Akademik Veri Yönetim Sistemi
Balkan Journal of Electrical and Computer Engineering, cilt.9, sa.1, ss.23-32, 2021 (Hakemli Dergi)
In the field of biomedicine, applications for the
identification of biomarkers require a robust gene selection
mechanism. To identify the characteristic marker of an observed
event, the selection of attributes becomes important. The
robustness of gene selection methods affects the detection of
biologically meaningful genes in tumor diagnosis. For mapping, a
sequential feature long short-term memory (LSTM) network was
used with artificial immune recognition systems (AIRS) to
remember gene sequences and effectively recall learned sequential
patterns. An attempt was made to improve AIRS with LSTM,
which is a type of RNNs, to produce discriminative gene subsets
for finding biologically meaningful genes in tumor diagnosis. The
algorithms were evaluated using six common cancer microarray
datasets. By converging to the intrinsic information of the
microarray datasets, specific groups such as functions of the coregulated groups were observed. The results showed that the
LSTM-based AIRS model could successfully identify biologically
significant genes from the microarray datasets. Furthermore, the
predictive genes for biological sequences are important in gene
expression microarrays. This study confirmed that different genes
could be found in the same pathways. It was also found that the
gene subsets selected by the algorithms were involved in important
biological pathways. In this manuscript, we tried an LSTM
network on our learning problem. We suspected that recurrent
neural networks would be a good architecture for making
predictions. The results showed that the optimal gene subsets were
based on the suggested framework, so they should have rich
biomedical interpretability.