<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...

Internet site	Program	Thematic area
www. allofus.nih.gov/	All of Us Research Program	Cancer, rare diseases, complex traits
www.australiangenomics.org.au	Australian Genomics	Cancer, rare diseases, complex traits
www.brcaexchange.org	BRCA Challenge	Cancer, rare diseases, complex traits
www.candig.github.io	Canadian Distributed Infrastructure for Genom- ics	Cancer, rare diseases, basic biology
www.clinicalgenome.org	Clinical Genome Resource	Rare diseases
www.elixir-europe.org	ELIXIR Beacon	Rare diseases, basic biology
www.ebi.ac.uk	European Genome-Phenome Archive	Rare diseases, basic biology
www.genomicsengland.co.uk	Genomics England	Cancer, rare diseases, complex traits
www.humancellatlas.org/	Human Cell Atlas	Cancer, rare diseases, complex traits, basic biology
www.icgcmed.org	ICGC-ARGO	Cancer
www.matchmakerexchange.org	Matchmaker Exchange	Rare diseases
www.gdc.cancer.gov	National Cancer Institute Genomic Data Com- mons	Cancer, rare diseases
www.monarchinitiative.org	Monarch Initiative	Rare diseases, complex traits, basic biology
www.nhlbiwgs.org	Trans-Omics for Precision Medicine	Rare diseases, complex traits, basic biology
www.cancervariants.org	Variant Interpretation for Cancer Consortium	Cancer

Internet site	Program name	Thematic area
www.sourceforge.net/p/fingerid	FingerID	Molecular fingerprinting
https://bio.informatik.uni-jena.de/software/sirius/	SIRIUS	Molecular fingerprinting
http://www.metaboanalyst.ca/	Metaboanalyst	Metabolomics analysis
www.knime.com/	KNIME	Machine learning tool
www.cs.waikato.ac.nz/ml/weka/	Weka	Machine learning tool
https://orange.biolab.si/	Orange	Machine learning tool
http://cafe.berkeleyvision.org/	TensorFlow	Machine learning tool
http://cafe.berkeleyvision.org/	Cafe	Machine learning tool
http://deeplearning.net/software/theano/	Theano	Machine learning tool
http://torch.ch/	Torch	Machine learning tool

Artificial intelligence used in genome analysis studies

Article Category: Review

Publié en ligne: 25 avr. 2018

Pages: 78 - 88

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

Mots clés
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.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Main genomic programs concerned with cancer and rare diseases

Main software tools used for machine learning studies

Artificial intelligence used in genome analysis studies

Article Category: Review

Publié en ligne: 25 avr. 2018

Pages: 78 - 88

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

Mots clésdeep 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.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Main genomic programs concerned with cancer and rare diseases

Main software tools used for machine learning studies

Mots clés
deep learning, NGS, genomics, molecular diagnosis