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Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile

The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA mi...

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Autor principal: Almarzouki, Hatim Z
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759849/
https://www.ncbi.nlm.nih.gov/pubmed/35035840
http://dx.doi.org/10.1155/2022/4715998
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author Almarzouki, Hatim Z
author_facet Almarzouki, Hatim Z
author_sort Almarzouki, Hatim Z
collection PubMed
description The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method's main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure's training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy.
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spelling pubmed-87598492022-01-15 Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile Almarzouki, Hatim Z J Healthc Eng Research Article The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method's main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure's training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy. Hindawi 2022-01-07 /pmc/articles/PMC8759849/ /pubmed/35035840 http://dx.doi.org/10.1155/2022/4715998 Text en Copyright © 2022 Hatim Z Almarzouki. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Almarzouki, Hatim Z
Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile
title Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile
title_full Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile
title_fullStr Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile
title_full_unstemmed Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile
title_short Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile
title_sort deep-learning-based cancer profiles classification using gene expression data profile
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759849/
https://www.ncbi.nlm.nih.gov/pubmed/35035840
http://dx.doi.org/10.1155/2022/4715998
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