Allergies and asthma are increasing in number. According to the CDC, food allergies alone rose 50 percent in children from 1997 to 2011.

Wouldn’t it be wonderful if people could predict the presence of allergens we could be more preemptive about treating them and companies could be better about not introducing them into their products?

Predicting allergens to create better products

Chris Lawrence and Ha Dang, researchers from the Virginia Bioinformatics Institute and the Department of Biological Sciences in the College of Science at Virginia Tech, thought so. They created Allerdictor, a new computational approach and software that helps predict allergies.

“As more biotechnology derived products like new protein-based therapeutics or genetically modified plants are developed, it becomes increasingly important for potential allergens to be identified and dealt with before these come to market,” explained Lawrence, the project director. “Predicting allergens more accurately will also aid in basic scientific research.”

Businesses and consumers are protected

The new Allerdictor system predicts allergens with a very high accuracy at very fast speeds. Allerdictor uses machine-learning approaches borrowed from artificial intelligence research to learn and apply those lessons to the analysis of large-scale submissions for the presence of allergens. It is now easier, faster, and more accurate making it better for eliminating allergens and making sure businesses and consumers are protected.

Analyze whole genomic sequences faster and better

“This new approach for identifying potential allergens is also applicable when analyzing the vast amount of genomic data that is being produced from various DNA sequencing projects worldwide. Most current methods of allergen prediction tend to be slow and inaccurate, yielding false positives that skew the data. Allerdictor literally take minutes to analyze entire genomes,” said Dang, first author.

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