A review on convolutional neural network based deep learning methods in gene expression data for disease diagnosis
| Autor: | C. Gunavathi, C Paramasivam, P. Keerthika, K. Sivasubramanian |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Artificial neural network business.industry Deep learning 02 engineering and technology 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Convolutional neural network Cancer treatment ComputingMethodologies_PATTERNRECOGNITION Fragment (logic) Informatics 0103 physical sciences Gene expression ComputingMethodologies_GENERAL Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | Materials Today: Proceedings. 45:2282-2285 |
| ISSN: | 2214-7853 |
| DOI: | 10.1016/j.matpr.2020.10.263 |
| Popis: | Bioinformatics is the discipline of employing informatics technologies on biological datasets to extract the hidden knowledge from both biology and computer science fields. Gene expression datasets are widely used in disease prediction and diagnosis especially in cancer treatment. There are many computational techniques that are available for gene expression analysis. Deep learning methods are a fragment of machine learning techniques which are based on artificial neural networks. Convolutional neural networks are the most important deep learning model that is designed for data that comes in the form of multidimensional arrays. This paper reviews the recent research works that utilize convolutional neural network deep learning methods on gene expression data analysis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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