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EL-RMLocNet :
EL-RMLocNet : An explainable LSTM network for RNA-associated multi-compartment localization prediction
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- Asim, Muhammad Nabeel (author)
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
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- Ibrahim, Muhammad Ali (author)
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
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- Malik, Muhammad Imran (author)
- School of Computer Science & Electrical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
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- Zehe, Christoph (author)
- Sartorius Corporate Research, Sartorius Stedim Cellca GmbH, Ulm, Germany
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- Cloarec, Olivier (author)
- Sartorius Corporate Research, Sartorius Stedim Cellca GmbH, Ulm, Germany
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- Trygg, Johan (author)
- Umeå universitet,Kemiska institutionen,Sartorius Corporate Research, Sartorius Stedim Data Analytics, Umeå, Sweden
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- Dengel, Andreas (author)
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
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- Ahmed, Sheraz (author)
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
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(creator_code:org_t)
- Elsevier, 2022
- 2022
- English.
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In: Computational and Structural Biotechnology Journal. - : Elsevier. - 2001-0370. ; 20, s. 3986-4002
- Related links:
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https://umu.diva-por... (primary) (Raw object)
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Abstract
Subject headings
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- 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/).
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Keyword
- Attention mechanism
- Deep learning
- Explainable
- GeneticSeq2Vec
- Human
- LSTM
- Mouse
- Multi-class
- Multi-label
- Neural tricks
- RNA subcellular localization prediction
- Single or multi compartment
Publication and Content Type
- ref (subject category)
- art (subject category)
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