Autoscribe to Highlight Matrix Gemini LIMS at Lab Indonesia 2014
News Mar 30, 2014
Autoscribe will be demonstrating the flexibility and scalability offered by its flagship Matrix Gemini LIMS (Laboratory Information Management System) at the Lab Indonesia 2014 exhibition in Jakarta from 7-9 May 2014. Autoscribe will be exhibiting on the booth of its local distributor, PT Agriya Analitika.
Matrix Gemini has applications in a wide range of laboratories including, food, pharmaceutical, chemicals, oil, petrochemicals, veterinary, healthcare, materials, metals, hospitals and environmental information management systems.
Matrix Gemini is characterized by a powerful suite of configuration tools and a desktop/web-browser user interface. This provides quick and easy set up of the system according to the specific terminology of the laboratory in terms of workflows, screen designs, menu designs, terminology, report designs and much more without the need for custom coding or scripting, to give comprehensive tracking and monitoring of samples from booking-in through testing to result reporting and sample disposal. It also provides the flexibility to accommodate any future changes in requirements within the laboratory.
Algorithm Speeds Up Medical Image Analysis 1000 TimesNews
Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. Researchers have described a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.
Antarctic Worm and Machine Learning Help Identify Cerebral Palsy EarlierNews
A research team has released a study in the peer-reviewed journal BMC Bioinformatics showing that DNA methylation patterns in circulating blood cells can be used to help identify spastic cerebral palsy (CP) patients. The technique which makes use of machine learning, data science and even analysis of Antarctic worms, raises hopes for earlier targeted CP therapies.
Towards Personalized Medicine: One Type of Data is Not EnoughNews
To understand the biology of diseased organs researchers use different types of molecular data. One of the biggest computational challenges at the moment is integrating these multiple data types. A new computational method jointly analyses different types of molecular data and disentangles the sources of disease variability to guide personalized treatment.READ MORE