A new local covariance matrix estimation for the classification of gene expression profiles in high dimensional RNA-Seq data-Reference-Cited by-同舟云学术

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A new local covariance matrix estimation for the classification of gene expression profiles in high dimensional RNA-Seq data

Published:2021-04 Issue: Volume:167 Page:114200
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Kochan Necla,Yazgı Tütüncü G.,Giner Göknur

Funder

National Health and Medical Research Council

Cancer Therapeutics Cooperative Research Centre

Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference19 articles.

1. Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification;Algamal;Expert Systems with Applications,2015

2. On local dependence function for multivariate distributions;Bairamov;New Trends in Probability and Statistics,2000

3. A new measure on linear local dependence;Bairamov;Statistics,2003

4. Regularized logistic regression without a penalty term: An application to cancer classification with microarray data;Bielza;Expert Systems with Applications,2011

5. Caefer, C. E., & Rotman, S. R. (2009). Local covariance matrices for improved target detection performance. In Proceedings of the 1st workshop on hyperspectral image and signal processing: Evolution in emote sensing (WHISPERS ’09) (pp. 1–4).

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1. Ridge estimation of covariance matrix from data in two classes;Applications of Mathematics;2024-02-22

2. Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data;Research Anthology on Bioinformatics, Genomics, and Computational Biology;2023-12-29

3. Kimma: flexible linear mixed effects modeling with kinship covariance for RNA-seq data;Bioinformatics;2023-05-01

4. Bioinformatics and Biostatistics in Precision Medicine;Oncology: Genomics, Precision Medicine and Therapeutic Targets;2023

5. Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data;Encyclopedia of Data Science and Machine Learning;2022-10-14

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