Accurate diagnosis of prostate cancer using logistic regression

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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Bibliographic Details
Main Author: Hooshmand Arash
Format: Article
Language:English
Published: De Gruyter 2021-03-01
Series:Open Medicine
Subjects:
machine learning
prostate cancer
diagnosis
transcriptome
rna sequencing
high throughput technologies
logistic regression
classification
Online Access:https://doi.org/10.1515/med-2021-0238
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spelling doaj-61701a8587014689896691fb38325ee72021-10-03T07:42:37ZengDe GruyterOpen Medicine2391-54632021-03-0116145946310.1515/med-2021-0238Accurate diagnosis of prostate cancer using logistic regressionHooshmand Arash0Department of Biomedical Engineering and Health Systems, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 11428 Stockholm, SwedenA 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%.https://doi.org/10.1515/med-2021-0238machine learningprostate cancerdiagnosistranscriptomerna sequencinghigh throughput technologieslogistic regressionclassification
collection DOAJ
language English
format Article
sources DOAJ
author Hooshmand Arash
spellingShingle Hooshmand Arash
Accurate diagnosis of prostate cancer using logistic regression
Open Medicine
machine learning
prostate cancer
diagnosis
transcriptome
rna sequencing
high throughput technologies
logistic regression
classification
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
series Open Medicine
issn 2391-5463
publishDate 2021-03-01
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%.
topic machine learning
prostate cancer
diagnosis
transcriptome
rna sequencing
high throughput technologies
logistic regression
classification
url https://doi.org/10.1515/med-2021-0238
work_keys_str_mv AT hooshmandarash accuratediagnosisofprostatecancerusinglogisticregression
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