Identification of disease genes and assessment of eye-related diseases caused by disease genes using JMFC and GDLNN

Abstract

Early detection of disease genes helps humans to recover from certain gene-related diseases, like genetic eye diseases. This work identifies the possibility of eye diseasesfor the disease genes utilizing a Gaussian-activation function (G)-centric deeplearning neural network (GDLNN) model. In this work, human genes are selected by computing structural similarity and genes are clustered as disease genesand normal genes by using the JMFC clustering algorithm. Levy flight and Crossover and Mutation (LCM) centric Chicken Swarm Optimization (LCM-CSO) is employed for feature selection and GDLNN classifies the eye-related diseases for the input genes using the selected features.

Keywords:

• Disease gene identification
• eye disease identification
• deep learning neural network (DLNN)
• chicken swarm optimization (CSO)
• fuzzy C-means clustering; Jacobian matrix

Disclosure statement

No potential conflict of interest was reported by the authors.