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Speeding up the interpretation of differential gene expression analysis results. / Dordiuk, Vladislav; Demicheva, Ekaterina; Ushenin, Konstantin.
2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 560-565.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Dordiuk, V, Demicheva, E & Ushenin, K 2022, Speeding up the interpretation of differential gene expression analysis results. in 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022. Institute of Electrical and Electronics Engineers Inc., pp. 560-565, 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), 11/11/2022. https://doi.org/10.1109/SIBIRCON56155.2022.10017117

APA

Dordiuk, V., Demicheva, E., & Ushenin, K. (2022). Speeding up the interpretation of differential gene expression analysis results. In 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022 (pp. 560-565). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBIRCON56155.2022.10017117

Vancouver

Dordiuk V, Demicheva E, Ushenin K. Speeding up the interpretation of differential gene expression analysis results. In 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 560-565 doi: 10.1109/SIBIRCON56155.2022.10017117

Author

Dordiuk, Vladislav ; Demicheva, Ekaterina ; Ushenin, Konstantin. / Speeding up the interpretation of differential gene expression analysis results. 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 560-565

BibTeX

@inproceedings{ab5290e5dc1c42f8985ee7dba3af962e,
title = "Speeding up the interpretation of differential gene expression analysis results",
abstract = "The existing methods for interpreting the differential gene expression analysis results are mainly divided into three categories: cluster analysis, enrichment analysis, and the construction of genetic networks. Despite the rich abilities, all approaches take a lot of time to compute, and the final results are not always sufficient for understanding of the logic that binds genes into groups.In this paper, we propose a complete pipeline in order to make the process of understanding the results of differential gene expression analysis much faster, easier, and more efficient. The pipeline takes in Gene Ontology terms along with descriptions of collected genes, and returns the output of gene clusters, topics they are related to, and a filtered list of most common words that can be found in each of them. The processing involves an artificial neural network model BERT for semantic information extraction, BERTopic for unsupervised topic extraction, dimensional reduction for data simplification, and clustering for the search of dependencies.The pipeline was tested with ablation study and its performance was evaluated by an expert with gene expression datasets from NCBI GEO that include different types of cardiomyopathy: dilated, inflammatory, ischemic, non-ischemic, and healthy individuals.",
author = "Vladislav Dordiuk and Ekaterina Demicheva and Konstantin Ushenin",
note = "The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged.; 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) ; Conference date: 11-11-2022 Through 13-11-2022",
year = "2022",
month = nov,
day = "11",
doi = "10.1109/SIBIRCON56155.2022.10017117",
language = "English",
isbn = "978-166546480-2",
pages = "560--565",
booktitle = "2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

TY - GEN

T1 - Speeding up the interpretation of differential gene expression analysis results

AU - Dordiuk, Vladislav

AU - Demicheva, Ekaterina

AU - Ushenin, Konstantin

N1 - The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged.

PY - 2022/11/11

Y1 - 2022/11/11

N2 - The existing methods for interpreting the differential gene expression analysis results are mainly divided into three categories: cluster analysis, enrichment analysis, and the construction of genetic networks. Despite the rich abilities, all approaches take a lot of time to compute, and the final results are not always sufficient for understanding of the logic that binds genes into groups.In this paper, we propose a complete pipeline in order to make the process of understanding the results of differential gene expression analysis much faster, easier, and more efficient. The pipeline takes in Gene Ontology terms along with descriptions of collected genes, and returns the output of gene clusters, topics they are related to, and a filtered list of most common words that can be found in each of them. The processing involves an artificial neural network model BERT for semantic information extraction, BERTopic for unsupervised topic extraction, dimensional reduction for data simplification, and clustering for the search of dependencies.The pipeline was tested with ablation study and its performance was evaluated by an expert with gene expression datasets from NCBI GEO that include different types of cardiomyopathy: dilated, inflammatory, ischemic, non-ischemic, and healthy individuals.

AB - The existing methods for interpreting the differential gene expression analysis results are mainly divided into three categories: cluster analysis, enrichment analysis, and the construction of genetic networks. Despite the rich abilities, all approaches take a lot of time to compute, and the final results are not always sufficient for understanding of the logic that binds genes into groups.In this paper, we propose a complete pipeline in order to make the process of understanding the results of differential gene expression analysis much faster, easier, and more efficient. The pipeline takes in Gene Ontology terms along with descriptions of collected genes, and returns the output of gene clusters, topics they are related to, and a filtered list of most common words that can be found in each of them. The processing involves an artificial neural network model BERT for semantic information extraction, BERTopic for unsupervised topic extraction, dimensional reduction for data simplification, and clustering for the search of dependencies.The pipeline was tested with ablation study and its performance was evaluated by an expert with gene expression datasets from NCBI GEO that include different types of cardiomyopathy: dilated, inflammatory, ischemic, non-ischemic, and healthy individuals.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85147514896

U2 - 10.1109/SIBIRCON56155.2022.10017117

DO - 10.1109/SIBIRCON56155.2022.10017117

M3 - Conference contribution

SN - 978-166546480-2

SP - 560

EP - 565

BT - 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)

Y2 - 11 November 2022 through 13 November 2022

ER -

ID: 34716358