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Classification of cancer using deep learning techniques – a recent approach
Prakash B1, Rajalingam B2, Srihariakash K3, Poornima R. M4, Yashicka J. V5, Shen-Ming Chen6.
Introduction: Globally, cancer is the second foremost cause of death, next to heart disease. The name cancer refers to more than a thousand sicknesses illustrated by direct development and uncontrolled replication of multiple cells. In the recent years, the microarray datasets combined with machine learning methods are increasingly used to clas-sify the cancer in clinical conditions. Classification is one of the very broadly-used data mining techniques, to build a model that describes and distinguishes data classes in a manner to be used to predict the class of unseen instances. In machine learning, features are chosen manually for the classifier. Deep learning features extraction and model-ling steps that are automatic. Methods: Deep learning is one of the most significant forms of machine learning that requires computing systems to iteratively perform calculations to identify patterns by itself. Deep learning uses train-ing data to discover underlying patterns, build models and make predictions based on the best-fit model. Here we review deep learning for classification in bioinformatics; presenting examples of current research. Additionally, we discuss deep learning and convolutional neural network working principles to provide a useful and comprehensive perspective. This paper presents three works DeepGen, SDAE, and Enhance Feature learning in a brief description for each study. Conclusion: This review provides a comprehensive outlook and serve as a starting point for a clinical researcher to apply deep learning approaches for classification of gene expression profile in cancer specimens.
Affiliation:
- Vels Institute of Science, Technology and Advanced Studies, India
- Medisys Clinisearch India, India
- Kongu Engineering College, India
- Kongu Engineering College, India
- Vels Institute of Science, Technology and Advanced Studies, India
- National Taipei University of Technology, Taiwan
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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3 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (0.2) |
Rank |
Q4 (Medicine (all)) |
Additional Information |
SJR (0.144) |
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