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L2S-MirLoc :
L2S-MirLoc : A Lightweight Two Stage MiRNA Sub-Cellular Localization Prediction Framework
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- Asim, Muhammad Nabeel (författare)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; Tu Kaiserslautern, Kaiserslautern, Germany
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- Ibrahim, Muhammad Ali (författare)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; Tu Kaiserslautern, Kaiserslautern, Germany
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- Zehe, Christoph (författare)
- Sartorius Corporate Research, Sartorius Stedim Cellca GmbH, Ulm, Germany
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- Cloarec, Olivier (författare)
- Sartorius Corporate Research, Sartorius Stedim Cellca GmbH, Ulm, Germany
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- Sjogren, Rickard (författare)
- Sartorius Corporate Research, Sartorius Stedim Cellca GmbH, Ulm, Germany
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- Trygg, Johan (författare)
- Umeå universitet,Kemiska institutionen,Sartorius Corporate Research, Sartorius Stedim Data Analytics, Umeå, Sweden
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- Dengel, Andreas (författare)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; Tu Kaiserslautern, Kaiserslautern, Germany
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- Ahmed, Sheraz (författare)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: Proceedings of the International Joint Conference on Neural Networks. - : IEEE. - 9780738133669 - 9781665439008 - 9781665445979
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- A comprehensive understanding of miRNA sub-cellular localization may leads towards better understanding of physiological processes and support the fixation of diverse irregularities present in a variety of organisms. To date, diverse computational methodologies have been proposed to automatically infer sub-cellular localization of miR-NAs solely using sequence information, however, existing approaches lack in performance. Considering the success of data transformation approaches in Natural Language Processing which primarily transform multi-label classification problem into multi-class classification problem, here, we introduce three different data transformation approaches namely binary relevance, label power set, and classifier chains. Using data transformation approaches, at 1st stage, multi-label miRNA sub-cellular localization problem is transformed into multi-class problem. Then, at 2nd stage, 3 different machine learning classifiers are used to estimate which classifier performs better with what data transformation approach for hand on task. Empirical evaluation on independent test set indicates that L2S-MirLoc selected combination based on binary relevance and deep random forest outperforms state-of-the-art performance values by significant margin.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
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