Accurate diagnosis of prostate cancer using logistic regression

A new logistic regression-based method to distinguish between cancerous and noncancerous RNA genomic data is developed and tested with 100% precision on 595 healthy and cancerous prostate samples. A logistic regression system is developed and trained using whole-exome sequencing data at a high-level...

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Autor principal:	Hooshmand Arash
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	machine learning prostate cancer diagnosis transcriptome rna sequencing high throughput technologies logistic regression classification Medicine R
Acceso en línea:	https://doaj.org/article/61701a8587014689896691fb38325ee7
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spelling	oai:doaj.org-article:61701a8587014689896691fb38325ee72021-12-05T14:10:54ZAccurate diagnosis of prostate cancer using logistic regression2391-546310.1515/med-2021-0238https://doaj.org/article/61701a8587014689896691fb38325ee72021-03-01T00:00:00Zhttps://doi.org/10.1515/med-2021-0238https://doaj.org/toc/2391-5463A new logistic regression-based method to distinguish between cancerous and noncancerous RNA genomic data is developed and tested with 100% precision on 595 healthy and cancerous prostate samples. A logistic regression system is developed and trained using whole-exome sequencing data at a high-level, i.e., normalized quantification of RNAs obtained from 495 prostate cancer samples from The Cancer Genome Atlas and 100 healthy samples from the Genotype-Tissue Expression project. We could show that both sensitivity and specificity of the method in the classification of cancerous and noncancerous cells are perfectly 100%.Hooshmand ArashDe Gruyterarticlemachine learningprostate cancerdiagnosistranscriptomerna sequencinghigh throughput technologieslogistic regressionclassificationMedicineRENOpen Medicine, Vol 16, Iss 1, Pp 459-463 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning prostate cancer diagnosis transcriptome rna sequencing high throughput technologies logistic regression classification Medicine R
spellingShingle	machine learning prostate cancer diagnosis transcriptome rna sequencing high throughput technologies logistic regression classification Medicine R Hooshmand Arash Accurate diagnosis of prostate cancer using logistic regression
description	A new logistic regression-based method to distinguish between cancerous and noncancerous RNA genomic data is developed and tested with 100% precision on 595 healthy and cancerous prostate samples. A logistic regression system is developed and trained using whole-exome sequencing data at a high-level, i.e., normalized quantification of RNAs obtained from 495 prostate cancer samples from The Cancer Genome Atlas and 100 healthy samples from the Genotype-Tissue Expression project. We could show that both sensitivity and specificity of the method in the classification of cancerous and noncancerous cells are perfectly 100%.
format	article
author	Hooshmand Arash
author_facet	Hooshmand Arash
author_sort	Hooshmand Arash
title	Accurate diagnosis of prostate cancer using logistic regression
title_short	Accurate diagnosis of prostate cancer using logistic regression
title_full	Accurate diagnosis of prostate cancer using logistic regression
title_fullStr	Accurate diagnosis of prostate cancer using logistic regression
title_full_unstemmed	Accurate diagnosis of prostate cancer using logistic regression
title_sort	accurate diagnosis of prostate cancer using logistic regression
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/61701a8587014689896691fb38325ee7
work_keys_str_mv	AT hooshmandarash accuratediagnosisofprostatecancerusinglogisticregression
_version_	1718371607854972928

Accurate diagnosis of prostate cancer using logistic regression

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