AstraZeneca system implemented with input from Tessella wins 2014 Bio-IT World Best Practices Award
News May 10, 2014
The Bio IT World Best Practice Award 2014 judges selected Tessella and AstraZeneca as award winners in the category for Clinical & Health IT for AstraZeneca's Real Time Analytics for Clinical Trials (REACT), receiving a honorable mention from the judges at the award ceremony.
The award was given for AstraZeneca's REACT system, implemented with input from Tessella. The system tracks vital statistics, laboratory tests and adverse events on both population and subject-specific levels during the course of a clinical trial. By collecting this information together in a platform with flexible visualization and query features, as well as a complete profile for each patient, REACT allows AstraZeneca to respond rapidly to safety concerns and swiftly identify risk factors for adverse events, protecting trial participants while rescuing trials from costly late-stage failure.
Analytical Tool Predicts Disease-Causing GenesNews
Predicting genes that can cause disease due to the production of truncated or altered proteins that take on a new or different function, rather than those that lose their function, is now possible thanks to an international team of researchers that has developed a new analytical tool to effectively and efficiently predict such candidate genes.
Researchers Move Closer to Completely Optical Artificial Neural NetworkNews
Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition.
Big Data Study Targets Genomic Dark Matter from Ocean Floor to Gut FloraNews
An international team led by computational biologist Fran Supek at IRB Barcelona develop a machine learning method to predict unknown gene functions of microbes.The system examines and compares ‘big data’ available on the metagenomes of human and environmental microbiomes.READ MORE