Enterprise IT vendor IBM said KwaZulu-Natal Research Institute for Tuberculosis and HIV (K-RITH) will use Big Data analytics technologies to find new treatments and diagnostic approaches to fight tuberculosis (TB) in South Africa.
IBM’s Big Data analytics technologies will be used on bacterial genetics and drug susceptibility tests to better understand the genomic mechanisms that cause resistance to antibiotics.
Over 100,000 cases of TB are reported every year from KwaZulu-Natal province alone and over 60 percent are also infected with HIV.
The work with IBM involving its Big Data and deep data analytics technology — will enable K-RITH to understand bacteria genomes from drug-resistant strains of M. tuberculosis — the bacteria that causes TB.
Its science has benefitted from IBM’s global network of research labs and world leading expertise in computational biology. Researchers from the Haifa, Melbourne, and Africa labs are working together to analyze over 200 TB genomes, each with 4.4 million base pairs, to better understand the complex clinical picture of African tuberculosis infections.
Michal Rosen-Zvi, senior manager of analytics at IBM’s Research Lab in Haifa, Israel said: “The bioinformatics or computational tools needed to extract information on the disease are very new, yet cracking the code of this genomic information will help define which treatment combinations work best for different patients and how they work on different strains of TB.”
IBM’s work on other solutions, including EuResist program, developed to help physicians select the optimal treatments for HIV patients, have paved the way for the use of bioinformatics in disease treatment.
EuResist combines large databases and new prediction engines to provide predictive modeling of how HIV would react in a particular person treated with specific combination of drugs. This system, available since 2008, is the world’s largest database containing clinical and genomic information on HIV.
Last week, IBM said the New York Genome Center will test a IBM Watson prototype designed specifically for genomic research as a tool to help oncologists deliver more personalized care to cancer patients.
