Recognition of cancer mediating genes using MLP-SDAE model
This article introduces a predictive deep learning model called MLP-SDAE, which combines Multilayer Perceptron (MLP) and Stacked Denoising Auto-encoder (SDAE) techniques. Our model, MLP-SDAE is trained using Stacked Denoising Auto-Encoder for feature selection, and backpropagation is employed within...
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000085 |
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| author | Sougata Sheet Ranjan Ghosh Anupam Ghosh |
| author_facet | Sougata Sheet Ranjan Ghosh Anupam Ghosh |
| author_sort | Sougata Sheet |
| collection | DOAJ |
| description | This article introduces a predictive deep learning model called MLP-SDAE, which combines Multilayer Perceptron (MLP) and Stacked Denoising Auto-encoder (SDAE) techniques. Our model, MLP-SDAE is trained using Stacked Denoising Auto-Encoder for feature selection, and backpropagation is employed within the MLP structure. We have incorporated dropout to enhance the model’s performance and prevent overfitting. The primary objective of the MLP-SDAE model is to identify associations among genes that have undergone significant alterations from a normal to a diseased state based on their expression behaviors. This concept allows us to predict disease-mediating genes and their altered associations. The methodology involves calculating gene-based correlation coefficients and selecting a subset of genes based on this analysis. We have demonstrated the effectiveness of our methods using four gene expression datasets related to human leukemia, lung, colon, and breast cancer. As a result, we have identified several potentially important genes, such as CACLA, HBA, IGFBP3, EFGR, TFN, TP53, LI6, and TMTC1, which may play a crucial role in developing these cancers. Furthermore, we conducted a comprehensive comparative study with other deep learning techniques, including Recurrent Neural Network (RNN), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Auto-encoder (AE), and Denoising Auto-encoder (DAE). Our results have been validated through biochemical pathway analysis, t-tests, F-score, Gene Ontology (GO) identification, and the NCBI database. These validations demonstrate that our proposed MLP-SDAE model outperforms existing methods. |
| format | Article |
| id | doaj-art-12e3e93cda4146d78e7bb28f54dbb5a1 |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-12e3e93cda4146d78e7bb28f54dbb5a12025-08-20T02:34:40ZengElsevierSystems and Soft Computing2772-94192024-12-01620007910.1016/j.sasc.2024.200079Recognition of cancer mediating genes using MLP-SDAE modelSougata Sheet0Ranjan Ghosh1Anupam Ghosh2Department of Computer Science & Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India; A. K. Choudhury school of IT, University of Calcutta, Kolkata, 700106, West Bengal, India; Corresponding author at: Department of Computer Science & Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India.Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, 700009, West Bengal, IndiaDepartment of Computer Science & Engineering, Netaji Subhash Engineering College, Garia, 700152, West Bengal, IndiaThis article introduces a predictive deep learning model called MLP-SDAE, which combines Multilayer Perceptron (MLP) and Stacked Denoising Auto-encoder (SDAE) techniques. Our model, MLP-SDAE is trained using Stacked Denoising Auto-Encoder for feature selection, and backpropagation is employed within the MLP structure. We have incorporated dropout to enhance the model’s performance and prevent overfitting. The primary objective of the MLP-SDAE model is to identify associations among genes that have undergone significant alterations from a normal to a diseased state based on their expression behaviors. This concept allows us to predict disease-mediating genes and their altered associations. The methodology involves calculating gene-based correlation coefficients and selecting a subset of genes based on this analysis. We have demonstrated the effectiveness of our methods using four gene expression datasets related to human leukemia, lung, colon, and breast cancer. As a result, we have identified several potentially important genes, such as CACLA, HBA, IGFBP3, EFGR, TFN, TP53, LI6, and TMTC1, which may play a crucial role in developing these cancers. Furthermore, we conducted a comprehensive comparative study with other deep learning techniques, including Recurrent Neural Network (RNN), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Auto-encoder (AE), and Denoising Auto-encoder (DAE). Our results have been validated through biochemical pathway analysis, t-tests, F-score, Gene Ontology (GO) identification, and the NCBI database. These validations demonstrate that our proposed MLP-SDAE model outperforms existing methods.http://www.sciencedirect.com/science/article/pii/S2772941924000085Stacked denoising auto-encoderDeep learningMLPp-valuet-test |
| spellingShingle | Sougata Sheet Ranjan Ghosh Anupam Ghosh Recognition of cancer mediating genes using MLP-SDAE model Systems and Soft Computing Stacked denoising auto-encoder Deep learning MLP p-value t-test |
| title | Recognition of cancer mediating genes using MLP-SDAE model |
| title_full | Recognition of cancer mediating genes using MLP-SDAE model |
| title_fullStr | Recognition of cancer mediating genes using MLP-SDAE model |
| title_full_unstemmed | Recognition of cancer mediating genes using MLP-SDAE model |
| title_short | Recognition of cancer mediating genes using MLP-SDAE model |
| title_sort | recognition of cancer mediating genes using mlp sdae model |
| topic | Stacked denoising auto-encoder Deep learning MLP p-value t-test |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000085 |
| work_keys_str_mv | AT sougatasheet recognitionofcancermediatinggenesusingmlpsdaemodel AT ranjanghosh recognitionofcancermediatinggenesusingmlpsdaemodel AT anupamghosh recognitionofcancermediatinggenesusingmlpsdaemodel |