The availability of big data has enabled artificial intelligence (AI), machine learning (ML), and deep learning (DL) to transform many industries and scientific fields. Hitherto, these tools may even revolutionize biomedical research. They are being used to address the challenges of cancer biomarker discovery, where the analysis of large amounts of imaging and molecular data is beyond the capabilities of traditional statistical analysis and tools. In a special issue of cancer biomarkers, researchers present various approaches and explore some of the unique challenges (e.g., the nonlinear relationship between markers and disease, or the noise portion of markers interfering with the diagnosis). AI-based on nonlinear mapping can mechanically find the correlation between markers and disease through multiple iterations, which can improve the accuracy and predictive power of biomarkers for cancer and other diseases. Advances in the application of AI to cancer research in molecular bioscience are the development direction of the discipline in this field and should be conducted a comprehensive investigation.

In recent years, the field of machine learning has been developing rapidly in promising applications of cancer research, including identifying early cancers, inferring the site of specific cancers, helping to assign appropriate treatment regimens to each patient, characterizing the tumor microenvironment, and predicting response to immunotherapy. That’s what we are interested in, by dividing the above topics into categories, the research topic focuses on the application of AI to cancer research in a screening of cancer causative factors and accurate cancer classification, prediction of RNA interaction and disease-causing genes identification, prediction of metabolite-protein interaction and functional biomaterial screening.

This Research Topic welcomes Reviews, Mini Reviews, Research Articles, Methods, and Perspectives, focusing but not limited to the following subtopics:

Artificial intelligence in accurate cancer classification.

Artificial intelligence in functional bio-material screening.

Artificial intelligence in the screening of cancer causative factors.

Artificial intelligence in disease-causing genes identification.

Artificial intelligence in the prediction of RNA interactions.

Artificial intelligence in metabolite-protein interaction prediction.

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Frontiers in Molecular Biosciences

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