FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks

For example, in recent years, applications and the widespread adoption genomics sequencing algorithms have become more accessible of genomics sequencing, an increasing and affordable, making them widely used in clinical amount of AI-based algorithms and tools is entering settings (Yang et al., 2020). The importance of genomics sequencing the research and production stage affecting algorithms in disease diagnosis lies in their ability critical decision-making streams like drug discovery to identify genetic mutations that may cause or contribute and clinical outcomes. This paper demonstrates to the development of certain diseases. This information the vulnerability of AI models often utilized can be used to develop personalized treatment plans and downstream tasks on recognized public genomics potentially prevent the onset of certain diseases in at-risk datasets. We undermine model robustness individuals. Overall, genomics sequencing algorithms have by deploying an attack that focuses on input become an essential tool in modern medicine, and their usage transformation while mimicking the real data and is expected to continue to increase in the coming years confusing the model decision-making, ultimately as the field of genomic medicine continues to advance (Jacob yielding a pronounced deterioration in model performance.