Big Data, Mutant Models And Science 2.0
Modern biology has a problem - how to find meaning in the rising oceans of genomic data, such as the reams of cancer mutations that genome-wide studies are publishing every week. The challenge is finding efficient ways to parse the signals from the noise. There are efforts to fuse statistical mechanics and a learning algorithm into a mathematical toolkit that can turn cancer-mutation data into multidimensional models that show how specific mutations alter the social networks of proteins in cells. From this, biologists can deduce which mutations among the myriad mutations present in cancer cells might actually play a role in driving disease. Statistical mechanics describes large phenomena by predicting the macroscopic properties of microscopic components. read more