Identification of Cancer Mediating Biomarkers using Stacked Denoising Autoencoder Model - An Application on Human Lung Data
In this work, we form stacked denoising auto encoder model which is recognized few feasible genes mediating human lung adenocarcinoma. At first we have trained the data for feature selection by using Stacked Denoising Auto-encoder (SDAE) and for backpropagation we have used Multilayer Perceptron (ML...
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| Vydané v: | Procedia computer science Ročník 167; s. 686 - 695 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
2020
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| ISSN: | 1877-0509, 1877-0509 |
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| Abstract | In this work, we form stacked denoising auto encoder model which is recognized few feasible genes mediating human lung adenocarcinoma. At first we have trained the data for feature selection by using Stacked Denoising Auto-encoder (SDAE) and for backpropagation we have used Multilayer Perceptron (MLP) procedure. We said this model is MLP-SDAE. The process include classification of genes according to correlation coefficient value and select few feasible genes. The superiority of the method has been established some present gene selection procedures like Support Vector Machine (SVM), Significance Analysis of Microarry (SAM), Bayesian Regularization (BR), Neighborhood Analysis (NA), and Gaussian Mixture Model (GMM). The MLP-SDAE model has been effectively used to one human lung microarray gene expression data. The result are appropriately verify by preliminary analysis, t-test, and gene expression profile plots. In this method, we have established more number of true positive genes then another present procedures. |
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| AbstractList | In this work, we form stacked denoising auto encoder model which is recognized few feasible genes mediating human lung adenocarcinoma. At first we have trained the data for feature selection by using Stacked Denoising Auto-encoder (SDAE) and for backpropagation we have used Multilayer Perceptron (MLP) procedure. We said this model is MLP-SDAE. The process include classification of genes according to correlation coefficient value and select few feasible genes. The superiority of the method has been established some present gene selection procedures like Support Vector Machine (SVM), Significance Analysis of Microarry (SAM), Bayesian Regularization (BR), Neighborhood Analysis (NA), and Gaussian Mixture Model (GMM). The MLP-SDAE model has been effectively used to one human lung microarray gene expression data. The result are appropriately verify by preliminary analysis, t-test, and gene expression profile plots. In this method, we have established more number of true positive genes then another present procedures. |
| Author | Ghosh, Anupam Chakrabarti, Amlan Ghosh, Ranjan Sheet, Sougata |
| Author_xml | – sequence: 1 givenname: Sougata surname: Sheet fullname: Sheet, Sougata email: sougata.sheet@gmail.com organization: A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata-700106, India – sequence: 2 givenname: Anupam surname: Ghosh fullname: Ghosh, Anupam organization: Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata-700152, India – sequence: 3 givenname: Ranjan surname: Ghosh fullname: Ghosh, Ranjan organization: A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata-700106, India – sequence: 4 givenname: Amlan surname: Chakrabarti fullname: Chakrabarti, Amlan organization: A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata-700106, India |
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| Keywords | Multilayer perceptron t-test Deep neural network Auto-encoder p-value Gene expression |
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| Title | Identification of Cancer Mediating Biomarkers using Stacked Denoising Autoencoder Model - An Application on Human Lung Data |
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