000164502 001__ 164502
000164502 005__ 20230103103140.0
000164502 020__ $$a978-3-030-20241-5 (print)
000164502 020__ $$a978-3-030-20242-2 (electronic)
000164502 0247_ $$2doi$$a10.1007/978-3-030-20242-2_14
000164502 0247_ $$2ISSN$$a0302-9743
000164502 0247_ $$2ISSN$$a1611-3349
000164502 0247_ $$2altmetric$$aaltmetric:61330992
000164502 037__ $$aDZNE-2022-01054
000164502 1001_ $$aCai, Zhipeng$$b0$$eEditor
000164502 1112_ $$aInternational Symposium on Bioinformatics Research and Applications
000164502 245__ $$aDeep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles
000164502 260__ $$aCham$$bSpringer International Publishing$$c2019
000164502 29510 $$aBioinformatics Research and Applications / Cai, Zhipeng (Editor) ; Cham : Springer International Publishing, 2019, Chapter 14 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-030-20241-5=978-3-030-20242-2 ; doi:10.1007/978-3-030-20242-2
000164502 300__ $$a159 - 170
000164502 3367_ $$2ORCID$$aCONFERENCE_PAPER
000164502 3367_ $$033$$2EndNote$$aConference Paper
000164502 3367_ $$2BibTeX$$aINPROCEEDINGS
000164502 3367_ $$2DRIVER$$aconferenceObject
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000164502 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1654068792_17981
000164502 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000164502 4900_ $$aLecture Notes in Computer Science$$v11490
000164502 520__ $$aThe lack of well-structured annotations in a growing amount of RNA expression data complicates data interoperability and reusability. Commonly used text mining methods extract annotations from existing unstructured data descriptions and often provide inaccurate output that requires manual curation. Automatic data-based augmentation (generation of annotations on the base of expression data) can considerably improve the annotation quality and has not been well-studied. We formulate an automatic augmentation of small RNA-seq expression data as a classification problem and investigate deep learning (DL) and random forest (RF) approaches to solve it. We generate tissue and sex annotations from small RNA-seq expression data for tissues and cell lines of homo sapiens. We validate our approach on 4243 annotated small RNA-seq samples from the Small RNA Expression Atlas (SEA) database. The average prediction accuracy for tissue groups is 98% (DL), for tissues - 96.5% (DL), and for sex - 77% (DL). The “one dataset out” average accuracy for tissue group prediction is 83% (DL) and 59% (RF). On average, DL provides better results as compared to RF, and considerably improves classification performance for ‘unseen’ datasets.
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000164502 588__ $$aDataset connected to CrossRef Book Series, Journals: pub.dzne.de
000164502 7001_ $$aSkums, Pavel$$b1$$eEditor
000164502 7001_ $$00000-0002-0188-1394$$aLi, Min$$b2$$eEditor
000164502 7001_ $$aFiosina, Jelena$$b3
000164502 7001_ $$0P:(DE-2719)2811935$$aFiosins, Maksims$$b4$$udzne
000164502 7001_ $$0P:(DE-2719)2810547$$aBonn, Stefan$$b5$$eLast author$$udzne
000164502 773__ $$a10.1007/978-3-030-20242-2_14
000164502 909CO $$ooai:pub.dzne.de:164502$$pVDB
000164502 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2811935$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b4$$kDZNE
000164502 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2810547$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b5$$kDZNE
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000164502 9141_ $$y2019
000164502 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2020-08-25$$wger
000164502 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-08-25
000164502 9201_ $$0I:(DE-2719)1210002$$kAG Heutink 1$$lGenome Biology of Neurodegenerative Diseases$$x0
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000164502 980__ $$aI:(DE-2719)1210002
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