  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.
