TY  - JOUR
T1  - Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification
AU  - Batur Şahin, Canan
AU  - Diri, Banu
PY  - 2021
DA  - January
DO  - 10.17694/bajece.604885
JF  - Balkan Journal of Electrical and Computer Engineering
PB  - MUSA YILMAZ
WT  - DergiPark
SN  - 2147-284X
SP  - 23
EP  - 32
VL  - 9
IS  - 1
LA  - en
AB  - In the field of biomedicine, applications ofthe identification of biomarkers require a robust gene selection mechanism. Foridentifying the characteristic marker of an observed event, the selection ofattributes becomes important. The robustness of gene selection methods affectsdetecting biologically meaningful genes in tumor diagnosis. For mapping,sequential features Long-short-term memory (LSTM) was used with ArtificialImmune Recognition Systems (AIRS) to remember gene sequences and effectivelyrecall of learned sequential patterns. An attempt was made to improve AIRS withLSTM which is a type of RNNs to produce discriminative gene subsets for findingbiologically meaningful genes in tumor diagnosis. The algorithms were evaluatedusing six common cancer microarray datasets. By converging to the intrinsicinformation of the microarray datasets, specific groups like functions of theco-regulated groups were observed. The results showed that the LSTM based AIRSmodel can successfully identify biologically significant genes from themicroarray datasets. Also the predictive genes for biological sequencesareimportant in gene expression microarrays.This study confirms that differentgenes can be found in the same pathways. It was also found that the genesubsets selected by the algorithms were involved in important biologicalpathways.In this work we try an LSTM on our learning problem. We suspected thatrecurrent neural networks would be a good architecture for makingpredictions.The results show that the optimal gene subsets were based on thesuggested framework, so they should have rich biomedical interpretability.
KW  - Artificial immune system
KW  - Biomarker discovery
KW  - Robustness
KW  - Tumor Classification
KW  - Optimization
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UR  - https://doi.org/10.17694/bajece.604885
L1  - https://dergipark.org.tr/en/download/article-file/783938
ER  -