Research on Adoption of Gene-Disease Identification and Recognition Phenotype Process in High-Performance Computing and Applications (HPCA) Research

Abstract

The research contribution of High-Performance Computing and Applications (HPCA), combined with biomedical text related to generic and phenotypes, has been published at various levels to optimize user demand. However, there is still a huge challenge in eliminating ambiguities that occur in biomedical text mining due to the use of the same expression in the investigation of the genes disease. This article uses two combined approaches that represent identification and detection in high-performance computing and Applications (HPCA) system for the evaluation of gene-disease and phenotype processes in biomedicine. The Current approaches use machine learning as a positive training phrase P and a gene as the negative training set N for identification, but these approaches are only of the low-noise negative set which is the performance of the quality of the HPCA System affects, our first contribution paper proposes a new implementation based on positive unlabeled Disease gene Identification (PUDI) to construct our classifier. The second contribution we introduce high precision and recall values which achieve term classification accuracy between 0.96 and 0.99, precision values between 0.95 and 0.99, and recall values between 0.96 and 0.99. Definitively, the F-score value obtained confirmed outperformed results on the classifiers with high accuracy over a wide range of parameters.