Abstract
This article proposed a new framework to predict novel disease-associated genes. First, we have compiled a gene-disease network from an existing gene-disease association database. Next, we associated gene ontology and protein interaction networks with the compiled gene-disease network. The prediction is based on the three statistical hypothesis, we have deduced from the topological structure of the compiled network. The first two hypothesis represents the association between the functional similar genes with the disease classes. The third hypothesis infers the association between the genes with disease class. The prediction is made based on the conclusions of these three hypotheses. Statistical tests are conducted to prove the three hypothesis. The results show 400 high-confidence gene-disease associations. The predictions are validated using a literature study and statistical test. The predictions are demonstrated by using several visualization techniques.
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Alberuni, S., Ray, S. (2024). Predicting Disease-Associated Genes Through Interaction and Ontology-Based Inference Technique. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1956. Springer, Cham. https://doi.org/10.1007/978-3-031-48879-5_20
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