Machine Learning for Genomic Expression Classification-Based Phenotype Prediction in Topological Data Analysis

Genomic data has become more prevalent due to sequencing and Machine Learning (ML) innovations, which have increased the biological genomics study. The multidimensional nature of this data provides challenges to phenotype prediction, which is required for individualized health care and the research investigation of genetic problems; nevertheless, it holds tremendous potential for understanding the association between genes and physical features. The authors of this paper introduce a new technique for symptom prediction from data from genomes, which combines Topological Data Analysis (TDA), Graph Convolutional Networks (GCN), and Support Vector Machines (SVM). The proposed method aims to address these challenges. By using TDA for multifaceted feature extraction, GCN to analyze gene interaction networks, and SVM for reliable classification in high-dimensional spaces, the above technique overcomes the drawbacks of conventional approaches. This TDA-GCN-SVM model has been demonstrated to be implemented in a method that is superior to conventional methods on distinct tumor datasets in terms of accuracy and additional measures. A novel method for genomic study and a more significant comprehension of genomic data analysis are both caused by this innovation, which is an enormous achievement in precision healthcare.