This approach improves the detection of the small fraction of cancer DNA, making the test, named TriOx, particularly sensitive for identifying cancer. 'Our new test brings together the best of cutting ...

The detection of LCC was achieved using an ensemble of three classifiers: competitive neural networks (CNNs), extreme learning machine (ELM), and long short-term memory (LSTM). This ensemble ...

In a proof-of-concept study, the team used machine learning to analyze blood plasma from more than 2,000 participants and link molecular patterns to lung cancer, extrapolating a potential ...

Machine learning–based classification of cancer types using genomic profiling data from the Australian Molecular Screening and Therapeutics (MoST) program. Authors: Frank Po-Yen Lin, Min Li Huang, ...

Recently, a machine learning–based model using step count measured with a wearable fitness tracker was found to predict hospitalization in patients receiving chemoradiation. 21 However, it is unclear ...

Journal reference: Orcutt, X., Chen, K., Mamtani, R., et al. (2025). Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations.

Craif, spun off from Nagoya University in Japan in 2018, is using microRNA (miRNA) to develop an AI-powered early cancer detection software, and it has raised $22 million in Series C funding to ...

With advances in machine learning, researchers are using neural networks to predict cancer risks and guide personalized treatments. By processing immense datasets, AI helps identify factors ...