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An enhanced Genetic Folding algorithm for prostate and breast cancer detection

Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate canc...

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Autores principales: Mezher, Mohammad A., Altamimi, Almothana, Altamimi, Ruhaifa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299265/
https://www.ncbi.nlm.nih.gov/pubmed/35875638
http://dx.doi.org/10.7717/peerj-cs.1015
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author Mezher, Mohammad A.
Altamimi, Almothana
Altamimi, Ruhaifa
author_facet Mezher, Mohammad A.
Altamimi, Almothana
Altamimi, Ruhaifa
author_sort Mezher, Mohammad A.
collection PubMed
description Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model’s area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed.
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spelling pubmed-92992652022-07-21 An enhanced Genetic Folding algorithm for prostate and breast cancer detection Mezher, Mohammad A. Altamimi, Almothana Altamimi, Ruhaifa PeerJ Comput Sci Bioinformatics Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model’s area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed. PeerJ Inc. 2022-06-21 /pmc/articles/PMC9299265/ /pubmed/35875638 http://dx.doi.org/10.7717/peerj-cs.1015 Text en ©2022 Mezher et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Mezher, Mohammad A.
Altamimi, Almothana
Altamimi, Ruhaifa
An enhanced Genetic Folding algorithm for prostate and breast cancer detection
title An enhanced Genetic Folding algorithm for prostate and breast cancer detection
title_full An enhanced Genetic Folding algorithm for prostate and breast cancer detection
title_fullStr An enhanced Genetic Folding algorithm for prostate and breast cancer detection
title_full_unstemmed An enhanced Genetic Folding algorithm for prostate and breast cancer detection
title_short An enhanced Genetic Folding algorithm for prostate and breast cancer detection
title_sort enhanced genetic folding algorithm for prostate and breast cancer detection
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299265/
https://www.ncbi.nlm.nih.gov/pubmed/35875638
http://dx.doi.org/10.7717/peerj-cs.1015
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