Prerna Saurabh
Amar Kumar Verma
Saurabh Rai
Sourabh Singla
Abstract
Gene-Gene Interactions (GGI) Networks-Genomics essentially finds the driver node to understand the functional mechanism of Gene-Gene Interaction or GGI. This process can significantly improvise while examining molecular processes perturbed by genetics in human diseases. With Artificial Intelligence (AI) developments, pattern recognition and machine learning advancements can be exploited to automate from more superficial to handle any task in the medical field. In the past, deep learning-based methods have provided encouraging results in the medical field, such as breast cancer detection, skin cancer classification, brain disease classification, arrhythmia detection, pneumonia detection from X-Ray images, and lung segmentation. Interestingly, employing machine learning in the medical field has caught my great attention and motivated us to utilize machine learning-based algorithms to detect drive nodes. Several recent types of research have focused on identifying the bare minimum of driver nodes required to control underlying gene-gene interaction networks. This study is about analyzing gene interaction networks statistically. One or more abnormalities cause a genetic condition in the DNA. Disease-related genes play essential biological roles in the cell. Multiple genes often work together to cause complex genetic illnesses. So, the central concept is to create a system that can assess networks in various elements that influence them.
Keywords
Gene-Gene interaction, Driver nodes, Statistical analysis, DNA, Genetic, k-nearest neighbors (KNN)
