Gene selection and classification of microarray data using random forest. (Q25255911)

6 July 2017

Gene selection and classification of microarray data using random forest. (English)

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6 July 2017

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Ramón Díaz-Uriarte

1

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author name string

Sara Alvarez de Andrés

series ordinal

2

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PubMed ID

16398926

6 July 2017

language of work or name

English

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publication date

6 January 2006

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6 July 2017

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6 July 2017

7

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6 July 2017

1

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page(s)

3

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https://scigraph.springernature.com/pub.10.1186/1471-2105-7-3

6 July 2017

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cites work

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Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes

7 April 2017

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Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes

7 April 2017

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Multiclass classification of microarray data with repeated measurements: application to cancer

7 April 2017

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7 April 2017

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Gene expression profiling predicts clinical outcome of breast cancer

7 April 2017

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Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

7 April 2017

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

7 April 2017

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Systematic variation in gene expression patterns in human cancer cell lines

7 April 2017

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A molecular signature of metastasis in primary solid tumors

7 April 2017

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Diagnosis of multiple cancer types by shrunken centroids of gene expression

7 April 2017

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

7 April 2017

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Multiclass cancer diagnosis using tumor gene expression signatures

7 April 2017

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

7 April 2017

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Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays

7 April 2017

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Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data

28 September 2017

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28 September 2017

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Evaluating methods for classifying expression data

28 September 2017

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BagBoosting for tumor classification with gene expression data

28 September 2017

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Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial

28 September 2017

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A pilot study on the application of statistical classification procedures to molecular epidemiological data

28 September 2017

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Mapping complex traits using Random Forests

28 September 2017

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Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

28 September 2017

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Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions

28 September 2017

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Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro

28 September 2017

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An accelerated procedure for recursive feature ranking on microarray data.

28 September 2017

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Classification of multiple cancer types by multicategory support vector machines using gene expression data

28 September 2017

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Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification

28 September 2017

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Bayesian automatic relevance determination algorithms for classifying gene expression data

28 September 2017

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Selection bias in gene extraction on the basis of microarray gene-expression data

28 September 2017

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Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

28 September 2017

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Support vector machine classification and validation of cancer tissue samples using microarray expression data

28 September 2017

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Outcome signature genes in breast cancer: is there a unique set?

28 September 2017

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A predictor based on the somatic genomic changes of the BRCA1/BRCA2 breast cancer tumors identifies the non-BRCA1/BRCA2 tumors with BRCA1 promoter hypermethylation

31 May 2018

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Optimal number of features as a function of sample size for various classification rules.

28 November 2018

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A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression.

28 November 2018

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Is cross-validation better than resubstitution for ranking genes?

28 November 2018

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https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1363357

Pattern recognition in gene expression profiling using DNA array: a comparative study of different statistical methods applied to cancer classification.

28 November 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1363357

Evolutionary algorithms for finding optimal gene sets in microarray prediction

28 November 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1363357

Prediction of cancer outcome with microarrays: a multiple random validation strategy

28 November 2018

1 reference

PubMed

https://pubmed.ncbi.nlm.nih.gov/16398926

inferred from PubMed ID database lookup

12 December 2020

based on heuristic

An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas

1 reference

PubMed

https://pubmed.ncbi.nlm.nih.gov/16398926

inferred from PubMed ID database lookup

12 December 2020

based on heuristic

Gene expression correlates of clinical prostate cancer behavior

1 reference

PubMed

https://pubmed.ncbi.nlm.nih.gov/16398926

inferred from PubMed ID database lookup

12 December 2020

based on heuristic

Identifiers

DOI

10.1186/1471-2105-7-3

1 reference

journals/bmcbi/Diaz-UriarteA06

6 July 2017

1 reference

DBLP Dataset 2021-01-02

Dimensions Publication ID

28 January 2021

1004156594

0 references

OpenCitations bibliographic resource ID

641100

1 reference

Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

1 reference

6 July 2017

1 reference

PubMed ID

16398926