<i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design a...

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Autores principales:	Niyazi Senturk, Gulten Tuncel, Berkcan Dogan, Lamiya Aliyeva, Mehmet Sait Dundar, Sebnem Ozemri Sag, Gamze Mocan, Sehime Gulsun Temel, Munis Dundar, Mahmut Cerkez Ergoren
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	breast cancer <i>BRCA1</i> <i>BRCA2</i> variation artificial intelligence translational fuzzy logic Genetics QH426-470
Acceso en línea:	https://doaj.org/article/34d3a02102d843b8aa3a735aa953a776
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id	oai:doaj.org-article:34d3a02102d843b8aa3a735aa953a776
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spelling	oai:doaj.org-article:34d3a02102d843b8aa3a735aa953a7762021-11-25T17:41:47Z<i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models10.3390/genes121117742073-4425https://doaj.org/article/34d3a02102d843b8aa3a735aa953a7762021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4425/12/11/1774https://doaj.org/toc/2073-4425Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 <i>BRCA1</i>, 128 <i>BRCA2</i> and 11 both <i>BRCA1</i> and <i>BRCA2</i> genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for <i>BRCA</i> variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.Niyazi SenturkGulten TuncelBerkcan DoganLamiya AliyevaMehmet Sait DundarSebnem Ozemri SagGamze MocanSehime Gulsun TemelMunis DundarMahmut Cerkez ErgorenMDPI AGarticlebreast cancer<i>BRCA1</i><i>BRCA2</i>variationartificial intelligencetranslational fuzzy logicGeneticsQH426-470ENGenes, Vol 12, Iss 1774, p 1774 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast cancer <i>BRCA1</i> <i>BRCA2</i> variation artificial intelligence translational fuzzy logic Genetics QH426-470
spellingShingle	breast cancer <i>BRCA1</i> <i>BRCA2</i> variation artificial intelligence translational fuzzy logic Genetics QH426-470 Niyazi Senturk Gulten Tuncel Berkcan Dogan Lamiya Aliyeva Mehmet Sait Dundar Sebnem Ozemri Sag Gamze Mocan Sehime Gulsun Temel Munis Dundar Mahmut Cerkez Ergoren <i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models
description	Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 <i>BRCA1</i>, 128 <i>BRCA2</i> and 11 both <i>BRCA1</i> and <i>BRCA2</i> genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for <i>BRCA</i> variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.
format	article
author	Niyazi Senturk Gulten Tuncel Berkcan Dogan Lamiya Aliyeva Mehmet Sait Dundar Sebnem Ozemri Sag Gamze Mocan Sehime Gulsun Temel Munis Dundar Mahmut Cerkez Ergoren
author_facet	Niyazi Senturk Gulten Tuncel Berkcan Dogan Lamiya Aliyeva Mehmet Sait Dundar Sebnem Ozemri Sag Gamze Mocan Sehime Gulsun Temel Munis Dundar Mahmut Cerkez Ergoren
author_sort	Niyazi Senturk
title	<i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models
title_short	<i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models
title_full	<i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models
title_fullStr	<i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models
title_full_unstemmed	<i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models
title_sort	<i>brca</i> variations risk assessment in breast cancers using different artificial intelligence models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/34d3a02102d843b8aa3a735aa953a776
work_keys_str_mv	AT niyazisenturk ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT gultentuncel ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT berkcandogan ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT lamiyaaliyeva ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT mehmetsaitdundar ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT sebnemozemrisag ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT gamzemocan ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT sehimegulsuntemel ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT munisdundar ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels AT mahmutcerkezergoren ibrcaivariationsriskassessmentinbreastcancersusingdifferentartificialintelligencemodels
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<i>BRCA</i> Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

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