Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F23%3A43898000" target="_blank" >RIV/44555601:13440/23:43898000 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-3417/13/21/11823" target="_blank" >https://www.mdpi.com/2076-3417/13/21/11823</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app132111823" target="_blank" >10.3390/app132111823</a>

Result language

angličtina

Original language name

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

Original language description

The importance of gene expression data processing in solving the classification task is determined by its ability to discern intricate patterns and relationships within genetic information, enabling the precise categorization and understanding of various gene expression profiles and their consequential impacts on biological processes and traits. In this study, we investigated various architectures and types of recurrent neural networks focusing on gene expression data. The effectiveness of the appropriate model was evaluated using various classification quality criteria based on type 1 and type 2 errors. Moreover, we calculated the integrated F1-score index using the Harrington desirability method, the value of which allowed us to improve the objectivity of the decision making when model effectiveness was evaluated. The final decision regarding model effectiveness was made based on a comprehensive classification quality criterion, which was calculated as the weighted sum of classification accuracy, integrated F1-score index, and loss function values. The simulation results show higher appeal of a single-layer GRU recurrent network with 75 neurons in the recurrent layer. We also compared convolutional and recurrent neural networks on gene expression data classification. Although convolutional neural networks showcase benefits in terms of loss function value and training time, a comparative analysis revealed that in terms of classification accuracy calculated on the test data subset, the GRU neural network model is slightly better than the CNN and LSTM models. The classification accuracy when using the GRU network was 97.2%; in other cases, it was 97.1%. In the first case, 954 out of 981 objects were correctly identified. In other cases, 952 objects were correctly identified.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Volume of the periodical

13

Issue of the periodical within the volume

21

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

"nestrankovano"

UT code for WoS article

001099489500001

EID of the result in the Scopus database

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