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EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction
Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany.
Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany.
School of Computer Science & Electrical Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
Sartorius Corporate Research, Sartorius Stedim Cellca GmbH, Ulm, Germany.
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2022 (English)In: Computational and Structural Biotechnology Journal, E-ISSN 2001-0370, Vol. 20, p. 3986-4002Article in journal (Refereed) Published
Abstract [en]

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network “EL-RMLocNet”, predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds_genetic_analysis.opendfki.de/subcellular_loc/).

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 20, p. 3986-4002
Keywords [en]
Attention mechanism, Deep learning, Explainable, GeneticSeq2Vec, Human, LSTM, Mouse, Multi-class, Multi-label, Neural tricks, RNA subcellular localization prediction, Single or multi compartment
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:umu:diva-198571DOI: 10.1016/j.csbj.2022.07.031ISI: 000889723800009Scopus ID: 2-s2.0-85135296076OAI: oai:DiVA.org:umu-198571DiVA, id: diva2:1687008
Available from: 2022-08-12 Created: 2022-08-12 Last updated: 2023-09-05Bibliographically approved

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Trygg, Johan

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