Abstract
Leishmaniasis is an endemic parasitic disease, predominantly found in the poor locality of Africa, Asia, and Latin America. It is associated with malnutrition, the weak immune system of people, and their housing locality. It is diagnosed by microscopic identification, molecular and biochemical characterization, or serum analysis for parasitic compounds. In this study, we present a new approach for diagnosing leishmaniasis using transcriptome data and cognitive computing. The transcriptome data of leishmaniasis were collected from the Gene Expression Omnibus database, and it was processed. The algorithm for training and developing a model based on the data was prepared and coded using python. The algorithm and their corresponding datasets were integrated using the TensorFlow data frame. A feedforward artificial neural network trained model with multilayer perceptron was developed as a diagnosing model for leishmaniasis, using transcriptome data. It was created using a recurrent neural network. The cognitive model of the trained network was interpreted using the maps and mathematical formula of the influencing parameters. The credit of the system was measured using the accuracy, loss, and error of the system. This integrated leishmaniasis transcriptome data and neural network system acted as an excellent diagnosis model with higher accuracy and lower error. The experimental results of feedforward multilayer perceptron model after normalization, mean square error (219.84), loss function (1.94), and accuracy (85.71%) of the model show the excellent fit of the model with the process, and it could be a better solution for diagnosing leishmaniasis in future, using transcriptome data.
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Sundaramahalingam, M.A., Kabra, R., Singh, S. (2022). Construction of Feedforward Multilayer Perceptron Model for Diagnosing Leishmaniasis Using Transcriptome Datasets and Cognitive Computing. In: Singh, S. (eds) Machine Learning and Systems Biology in Genomics and Health. Springer, Singapore. https://doi.org/10.1007/978-981-16-5993-5_1
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