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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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Current progress and open challenges for applying deep learning across the biosciences.

Nicolae Sapoval¹, Amirali Aghazadeh², Michael G Nute¹

¹Department of Computer Science, Rice University, Houston, TX, USA.

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|April 2, 2022

Summary

This summary is machine-generated.

Deep learning (DL) is revolutionizing computational biology, significantly advancing protein structure prediction. This review explores DL

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

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Published on: September 25, 2021

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Protein structure prediction is a long-standing challenge in computational biology.
Deep Learning (DL) has emerged as a powerful tool for biological data analysis.

Purpose of the Study:

To review recent advances in DL applications across five key areas of biosciences.
To identify limitations and future perspectives of DL in these areas.

Main Methods:

Review of recent literature on DL in computational biology.
Analysis of DL applications in protein structure/function prediction, genome engineering, systems biology, and phylogenetic inference.

Main Results:

DL has achieved significant breakthroughs, particularly in protein structure prediction.
Key bottlenecks include training data, problem scope, and adaptability of DL architectures.

Conclusions:

DL holds immense potential for biosciences but faces challenges.
Addressing data limitations and architectural adaptability is crucial for future progress.