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Deep learning architectures for the prediction of YY1-mediated chromatin loops
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; University of Kaiserslautern-Landau, Kaiserslautern (RPTU), Germany.
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Umeå University, Faculty of Science and Technology, Department of Chemistry. Sartorius Corporate Research, Sartorius Stedim Data Analytics, Umeå, Sweden.ORCID iD: 0000-0003-3799-6094
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; University of Kaiserslautern-Landau, Kaiserslautern (RPTU), Germany.
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2023 (English)In: Bioinformatics research and applications: 19th international symposium, ISBRA 2023, Wrocław, Poland, October 9–12, 2023, proceedings / [ed] Xuan Guo; Serghei Mangul; Murray Patterson; Alexander Zelikovsky, Springer, 2023, p. 72-84Conference paper, Published paper (Refereed)
Abstract [en]

YY1-mediated chromatin loops play substantial roles in basic biological processes like gene regulation, cell differentiation, and DNA replication. YY1-mediated chromatin loop prediction is important to understand diverse types of biological processes which may lead to the development of new therapeutics for neurological disorders and cancers. Existing deep learning predictors are capable to predict YY1-mediated chromatin loops in two different cell lines however, they showed limited performance for the prediction of YY1-mediated loops in the same cell lines and suffer significant performance deterioration in cross cell line setting. To provide computational predictors capable of performing large-scale analyses of YY1-mediated loop prediction across multiple cell lines, this paper presents two novel deep learning predictors. The two proposed predictors make use of Word2vec, one hot encoding for sequence representation and long short-term memory, and a convolution neural network along with a gradient flow strategy similar to DenseNet architectures. Both of the predictors are evaluated on two different benchmark datasets of two cell lines HCT116 and K562. Overall the proposed predictors outperform existing DEEPYY1 predictor with an average maximum margin of 4.65%, 7.45% in terms of AUROC, and accuracy, across both of the datases over the independent test sets and 5.1%, 3.2% over 5-fold validation. In terms of cross-cell evaluation, the proposed predictors boast maximum performance enhancements of up to 9.5% and 27.1% in terms of AUROC over HCT116 and K562 datasets.

Place, publisher, year, edition, pages
Springer, 2023. p. 72-84
Series
Lecture Notes in Computer Science (LNBI), ISSN 0302-9743, E-ISSN 1611-3349 ; 14248
Keywords [en]
Chromatin loops, Convolutional Networks, Gene regulation, LSTM, One hot encoding, Word2vec, YY1
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:umu:diva-215831DOI: 10.1007/978-981-99-7074-2_6Scopus ID: 2-s2.0-85174274462ISBN: 9789819970735 (print)ISBN: 9789819970742 (electronic)OAI: oai:DiVA.org:umu-215831DiVA, id: diva2:1809751
Conference
19th International Symposium on Bioinformatics Research and Applications, ISBRA 2023
Available from: 2023-11-06 Created: 2023-11-06 Last updated: 2023-11-06Bibliographically approved

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Trygg, Johan

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