The rapid growth and accessibility of artificial intelligence (AI) and machine learning (ML) have opened many avenues to revolutionize biomedical research, particularly in oncogenesis. Oncogenesis is a hallmark process in the development of cancer, involving the amplification of proto-oncogenes and the subsequent dysregulation of molecular signaling networks. These pathways—including the RAS/RAF/MEK/ERK, PI3K-AKT, JAK-STAT, TGF-β/Smad, Wnt/β-Catenin, and Notch cascades—have been studied extensively in isolation, with major strides achieved in understanding how they drive cancer. However, there are still many considerations regarding how these networks interact. Ongoing studies show that crosstalk among these pathways occurs through feedback loops, shared intermediates, and compensatory activation, creating a complex network that enables tumor cells to adapt and metastasize. New developments in AI and ML have enabled modeling and prediction of these interactions for pathway discovery, mapping oncogenic crosstalk, predicting drug resistance and therapeutic responses, and complex data analysis. Novel technologies such as feature selection algorithms and convolutional neural networks have demonstrated immense translational potential to bridge computational predictions in cancer genomics with clinical applications. Similar models have also proven useful for learning from genomic datasets and reducing multidimensionality in heterogeneous multiomics data. As current AI/ML approaches continue to develop, it is also important to consider the limitations of batch effects, model generalizability, and potential bias in training datasets. This review aims to integrate the most recent AI and ML applications in uncovering the hidden interactions within oncogenic networks that drive tumorigenesis, heterogeneity, and resistance to therapies. Moreover, this review aims to synthesize the functionality of emerging computational methods that elucidate these insights, as well as the transformative implications of AI-guided systems biology on precision oncology and combinatorial therapies.
Sutanto et al. (Thu,) studied this question.