<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Next Generation Sequencing (NGS) or deep sequencing technology enables parallel reading of multiple individual DNA fragments, thereby enabling the identification of millions of base pairs in several hours. Recent research has clearly shown that machine learning technologies can efficiently analyse large sets of genomic data and help to identify novel gene functions and regulation regions. A deep artificial neural network consists of a group of artificial neurons that mimic the properties of living neurons. These mathematical models, termed Artificial Neural Networks (ANN), can be used to solve artificial intelligence engineering problems in several different technological fields (e.g., biology, genomics, proteomics, and metabolomics). In practical terms, neural networks are non-linear statistical structures that are organized as modelling tools and are used to simulate complex genomic relationships between inputs and outputs. To date, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) have been demonstrated to be the best tools for improving performance in problem solving tasks within the genomic field.</p></abstract>

Next Generation Sequencing (NGS) or deep sequencing technology enables parallel reading of multiple individual DNA fragments, thereby enabling the identification of millions of base pairs in several hours. Recent research has clearly shown that machine learning technologies can efficiently analyse large sets of genomic data and help to identify novel gene functions and regulation regions. A deep artificial neural network consists of a group of artificial neurons that mimic the properties of living neurons. These mathematical models, termed Artificial Neural Networks (ANN), can be used to solve artificial intelligence engineering problems in several different technological fields (e.g., biology, genomics, proteomics, and metabolomics). In practical terms, neural networks are non-linear statistical structures that are organized as modelling tools and are used to simulate complex genomic relationships between inputs and outputs. To date, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) have been demonstrated to be the best tools for improving performance in problem solving tasks within the genomic field.

Artificial intelligence used in genome analysis studies

Department of Agricultural, Food, Environment and Animal Sciences

The EuroBiotech Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Artificial intelligence used in genome analysis studies"}

Next Generation Sequencing (NGS) or deep sequencing technology enables parallel reading of multiple individual DNA fragments, thereby enabling the...

Artificial intelligence used in genome analysis studies

Article Category: Review

Online veröffentlicht: 25. Apr. 2018

Seitenbereich: 78 - 88

DOI: https://doi.org/10.2478/ebtj-2018-0012

Schlüsselwörter
deep learning, NGS, genomics, molecular diagnosis

© 2018 Edo D’Agaro, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Artificial intelligence used in genome analysis studies

Article Category: Review

Online veröffentlicht: 25. Apr. 2018

Seitenbereich: 78 - 88

DOI: https://doi.org/10.2478/ebtj-2018-0012

Schlüsselwörterdeep learning, NGS, genomics, molecular diagnosis

© 2018 Edo D’Agaro, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
deep learning, NGS, genomics, molecular diagnosis