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About Deep Learning: New Computational Modelling Techniques For Genomics
This document discusses how deep learning techniques can be applied to genomics modeling tasks. It begins by explaining how genomics has traditionally relied on machine learning to analyze large datasets but that deep learning models may provide more powerful approaches. The document then reviews four major classes of neural networks commonly used in genomics: fully connected, convolutional, recurrent, and graph convolutional networks. It also discusses techniques like multitask learning, transfer learning, and unsupervised models like autoencoders and generative adversarial networks that are relevant for genomics applications. The goal is to provide an overview of deep learning methods and their potential to derive novel biological insights from genomic data.
Detailed Information
| Author: | ['Pandikannan Krishnamoorthy'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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