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Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer

Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. The early detection of cancer is crucial for successful treatment. Some cancers affect the concentration of certain molecules in the blood, which allows early diagnosis by analyzing the blood mass spectrum. I...

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Autores principales: Thakur, Ankita, Mishra, Vijay, Jain, Sunil K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Österreichische Apotheker-Verlagsgesellschaft 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3163368/
https://www.ncbi.nlm.nih.gov/pubmed/21886899
http://dx.doi.org/10.3797/scipharm.1105-11
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author Thakur, Ankita
Mishra, Vijay
Jain, Sunil K.
author_facet Thakur, Ankita
Mishra, Vijay
Jain, Sunil K.
author_sort Thakur, Ankita
collection PubMed
description Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. The early detection of cancer is crucial for successful treatment. Some cancers affect the concentration of certain molecules in the blood, which allows early diagnosis by analyzing the blood mass spectrum. It is possible that exclusive serum proteomic patterns could be used to differentiate cancer samples from non-cancer ones. Several techniques have been developed for the analysis of mass-spectrum curve, and use them for the detection of prostate, ovarian, breast, bladder, pancreatic, kidney, liver, and colon cancers. In present study, we applied data mining to the diagnosis of ovarian cancer and identified the most informative points of the mass-spectrum curve, then used student t-test and neural networks to determine the differences between the curves of cancer patients and healthy people. Two serum SELDI MS data sets were used in this research to identify serum proteomic patterns that distinguish the serum of ovarian cancer cases from non-cancer controls. Statistical testing and genetic algorithm-based methods are used for feature selection respectively. The results showed that (1) data mining techniques can be successfully applied to ovarian cancer detection with a reasonably high performance; (2) the discriminatory features (proteomic patterns) can be very different from one selection method to another.
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spelling pubmed-31633682011-09-01 Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer Thakur, Ankita Mishra, Vijay Jain, Sunil K. Sci Pharm Research Article Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. The early detection of cancer is crucial for successful treatment. Some cancers affect the concentration of certain molecules in the blood, which allows early diagnosis by analyzing the blood mass spectrum. It is possible that exclusive serum proteomic patterns could be used to differentiate cancer samples from non-cancer ones. Several techniques have been developed for the analysis of mass-spectrum curve, and use them for the detection of prostate, ovarian, breast, bladder, pancreatic, kidney, liver, and colon cancers. In present study, we applied data mining to the diagnosis of ovarian cancer and identified the most informative points of the mass-spectrum curve, then used student t-test and neural networks to determine the differences between the curves of cancer patients and healthy people. Two serum SELDI MS data sets were used in this research to identify serum proteomic patterns that distinguish the serum of ovarian cancer cases from non-cancer controls. Statistical testing and genetic algorithm-based methods are used for feature selection respectively. The results showed that (1) data mining techniques can be successfully applied to ovarian cancer detection with a reasonably high performance; (2) the discriminatory features (proteomic patterns) can be very different from one selection method to another. Österreichische Apotheker-Verlagsgesellschaft 2011 2011-07-05 /pmc/articles/PMC3163368/ /pubmed/21886899 http://dx.doi.org/10.3797/scipharm.1105-11 Text en © Thakur et al.; licensee Österreichische Apotheker-Verlagsgesellschaft m. b. H., Vienna, Austria. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Thakur, Ankita
Mishra, Vijay
Jain, Sunil K.
Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer
title Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer
title_full Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer
title_fullStr Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer
title_full_unstemmed Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer
title_short Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer
title_sort feed forward artificial neural network: tool for early detection of ovarian cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3163368/
https://www.ncbi.nlm.nih.gov/pubmed/21886899
http://dx.doi.org/10.3797/scipharm.1105-11
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