Abstract
The early and accurate assessment of oncogenic risk is a critical challenge in computational biology and personalized medicine. We present the methodology behind SARAHAI-GPK (Genome / Variant PoL-KDE Platform) v388, a patented hybrid artificial intelligence system designed to predict oncogenic potential from FASTA and VCF genomic data files. The system integrates unsupervised and supervised learning techniques to create a multi-faceted risk profile. The core of the unsupervised analysis is a Pattern-of-Life (PoL) model, which uses Kernel Density Estimation (KDE) to establish a baseline of a healthy genome and detect anomalous deviations in sequence-derived features, yielding an Oncogenic Risk of Instability (ORI) score. This is complemented by a novel Tensor Interaction Score that models the synergistic risk of co-mutations within critical cancer pathways. For supervised analysis, a deep neural network processes a combined feature space from both FASTA and VCF data to learn specific known risk patterns. These disparate scores are integrated through a hierarchical, weighted-averaging model to produce a final Hybrid Aggregated Oncogenic Risk (HAOR) score. This paper details the distinct data processing pipelines, feature extraction techniques, and the mathematical and architectural basis of each analytical engine within the SARAHAI-GPK framework.