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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Next-generation Sequencing03:00

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Related Experiment Video

Updated: Dec 6, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Application of deep learning in genomics.

Jianxiao Liu1,2, Jiying Li3, Hai Wang4

  • 1National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.

Science China. Life Sciences
|October 14, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning significantly enhances biological data analysis for genomics and synthetic biology. This review covers its applications from DNA to protein, discussing challenges and future directions in the field.

Keywords:
convolutional neural networkdeep learninggenomics

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Area of Science:

  • Genomics
  • Synthetic Biology
  • Bioinformatics

Background:

  • Deep learning excels in complex pattern recognition within high-dimensional data, outperforming conventional methods.
  • Its application is rapidly growing in biological research for predicting genomic element functions and gene expression.
  • Understanding deep learning is crucial for advancing biological data analysis.

Purpose of the Study:

  • To review fundamental concepts of machine learning and artificial neural networks.
  • To elaborate on the workflow of convolutional neural networks in genomics.
  • To provide an overview of deep learning applications across DNA, RNA, and protein levels in genomics and synthetic biology.

Main Methods:

  • Description of basic machine learning and artificial neural network concepts.
  • Elaboration on the workflow of convolutional neural networks (CNNs) for genomic data.
  • Review of existing literature on deep learning applications in genomics and synthetic biology.

Main Results:

  • Deep learning demonstrates superior performance in complex biological prediction tasks.
  • Applications span DNA, RNA, and protein analyses, aiding in understanding genomic elements and gene expression.
  • The review synthesizes current deep learning methodologies and their impact.

Conclusions:

  • Deep learning offers powerful tools for deciphering complex biological data.
  • Significant challenges remain, including data integration and model interpretability.
  • Future research should focus on addressing these challenges to further unlock deep learning's potential in genomics and synthetic biology.