Monty Python’s Mr Gumby was anatomically on the money when he exclaimed “my brain hurts”. Our brains and bodies communicate via long axons that run from gray matter through our spinal cords. Neural communication is two way – when we cut a finger or knock a shin messages charge across a network of nerves to reach our brains, and it’s from here that feelings of physical hurt emanate. Communicating bodily damage is just one type of bi-directional traffic flowing between our brains and spinal cords; this network’s vital importance becomes devastating obvious when it fails.
Multiple sclerosis (MS) was first described by Jean-Martin Charcot in 1868. The great French neurologist observed that patients displaying damage to myelin sheaths coating axons of their brains and spinal cords suffer disrupted neural communication and reduced physical and cognitive capabilities. More than one hundred and forty years late we still cannot cure MS but medical researchers are unravelling the causes of a disease that kills more than a million people each year and distresses wider circles of family and friends.
At University at Buffalo in upper New York State a team of scientists investigates how genetics and environmental factors combine to create opportunities for MS to develop. In computational terms this is a nexus between big data and advanced analytics. The data sets are large because the genome of just one MS patient can give rise to hundreds of thousands of genetic variations, known as single nucleotide polymorphisms (SNPs). Current research indicates that the disease may be triggered by multiple SNPs interacting with one or more environmental factors - such as exposure to sunlight, levels of vitamin D3 in blood plasma, and smoking. Gene-environmental interaction analyses gives rise to a phenomenon called “combinatorial explosion” – a problem well-suited to investigation using algorithms of advanced analytics.
The research team at State University of New York (SUNY) have developed a technique they call AMBIENCE which uses algorithms to search efficiently for combinations of genetic and environmental factors that create risk of MS. Netezza running Revolution R Enterprise has reduced the time to compute AMBIENCE algorithms from 27 hours to fewer than 12 minutes. While reducing the wait time for their results increases productivity, equally liberating for the research team is the agility they gain from the R analytic language. Now they evolve their analytic model, adding and deleting variables simply by adding new lines of code rather than total rewrites demanded in other analytic tools.
A childhood friend of mine loved Mr. Gumby, occasionally going as far as rolling-up his trouser legs, donning a knotted handkerchief and proclaiming “My brain hurts”. If there was hurt, it didn’t show as Mick soared through school and up to Cambridge University, in to Bart’s Medical School and on to work in a hospital. Surgeons must be precise and dextrous, qualities destroyed by any failure between brain and body. Realizing he was no longer master of intricacies demanded at the operating table, Mick moved to other roles. But as he aged through his thirties MS stole more of him, and at what proved our final meeting Mick needed two shaking hands to raise a glass to his mouth and stumbled as he took three steps across the room.
Together Netezza and Revolution R free SUNY scientists from administrative burdens imposed on analytics by older computing architectures and languages; today they research more and research faster. Everyone who suffers MS or witnesses its ravages will thank SUNY and other research teams as they build on the work of Jean-Martin Charcot to shed light on what causes this disease.
For More Information:
- Press Release - Leading Global Multiple Sclerosis Research Center Taps IBM Analytics to Improve Patient Care
- Solution Brief - The IBM Healthcare Provider Data Warehouse
- White Paper - Leading Pharmaceutical Companies Speed Drug Discovery and Maximize Revenue Using Deep Analytics
- on the IBM Netezza data warehouse appliance
- from Mike Kearney