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Artificial intelligence used in genome analysis studies Cover

Artificial intelligence used in genome analysis studies

The EuroBiotech Journal
Volume 2 (2018): Issue 2 (April 2018)
By: Edo D’Agaro  
Open Access
|Apr 2018

Figures & Tables

Figure 1

Example of an artificial neuron.
Example of an artificial neuron.

Figure 2

A layered network with three layers.
A layered network with three layers.

Figure 3

Increase of the prediction accuracy as a function of the data training size.
Increase of the prediction accuracy as a function of the data training size.

Figure 4

Example of sparse connectivity of neurons between different convolutional layers: the neurons of a layer m are connected only to some of the neurons of the previous layer m-1.
Example of sparse connectivity of neurons between different convolutional layers: the neurons of a layer m are connected only to some of the neurons of the previous layer m-1.

Figure 5

Example of weight sharing (feature map) between different neurons convolutional layers, a link of the same color corresponds to equal weight
Example of weight sharing (feature map) between different neurons convolutional layers, a link of the same color corresponds to equal weight

Main genomic programs concerned with cancer and rare diseases

Internet siteProgramThematic area
www. allofus.nih.gov/All of Us Research ProgramCancer, rare diseases, complex traits
www.australiangenomics.org.auAustralian GenomicsCancer, rare diseases, complex traits
www.brcaexchange.orgBRCA ChallengeCancer, rare diseases, complex traits
www.candig.github.ioCanadian Distributed Infrastructure for Genom- icsCancer, rare diseases, basic biology
www.clinicalgenome.orgClinical Genome ResourceRare diseases
www.elixir-europe.orgELIXIR BeaconRare diseases, basic biology
www.ebi.ac.ukEuropean Genome-Phenome ArchiveRare diseases, basic biology
www.genomicsengland.co.ukGenomics EnglandCancer, rare diseases, complex traits
www.humancellatlas.org/Human Cell AtlasCancer, rare diseases, complex traits, basic biology
www.icgcmed.orgICGC-ARGOCancer
www.matchmakerexchange.orgMatchmaker ExchangeRare diseases
www.gdc.cancer.govNational Cancer Institute Genomic Data Com- monsCancer, rare diseases
www.monarchinitiative.orgMonarch InitiativeRare diseases, complex traits, basic biology
www.nhlbiwgs.orgTrans-Omics for Precision MedicineRare diseases, complex traits, basic biology
www.cancervariants.orgVariant Interpretation for Cancer ConsortiumCancer

Main software tools used for machine learning studies

Internet siteProgram nameThematic area
www.sourceforge.net/p/fingeridFingerIDMolecular fingerprinting
https://bio.informatik.uni-jena.de/software/sirius/SIRIUSMolecular fingerprinting
http://www.metaboanalyst.ca/MetaboanalystMetabolomics analysis
www.knime.com/KNIMEMachine learning tool
www.cs.waikato.ac.nz/ml/weka/WekaMachine learning tool
https://orange.biolab.si/OrangeMachine learning tool
http://cafe.berkeleyvision.org/TensorFlowMachine learning tool
http://cafe.berkeleyvision.org/CafeMachine learning tool
http://deeplearning.net/software/theano/TheanoMachine learning tool
http://torch.ch/TorchMachine learning tool
DOI: https://doi.org/10.2478/ebtj-2018-0012 | Journal eISSN: 2564-615X
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Language: English
Page range: 78 - 88
Published on: Apr 25, 2018
Published by: European Biotechnology Thematic Network Association
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
deep learning,
NGS,
genomics,
molecular diagnosis
Related subjects:
Life sciences,
Genetics,
Biotechnology,
Bioinformatics,
Life sciences, other

© 2018 Edo D’Agaro, published by European Biotechnology Thematic Network Association
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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