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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Deep learning in bioinformatics.

Malik Yousef¹, Jens Allmer²

¹Department of Information Systems, Zefat Academic College, Zefat, Israel.

Turkish Journal of Biology = Turk Biyoloji Dergisi

|April 29, 2024

Summary

This summary is machine-generated.

This review explores deep learning applications in bioinformatics, covering genome sequencing, drug discovery, and disease diagnosis. It highlights technical and ethical considerations for researchers using these powerful AI models.

Keywords:

Deep learning bioinformatics biological data analysis neural networks

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Area of Science:

Bioinformatics
Machine Learning
Artificial Intelligence

Background:

Deep learning, a subset of machine learning, utilizes multi-layered artificial neural networks to analyze vast datasets.
Bioinformatics integrates computational, mathematical, and statistical approaches to analyze biological data.

Purpose of the Study:

To review current deep learning applications in bioinformatics.
To discuss challenges and future directions for deep learning in biological data analysis.
To guide biomedical informatics researchers on technical and ethical aspects of deep learning model implementation.

Main Methods:

Review of recent advancements in deep learning for bioinformatics.
Survey of applications across various biological domains.
Discussion of critical technical and ethical considerations.

Main Results:

Deep learning shows significant promise in areas like genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis.
Identified challenges include data requirements, model interpretability, and potential biases.
Future opportunities lie in refining models and addressing ethical implications.

Conclusions:

Deep learning offers powerful tools for bioinformatics research.
Adoption requires careful consideration of fairness, bias, explainability, and accountability.
This review encourages broader, responsible use of deep learning in biological data analysis.