guest ::
| Home > Publications Database > Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles > print |
| Home > Publications Database > Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles > print |
| 001 | 164502 | ||
| 005 | 20230103103140.0 | ||
| 020 | _ | _ | |a 978-3-030-20241-5 (print) |
| 020 | _ | _ | |a 978-3-030-20242-2 (electronic) |
| 024 | 7 | _ | |a 10.1007/978-3-030-20242-2_14 |2 doi |
| 024 | 7 | _ | |a 0302-9743 |2 ISSN |
| 024 | 7 | _ | |a 1611-3349 |2 ISSN |
| 024 | 7 | _ | |a altmetric:61330992 |2 altmetric |
| 037 | _ | _ | |a DZNE-2022-01054 |
| 100 | 1 | _ | |a Cai, Zhipeng |b 0 |e Editor |
| 111 | 2 | _ | |a International Symposium on Bioinformatics Research and Applications |
| 245 | _ | _ | |a Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles |
| 260 | _ | _ | |a Cham |c 2019 |b Springer International Publishing |
| 295 | 1 | 0 | |a Bioinformatics 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 |
| 300 | _ | _ | |a 159 - 170 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1654068792_17981 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 490 | 0 | _ | |a Lecture Notes in Computer Science |v 11490 |
| 520 | _ | _ | |a The 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. |
| 536 | _ | _ | |a 899 - ohne Topic (POF4-899) |0 G:(DE-HGF)POF4-899 |c POF4-899 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef Book Series, Journals: pub.dzne.de |
| 700 | 1 | _ | |a Skums, Pavel |b 1 |e Editor |
| 700 | 1 | _ | |a Li, Min |0 0000-0002-0188-1394 |b 2 |e Editor |
| 700 | 1 | _ | |a Fiosina, Jelena |b 3 |
| 700 | 1 | _ | |a Fiosins, Maksims |0 P:(DE-2719)2811935 |b 4 |u dzne |
| 700 | 1 | _ | |a Bonn, Stefan |0 P:(DE-2719)2810547 |b 5 |e Last author |u dzne |
| 773 | _ | _ | |a 10.1007/978-3-030-20242-2_14 |
| 909 | C | O | |o oai:pub.dzne.de:164502 |p VDB |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 4 |6 P:(DE-2719)2811935 |
| 910 | 1 | _ | |a Deutsches Zentrum für Neurodegenerative Erkrankungen |0 I:(DE-588)1065079516 |k DZNE |b 5 |6 P:(DE-2719)2810547 |
| 913 | 1 | _ | |a DE-HGF |b Programmungebundene Forschung |l ohne Programm |1 G:(DE-HGF)POF4-890 |0 G:(DE-HGF)POF4-899 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-800 |4 G:(DE-HGF)POF |v ohne Topic |x 0 |
| 914 | 1 | _ | |y 2019 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2020-08-25 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2020-08-25 |
| 920 | 1 | _ | |0 I:(DE-2719)1210002 |k AG Heutink 1 |l Genome Biology of Neurodegenerative Diseases |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a contb |
| 980 | _ | _ | |a I:(DE-2719)1210002 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|