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GPU Based Composite Elements Discovery in Large DNADatasets. / Vishnevsky, Oleg; Bocharnikov, Andrey; Kolchanov, Nikolay.

Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 135-138 9214777 (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Vishnevsky, O, Bocharnikov, A & Kolchanov, N 2020, GPU Based Composite Elements Discovery in Large DNADatasets. in Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020., 9214777, Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020, Institute of Electrical and Electronics Engineers Inc., pp. 135-138, 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020, Novosibirsk, Russian Federation, 06.07.2020. https://doi.org/10.1109/CSGB51356.2020.9214777

APA

Vishnevsky, O., Bocharnikov, A., & Kolchanov, N. (2020). GPU Based Composite Elements Discovery in Large DNADatasets. In Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020 (pp. 135-138). [9214777] (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSGB51356.2020.9214777

Vancouver

Vishnevsky O, Bocharnikov A, Kolchanov N. GPU Based Composite Elements Discovery in Large DNADatasets. In Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 135-138. 9214777. (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020). doi: 10.1109/CSGB51356.2020.9214777

Author

Vishnevsky, Oleg ; Bocharnikov, Andrey ; Kolchanov, Nikolay. / GPU Based Composite Elements Discovery in Large DNADatasets. Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 135-138 (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020).

BibTeX

@inproceedings{f8ccf6e1a1e84dfead43a85479773c71,
title = "GPU Based Composite Elements Discovery in Large DNADatasets",
abstract = "Composite elements play an important role in the regulation of transcription. Existing methods for the revealing of potential composite elements are usually based on assessment of the significance of the mutual presence of the predicted transcription factor binding sites using weight matrices or other methods trained on samples of binding sites of known transcription factors. Thus, such methods essentially depend on the completeness of training samples and information on existing TFs. We have proposed a method for de novo discovery of potential composite elements, which does not require preliminary information about the localization of potential TFBS. Using the proposed approach, context signals are identified in the ChIP-Seq dataset, which can correspond to potential composite elements.",
keywords = "ChIP-Seq, composite elements, oligonucleotide motifs, transcription regulation",
author = "Oleg Vishnevsky and Andrey Bocharnikov and Nikolay Kolchanov",
year = "2020",
month = jul,
doi = "10.1109/CSGB51356.2020.9214777",
language = "English",
series = "Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "135--138",
booktitle = "Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020",
address = "United States",
note = "2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020 ; Conference date: 06-07-2020 Through 10-07-2020",

}

RIS

TY - GEN

T1 - GPU Based Composite Elements Discovery in Large DNADatasets

AU - Vishnevsky, Oleg

AU - Bocharnikov, Andrey

AU - Kolchanov, Nikolay

PY - 2020/7

Y1 - 2020/7

N2 - Composite elements play an important role in the regulation of transcription. Existing methods for the revealing of potential composite elements are usually based on assessment of the significance of the mutual presence of the predicted transcription factor binding sites using weight matrices or other methods trained on samples of binding sites of known transcription factors. Thus, such methods essentially depend on the completeness of training samples and information on existing TFs. We have proposed a method for de novo discovery of potential composite elements, which does not require preliminary information about the localization of potential TFBS. Using the proposed approach, context signals are identified in the ChIP-Seq dataset, which can correspond to potential composite elements.

AB - Composite elements play an important role in the regulation of transcription. Existing methods for the revealing of potential composite elements are usually based on assessment of the significance of the mutual presence of the predicted transcription factor binding sites using weight matrices or other methods trained on samples of binding sites of known transcription factors. Thus, such methods essentially depend on the completeness of training samples and information on existing TFs. We have proposed a method for de novo discovery of potential composite elements, which does not require preliminary information about the localization of potential TFBS. Using the proposed approach, context signals are identified in the ChIP-Seq dataset, which can correspond to potential composite elements.

KW - ChIP-Seq

KW - composite elements

KW - oligonucleotide motifs

KW - transcription regulation

UR - http://www.scopus.com/inward/record.url?scp=85094816360&partnerID=8YFLogxK

UR - https://www.elibrary.ru/item.asp?id=45183799

U2 - 10.1109/CSGB51356.2020.9214777

DO - 10.1109/CSGB51356.2020.9214777

M3 - Conference contribution

AN - SCOPUS:85094816360

T3 - Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

SP - 135

EP - 138

BT - Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

Y2 - 6 July 2020 through 10 July 2020

ER -

ID: 25863007