GenX: AI-Powered Genomic Hybridization Analysis and Prediction

GenX is a computational framework designed to simulate and analyze genomic hybridization between different species by creating hybrid DNA sequence using an advanced window-based merger method that retains essential genetic elements such as start codes with configurable prices. The system uses a comprehensive functional extraction pipeline to analyze genomic properties including GC content, sequence length, CG dinucleotide frequency, sequence complexity, and repeat density, while utilizing a random forest classifier trained on extensive simulated datasets (N = 1000) to predict hybrid viability with strong accuracy. Framework includes an interactive visualization suit that provides detailed representation of genomic disruption, inheritance patterns, crossover points, mutation events and confidence intervals, as well as a genetic contribution to the original species of extensive heritage analysis. GenX acts as a valuable tool for genetic engineering science, protective genetics and evolutionary biology research by offering a computational approach to studying the potential consequences of genomic hybridization events before experimental verification, making researchers effective to identify the hybridization landscapes.