Machine learning (ML), a field stemming from artificial intelligence, is part of a wider approach for data analysis termed data mining (DM).

Machine learning (ML) has the potential to transform oncology and, more broadly, medicine. 1 The introduction of ML in health care has been enabled by the digitization of patient data, including the ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

Machine learning is a tremendous resource for researchers. Yet not understanding the limitations of data and algorithms can lead to erroneous conclusions.

The use of real-world data (RWD) in oncology is becoming increasingly important for clinical decision making and tailoring treatment. Despite the significant success of targeted therapy and ...

Researchers review the application of machine learning in improving cancer diagnosis, treatment, and prognosis.

Data integration is a prerequisite for building analytics and AI applications. In healthcare, it seems that NLP is quite important, too.

The goal of implementing artificial intelligence and machine learning in clinical research is not to replace humans with digital tools but to increase their productivity.

Long-read sequencing technologies analyze long, continuous stretches of DNA. These methods have the potential to improve researchers' ability to detect complex genetic alterations in cancer ...