© 2000 - 2014 Virginia Bioinformatics Institute
Thursday, 17 April 2014
Machines learn to predict allergens more accurately and may be the first step in eliminating them
Marketing and Communications - Press Releases
published by   
April 15, 2014

BLACKSBURG, Va., April 15, 2014 -- It’s well known that allergies and asthma are on the rise around the globe. Food allergies alone rose 50 percent in children from 1997 to 2011, according to a recent study at the Centers for Disease Control and Prevention.

 

Christopher Lawrence and Ha Dang are in the recent Bioinformatics journal for Allerdictor software.

That’s part of why Chris Lawrence and Ha Dang, researchers from the Virginia Bioinformatics Institute and the department of biological sciences at Virginia Tech, created Allerdictor, a new computational approach and software that helps predicts allergens.  Moreover, a scientific publication describing this method was recently published in the journal Bioinformatics.

Lawrence, director of the project, stated, “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. Predicting allergens more accurately will also aid in basic scientific research."

The institute scientists report that Allerdictor predicts allergens with very high accuracy at fast speeds. Instead of using standard approaches for identifying allergens, Allerdictor uses machine-learning approaches — methods borrowed from artificial intelligence research — to learn and apply that learning to analyze large-scale submissions for the presence of allergens. This approach makes it easier and faster to scan large sequences of data, eliminating allergens and making sure businesses and consumers are protected.

Ha Dang, lead author of the paper and the developer of Allerdictor went on to state, “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 takes minutes to analyze entire genomes.”

Allerdictor is available free online for educational and non-profit public use.

A university-level Research Institute of Virginia Tech, the Virginia Bioinformatics Institute was established in 2000 with an emphasis on informatics of complex interacting systems scaling the microbiome to the entire globe. It helps solve challenges posed to human health, security, and sustainability. Headquartered at the Blacksburg campus, the institute occupies 154,600 square feet in research facilities, including state-of-the-art core laboratory and high-performance computing facilities, as well as research offices in the Virginia Tech Research Center in Arlington, Va.

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Tiffany L Trent
540-231-6822
ttrent@vt.edu