Abstract
The results of research regarding the development of a gene expression data classification system based on a convolutional neural network are presented. Gene expression data of patients who were studied for lung cancer were used as experimental data. 156 patients were studied, of which 65 were identified as healthy and 91 patients were diagnosed with cancer. Each of the DNA microchips contained 54,675 genes. Data processing was carried out in two stages. In the first stage, 10,000 of the most informative genes in terms of statistical criteria and Shannon entropy were allocated. In the second stage, the classification of objects containing as attributes the expression of the allocated genes was performed by using a convolutional neural network. The obtained diagrams of the data classification accuracy during both the neural network model learning and validation indicate the absence of the network retraining since the character of changing the accuracy and loss values on trained and validated subsets during the network learning procedure implementation is the same within the allowed error. The analysis of the obtained results has shown, that the accuracy of the object’s classification on the test data subset reached 97%. Only one object of 39 was identified incorrectly. This fact indicates the high efficiency of the proposed model.
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Yasinska-Damri, L., Babichev, S., Durnyak, B., Goncharenko, T. (2023). Application of Convolutional Neural Network for Gene Expression Data Classification. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_1
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