AI News, Machine Learning & Artificial Intelligence

IBM uses machine learning to detect early Alzheimer’s in blood samples

Long before memory loss occurs in patients with Alzheimer’s, the protein amyloid-beta—which has been implicated in the formation of brain tangles that characterize the disease—starts building up in the spinal fluid.

RELATED: Fighting ALS, dementia and more by trapping toxic brain proteins The IBM researchers were able to obtain information on nearly 400 proteins that were measured in blood samples from the ADNI cohort, but they wanted to develop a simple test that could use as few proteins as possible to reflect the buildup of amyloid-beta.

“While all models making use of blood analytes outperformed the base model of age and APOEε4, models that made use of the protein level measurements consistently achieved the strongest predictive performance,” the IBM researchers wrote in the study.

So pharma companies developing drugs to slow or halt the progression of the disease could use a low-cost, minimally invasive way to find clinical trial participants who face a high risk of Alzheimer’s but are many years removed from developing symptoms of the disease, they said.

The high failure rate of these and other Alzheimer’s trials “may be because the people enrolled are in the latest stages of the disease, and have likely already suffered a level of brain tissue loss that cannot easily be repaired,” suggested Ben Gaudey, a staff researcher on the genomics research team at IBM, in a blog post.

MachineLearning

It seems that, as of current, data programming works well for text but less so for images/audio.

Using machine learning to improve subseasonal climate forecasting

By using machine learning techniques like the one we created for this contest, [the new model] is able to help energy companies and cities prepare for severe storms much farther in advance,” says Cohen.  The dynamic team of experts combined historical weather-pattern recognition and machine learning in order to produce real-time predictions of temperature and precipitation anomalies two to six weeks in advance for the western United States.   “We capitalized on the current availability of ample meteorological records and high-performance computing techniques to blend both physics-based or dynamic models and statistical machine learning approaches in order to extend the skillful forecast horizon from days to weeks,” says Cohen.  The combination of machine learning techniques and historical weather-pattern recognition is very powerful because it can help the government maximize water resources and prepare for natural disasters or extreme weather conditions.  “There are certainly plans to continue this project, as we have been talking about extending the model to the entire U.S. We demonstrated with this contest that there is potential with this model to leapfrog the forecasting process.

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