Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. - Wikidata - awgldk - TriplyDB

Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models.

Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models.

http://www.wikidata.org/entity/Q33314154

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Big data in medical science--a biostatistical view Novel image markers for non-small cell lung cancer classification and survival prediction Selective genomic copy number imbalances and probability of recurrence in early-stage breast cancer Transforming RNA-Seq data to improve the performance of prognostic gene signatures.Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data.Investigating the prediction ability of survival models based on both clinical and omics data: two case studies.Competing risks data analysis with high-dimensional covariates: an application in bladder cancer.densityCut: an efficient and versatile topological approach for automatic clustering of biological data Random rotation survival forest for high dimensional censored data Flexible boosting of accelerated failure time models.Comparative optimism in models involving both classical clinical and gene expression information.CMA: a comprehensive Bioconductor package for supervised classification with high dimensional data.Survival prediction from clinico-genomic models--a comparative study.Testing the additional predictive value of high-dimensional molecular data.Protein mass spectra data analysis for clinical biomarker discovery: a global review.Cross-study validation for the assessment of prediction algorithms.Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.Semiparametric prognosis models in genomic studies.A comparative study on gene-set analysis methods for assessing differential expression associated with the survival phenotype Stability investigations of multivariable regression models derived from low- and high-dimensional data.Survival models with preclustered gene groups as covariates.Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer.Random survival forests for competing risks.Added predictive value of omics data: specific issues related to validation illustrated by two case studies.Joint impact of clinical and behavioral variables on the risk of unplanned readmission and death after a heart failure hospitalization.A weighting approach for judging the effect of patient strata on high-dimensional risk prediction signatures.Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.Gene selection using iterative feature elimination random forests for survival outcomes.Prognostic value of single nucleotide polymorphisms of candidate genes associated with inflammation in early stage breast cancer.Genomic copy number imbalances associated with bone and non-bone metastasis of early-stage breast cancer Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model.Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling.On stability issues in deriving multivariable regression models.Predicting survival time for metastatic castration resistant prostate cancer: An iterative imputation approach.An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?Tailoring sparse multivariable regression techniques for prognostic single-nucleotide polymorphism signatures.Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value.Improving the acidic stability of Staphylococcus aureus α-acetolactate decarboxylase in Bacillus subtilis by changing basic residues to acidic residues.

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P2860

Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models.

http://www.wikidata.org/entity/Q33314154

description

2008 nî lūn-bûn

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2008 թուականի Յունուարին հրատարակուած գիտական յօդուած

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2008 թվականի հունվարին հրատարակված գիտական հոդված

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2008年の論文

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2008年論文

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2008年論文

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2008年論文

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2008年論文

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2008年論文

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name

Allowing for mandatory covaria ...... h-dimensional survival models.

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Allowing for mandatory covaria ...... h-dimensional survival models.

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Allowing for mandatory covaria ...... h-dimensional survival models.

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Allowing for mandatory covaria ...... h-dimensional survival models.

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Allowing for mandatory covaria ...... h-dimensional survival models.

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Martin Schumacher

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P2860

The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.

A predictive model for aggressive non-Hodgkin's lymphoma. The International Non-Hodgkin's Lymphoma Prognostic Factors Project

THE LASSO METHOD FOR VARIABLE SELECTION IN THE COX MODEL

Microarray gene expression data with linked survival phenotypes: diffuse large-B-cell lymphoma revisited.

Supervised group Lasso with applications to microarray data analysis.

Predicting patient survival from microarray data by accelerated failure time modeling using partial least squares and LASSO.

Additive risk survival model with microarray data

Assessment of survival prediction models based on microarray data.

Efron-type measures of prediction error for survival analysis.

Consistent estimation of the expected Brier score in general survival models with right-censored event times.

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