Introduction to the special issue on Applications of Artificial Intelligence in Biomarker Research.

Recent advances in high throughput analytical technologies and artificial intelligence (AI) are changing the paradigm of biomarker discovery. This ability to analyze large numbers of clinical samples is providing unprecedented amounts of imaging and molecularbased biomarker data ranging from the scale of GBs for genome, transcriptome, metabolome and proteome data to TBs for imaging data [1] beyond the ability of traditional statistical analyses and tools. The availability of big data has resulted in the development of new computational tools, including new machine (ML) and deep learning (DL) algorithms to provide levels of accuracy expected of meaningful clinical interpretations. For example, the exciting results by AlphaFold in the recent Critical Assessment of Protein Structure Prediction (CASP) was driven by a deep neural network trained on over 170,000 protein structures allowing the team to accurately model 24 of the 43 domains with the next method far behind at 14 of the 43 domains [2,3]. However, as noted by the authors of AlphaFold, deep neural networks are complex and understanding how the network reaches the residue-based distance predictions is not clear. This is one of the most challenging areas for AI in application to biomedical problems, building highly accurate models that are both explainable and trustworthy. For biomarker discovery recent advances in integrating multi-omics data with advanced feature selection and ranking are leading to better understanding of mechanisms driving disease [4,5]. Further, integrating constraints into the models based on biological domain knowledge in a principled manner can improve both the accuracy and interpretation of models being applied to predict health outcomes, stratify patients for treatment, understand the underlying molecular mechanisms of disease, and many other tasks needed to realize the goals of personalized medicine [6]. This special issue of Cancer Biomarkers is devoted to the application of artificial intelligence and machine learning approaches to the unique challenges of biomarker discovery. The first article in the series, Mikdadi et al, presents a comprehensive review of the literature regarding the use of AI approaches to identify biomarkers for ovarian and pancreatic cancer. Although the examples chosen focus on ovarian and pancreatic cancer, the underlying principles are universal, and the analysis of gaps and challenges applies to the field as a whole. Two of the articles in this special issue focus on the development of new computational tools to facilitate the application of AI to biomarker identification. Yoon et al. describes the development of an approach to develop specialized vocabularies for AI and DL studies, without jeopardizing the subjects’ personally identifiable information. This issue is of increasing concern, as the resolution of AI approaches to feature selection continues to improve. Tayob et al. is focused on the development of novel Bayesian algorithms to leverage longitudinal measurements of the same features over time in a given patient to facilitate the early detection of cancer, using hepatocellular carcinoma as the model system. This is