In recent years, deep learning algorithms have outperformed the state-of-the
art methods in several areas thanks to the efficient methods for training and
for preventing overfitting, advancement in computer hardware, the availability
of vast amount data. The high performance of multi-task deep neural networks in
drug discovery has attracted the attention to deep learning algorithms in
bioinformatics area. Here, we proposed a hierarchical multi-task deep neural
network architecture based on Gene Ontology (GO) terms as a solution to protein
function prediction problem and investigated various aspects of the proposed
architecture by performing several experiments. First, we showed that there is
a positive correlation between performance of the system and the size of
training datasets. Second, we investigated whether the level of GO terms on GO
hierarchy related to their performance. We showed that there is no relation
between the depth of GO terms on GO hierarchy and their performance. In
addition, we included all annotations to the training of a set of GO terms to
investigate whether including noisy data to the training datasets change the
performance of the system. The results showed that including less reliable
annotations in training of deep neural networks increased the performance of
the low performed GO terms, significantly. We evaluated the performance of the
system using hierarchical evaluation method. Mathews correlation coefficient
was calculated as 0.75, 0.49 and 0.63 for molecular function, biological
process and cellular component categories, respectively. We showed that deep
learning algorithms have a great potential in protein function prediction area.
We plan to further improve the DEEPred by including other types of annotations
from various biological data sources. We plan to construct DEEPred as an open
access online tool.Comment: 19 pages, 4 figures, 4 table
