Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification

Year 2021, Volume: 9 Issue: 1, 23 - 32, 30.01.2021

Canan Batur Şahin , Banu Diri

https://doi.org/10.17694/bajece.604885

Cited By: 1

Abstract

In the field of biomedicine, applications of
the identification of biomarkers require a robust gene selection mechanism. For
identifying the characteristic marker of an observed event, the selection of
attributes becomes important. The robustness of gene selection methods affects
detecting biologically meaningful genes in tumor diagnosis. For mapping,
sequential features Long-short-term memory (LSTM) was used with Artificial
Immune Recognition Systems (AIRS) to remember gene sequences and effectively
recall of 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 like functions of the
co-regulated groups were observed. The results showed that the LSTM based AIRS
model can successfully identify biologically significant genes from the
microarray datasets. Also the predictive genes for biological sequences
areimportant in gene expression microarrays.This study confirms that different
genes can 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 work we try an LSTM on our learning problem. We suspected that
recurrent neural networks would be a good architecture for making
predictions.The results show that the optimal gene subsets were based on the
suggested framework, so they should have rich biomedical interpretability.

Keywords

Artificial immune system, Biomarker discovery, Robustness, Tumor Classification, Optimization

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