TY  - CONF
AU  - Fiosins, Maksims
AU  - Bonn, Stefan
TI  - Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles
M1  - DZNE-2020-00942
PY  - 2019
AB  - 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
T2  - 15th International Symposium on Bioinformatics Research and Applications (ISBRA)
CY  - 3 Jun 2019 - 6 Jun 2019, Barcelona (Spain)
Y2  - 3 Jun 2019 - 6 Jun 2019
M2  - Barcelona, Spain
LB  - PUB:(DE-HGF)6
UR  - https://pub.dzne.de/record/145612
ER  -