ENNGene: an Easy Neural Network model building tool for Genomics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F22%3A00125837" target="_blank" >RIV/00216224:14740/22:00125837 - isvavai.cz</a>
Result on the web
<a href="https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-022-08414-x.pdf" target="_blank" >https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-022-08414-x.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1186/s12864-022-08414-x" target="_blank" >10.1186/s12864-022-08414-x</a>
Alternative languages
Result language
angličtina
Original language name
ENNGene: an Easy Neural Network model building tool for Genomics
Original language description
Background The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. Results Here we present ENNGene-Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. Conclusions As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10603 - Genetics and heredity (medical genetics to be 3)
Result continuities
Project
<a href="/en/project/EF18_053%2F0016952" target="_blank" >EF18_053/0016952: Postdoc2MUNI</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
BMC Genomics
ISSN
1471-2164
e-ISSN
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Volume of the periodical
23
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
248
UT code for WoS article
000776723500002
EID of the result in the Scopus database
2-s2.0-85127451701