features and reduced survival in breast cancer The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.Optimising parallel R correlation matrix calculations on gene expression data using MapReduce.High dimensional biological data retrieval optimization with NoSQL technology.Breast cancer subtype specific classifiers of response to neoadjuvant chemotherapy do not outperform classifiers trained on all subtypes Obesity and overfeeding affecting both tumor and systemic metabolism activates the progesterone receptor to contribute to postmenopausal breast cancer Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?Minimising immunohistochemical false negative ER classification using a complementary 23 gene expression signature of ER status Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures.Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response.Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems.A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer Claudin-low breast cancers: clinical, pathological, molecular and prognostic characterization Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer.Predicting response and survival in chemotherapy-treated triple-negative breast cancer.Cell line derived multi-gene predictor of pathologic response to neoadjuvant chemotherapy in breast cancer: a validation study on US Oncology 02-103 clinical trial.Dual roles for immune metagenes in breast cancer prognosis and therapy prediction.Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework.Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement Determination of minimum training sample size for microarray-based cancer outcome prediction-an empirical assessment.Tumor-infiltrating immune cell profiles and their change after neoadjuvant chemotherapy predict response and prognosis of breast cancer.Shift in GATA3 functions, and GATA3 mutations, control progression and clinical presentation in breast cancer.Differential network analysis applied to preoperative breast cancer chemotherapy response.Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis.ADAM12-L is a direct target of the miR-29 and miR-200 families in breast cancer.Evaluation of gene expression classification studies: factors associated with classification performance Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value A gene expression signature from human breast cancer cells with acquired hormone independence identifies MYC as a mediator of antiestrogen resistance A breast cancer meta-analysis of two expression measures of chromosomal instability reveals a relationship with younger age at diagnosis and high risk histopathological variables.A three-gene model to robustly identify breast cancer molecular subtypes Notch promotes recurrence of dormant tumor cells following HER2/neu-targeted therapy.Study design requirements for RNA sequencing-based breast cancer diagnostics Breast cancer subtype predictors revisited: from consensus to concordance?Characterization of DNA variants in the human kinome in breast cancer.PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer.

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P2860

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

http://www.wikidata.org/entity/Q33892880

description

2010 nî lūn-bûn

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2010 թուականի Յունուարին հրատարակուած գիտական յօդուած

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2010 թվականի հունվարին հրատարակված գիտական հոդված

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2010年の論文

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2010年論文

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2010年論文

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2010年論文

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2010年論文

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2010年論文

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2010年论文

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name

Effect of training-sample size ...... accuracy of genomic predictors

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Effect of training-sample size ...... accuracy of genomic predictors

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type

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Effect of training-sample size ...... accuracy of genomic predictors

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Effect of training-sample size ...... accuracy of genomic predictors

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Effect of training-sample size ...... accuracy of genomic predictors

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Effect of training-sample size ...... accuracy of genomic predictors

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Breast Cancer Research

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Effect of training-sample size ...... accuracy of genomic predictors

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P2093

Alex Ishkin

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Daniel Booser

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Frank W Samuelson

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Gabriel N Hortobagyi

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Kenneth R Hess

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Lajos Pusztai

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Leming Shi

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Marina Tsyganova

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Mauro Delorenzi

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Tatiana Nikolskaya

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P2860

The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists

A gene-expression signature as a predictor of survival in breast cancer

Molecular portraits of human breast tumours

A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer

Prognostic gene signatures for non-small-cell lung cancer.

Classification based upon gene expression data: bias and precision of error rates.

A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.

Assessing classifiers from two independent data sets using ROC analysis: a nonparametric approach.

An empirical study of univariate and genetic algorithm-based feature selection in binary classification with microarray data.

Concordance among gene-expression-based predictors for breast cancer.

P2888

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R5

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scholarly article

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10.1186/BCR2468

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1

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12

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Brandon D. Gallas

Christos Hatzis

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2010-01-11T00:00:00Z

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40906852

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1031024054

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20064235

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2880423

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