AI-Driven Precision Oncology Prediction: Deep Learning–Driven Early Detection of Carcinogenesis Using Multi-Omics Data Integration

Abstract

One of the most essential issues concerning oncology medicine is the possibility to detect the carcinogenesis early enough. This paper demonstrates a new deep learning system, called Deep Learning Multi-Omics Integration (DL-MOI), to AI-assisted precision oncology by early carcinogenesis detection of detected multi-omics data. The suggested architecture integrates Convolutional Neural Networks (CNNs) with the long Short-term memory (LSTM) networks and Transformer encoders into the framework of attention-based fusion in order to concomitantly accommodate genomic, transcriptomic, proteomic and epigenomic forms of data. With a mean area under the receiver operating characteristic curve (AUC) of 0.976, an overall accuracy of 96.1, sensitivity of 95.7 and specificity of 96.8, TCGA (n = 11,432 samples, 7 cancer types) on the TCGA produced a multi-caner prediction model (DL-MOI). TP53 mutation frequency, BRCA1/ 2 methylation status, and MYC copy-number variation were the three dominant biomarkers identified through SHAP-based interpretability analysis. The results of the comparative benchmarking results over five of the state-of-the-art techniques showed the same degree of improvement in all evaluation measures. The DL-MOI framework constitutes a clinical intervention and biologically comprehensible solution to multi-cancer early diagnosis with a potential of individual therapeutic intervention.