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

Abstract

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.

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