Researchers have developed an automated method using machine learning to predict the effectiveness of viral diagnostic tests and designs optimized ones. This interesting news came to us from Phys.org in their article, “Machine learning could help scientists design better viral diagnostics.”
ADAPT uses trained algorithms to predict the best sequences for a diagnostic, promises to help scientists rapidly design tests that are more effective for a large number of different viruses and can be quickly modified and scaled as viruses evolve.
Machine learning is becoming a commodity. Numerous machine learning frameworks and services are available to data holders who are not experts but want to train predictive models on their data.
Interestingly, most organizations have little knowledge on how artificial intelligence (AI) systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
