Abstract
A key step to apprehend the mechanisms of cells related to a particular disease is the disease gene identification. Computational forecast of disease genes are inexpensive and also easier compared to biological experiments. Here, an effectual deep learning-centered fusion algorithm called Naive Bayes-Artificial Neural Networks (NB-ANN) is proposed aimed at disease gene identification. Additionally, this paper proposes an effectual classifier, namely Levy Flight Krill herd (LFKH) based Adaptive Neuros-Fuzzy Inferences System (ANFIS), for the prediction of eye disease that are brought about by the human disease genes. Utilizing this technique, completely '10' disparate sorts of eye diseases are identified. The NB-ANN includes these ‘4’ steps: a) construction of ‘4’ Feature Vectors (FV), b) selection of negative data, c) training of FV utilizing NB, and d) ANN aimed at prediction. The LFKH-ANFIS undergoes Feature Extraction (FE), Feature Reduction (FR), along with classification for eye disease prediction. The experimental outcomes exhibit that method’s efficiency with regard to precision and recall.
Highlights
Disease genes are the dysfunction of a collection of genes, which in turn leads to Complex diseases [1] [2]
A novel sequence-based fusion method (NB-ANN) is proposed aimed at disease genes identification, and the LFKHANFIS is proposed aimed at identifying eye-related diseases that are triggered by those disease genes, say Age-associated Macular Degenerations (AMD), cataract, glaucoma, inherited optics neuropathies, Marfan syndrome polypoidal choroidals vasculopathies, retinitis pigmentosas, Stargardt disease, along with uveal melanoma
Performance Metrics Sensitivity Specificity Accuracy Precision Recall F-measure NPV FPR FNR MCC FRR
Summary
Disease genes are the dysfunction of a collection of genes, which in turn leads to Complex diseases [1] [2]. A great quantity of machine learning-centered computational methods was generated for predicting disease genes [8], say restricted Boltzmann machines [9], deep belief network [10], linear regressions model [11], support vectors machine [12], multilayer perceptions (MLP) [13], et cetera. These often attain greater prediction accuracy on larger data sets [14]. In this paper, an NBANN is proposed for identifying the disease genes as well as the LFKH-ANFIS is proposed for the identification of eyelinked diseases triggered by means of those recognized disease genes
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