Optimized application of penalized regression methods to diverse genomic data. - Wikidata - awgldk - TriplyDB

Optimized application of penalized regression methods to diverse genomic data.

Optimized application of penalized regression methods to diverse genomic data.

http://www.wikidata.org/entity/Q34096074

about

Pattern recognition in bioinformatics glmgraph: an R package for variable selection and predictive modeling of structured genomic data Cross-study validation for the assessment of prediction algorithms.A 19-Gene expression signature as a predictor of survival in colorectal cancer Predictive modeling using a somatic mutational profile in ovarian high grade serous carcinoma Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study.A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses.Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data Using Genetic Distance to Infer the Accuracy of Genomic Prediction Fused Regression for Multi-source Gene Regulatory Network Inference.Control of Gene Expression by RNA Binding Protein Action on Alternative Translation Initiation Sites.Application of Penalized Regression Techniques in Modelling Insulin Sensitivity by Correlated Metabolic Parameters.PyLDM - An open source package for lifetime density analysis of time-resolved spectroscopic data.Hepatocellular carcinoma associated microRNA expression signature: integrated bioinformatics analysis, experimental validation and clinical significance.Evaluation of the lasso and the elastic net in genome-wide association studies.SLINGER: large-scale learning for predicting gene expression The use of vector bootstrapping to improve variable selection precision in Lasso models.Comprehensive analysis and selection of anthrax vaccine adsorbed immune correlates of protection in rhesus macaques.Feature selection and survival modeling in The Cancer Genome Atlas.Characterizing genomic alterations in cancer by complementary functional associations.Evaluation of variable selection methods for random forests and omics data sets.Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques.Beyond genomics: understanding exposotypes through metabolomics.Machine learning methods in the computational biology of cancer.Survival analysis by penalized regression and matrix factorization.Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model.

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P2860

Optimized application of penalized regression methods to diverse genomic data.

http://www.wikidata.org/entity/Q34096074

description

2011 nî lūn-bûn

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2011 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2011 թվականի դեկտեմբերին հրատարակված գիտական հոդված

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2011年の論文

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2011年論文

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2011年論文

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2011年論文

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2011年論文

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2011年論文

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2011年论文

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name

Optimized application of penalized regression methods to diverse genomic data.

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Optimized application of penalized regression methods to diverse genomic data.

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Optimized application of penalized regression methods to diverse genomic data.

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type

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Optimized application of penalized regression methods to diverse genomic data.

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Optimized application of penalized regression methods to diverse genomic data.

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Optimized application of penalized regression methods to diverse genomic data.

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prefLabel

Optimized application of penalized regression methods to diverse genomic data.

@ast

Optimized application of penalized regression methods to diverse genomic data.

@en

Optimized application of penalized regression methods to diverse genomic data.

@nl

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Optimized application of penalized regression methods to diverse genomic data.

@en

P2093

Frances A Shepherd

http://www.w3.org/2001/XMLSchema#string

Igor Jurisica

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Melania Pintilie

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P2860

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

A human gut microbial gene catalogue established by metagenomic sequencing

An obesity-associated gut microbiome with increased capacity for energy harvest

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications

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

Microbial ecology: human gut microbes associated with obesity

THE LASSO METHOD FOR VARIABLE SELECTION IN THE COX MODEL

Gene-expression profiles predict survival of patients with lung adenocarcinoma

Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data.

Reader's reaction to "Dimension reduction for classification with gene expression microarray data" by Dai et al (2006).

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10.1093/BIOINFORMATICS/BTR591

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24

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27

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Curtis Huttenhower

Ming-Sound Tsao

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2011-12-01T00:00:00Z

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