The Digital Laboratory - Machine learning models can help diagnose ALS earlier from a blood sample
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Event guide: 3rd IMPACCT Real World Evidence Summit EuropeEvent guide: 3rd IMPACCT Real World Evidence Summit Europe

Real-world data (RWD) has come a long way. Today, it’s all about turning that data into real-world evidence (RWE) that shapes regulatory decisions, payer discussions, and clinical practice.

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   Machine learning models can help diagnose ALS earlier from a blood sampleMachine learning models can help diagnose ALS earlier from a blood sample
 
Using machine learning models, researchers at Michigan Medicine have identified a potential way to diagnose amyotrophic lateral sclerosis, or ALS, earlier from a blood sample, a study suggests.
 
   Small molecule discovery could open the door to new class of treatments for hard-to-treat cancersSmall molecule discovery could open the door to new class of treatments for hard-to-treat cancers
 
Researchers at the UCLA Health Jonsson Comprehensive Cancer Center have identified a small molecule that can inhibit a cancer-driving protein long considered impossible to target with drugs - a discovery that could open the door to a new class of treatments for leukemia and other hard-to-treat cancers.
 
 Lab Thread announces beta release of unified lab software platform
 
Lab Thread announces beta release of unified lab software platformLab Thread Ltd, a UK-based life science software company, today announced the beta launch of its flagship unified lab software platform.
 
 
 Delivering Health Equity in a Warming World
 
Delivering Health Equity in a Warming WorldThe intersection of health equity and vaccines is critical as climate change threatens access. Universal immunization is essential for a resilient future.
 
 
 Machine learning reveals dental caries heterogeneity in NHANES
 
Machine learning reveals dental caries heterogeneity in NHANESA new article published in the Journal of Dental Research explores the development an integrated data-cleaning and subtype discovery pipeline using unsupervised machine learning for comprehensive analysis and visualization of data patterns in the National Health and Nutrition Examination Survey (NHANES) database.
 
 

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