This year, the National Cancer Institute estimates that nearly 600,000 people in the U.S. will die from cancer. That’s a number Jian Ma, an associate professor in computation biology at Carnegie Mellon’s School of Computer Science, is working to reduce.

He and his team have worked to develop a computational model that analyzes cancer cells’ genomes using a new algorithm, called Weaver. The team attempts to create computational methods that provide a closer look at the basic functions of the human genome and human diseases’ molecular mechanisms.

“We were interested in studying how the cancer genome evolved from normal genomes,” Ma says. With Weaver, researchers will now be able to identify two different types of genetic changes associated with cancers and to identify connections between the two. This will improve their ability to characterize different cancers.

Weaver may prove to be a tool that provides researchers a detailed look at the specific genome changes that occur during the evolution of individual cancers, in turn leading to more personalized treatments.

“The method takes a step further in allowing us to characterize the genome more accurately,” Ma says. “It provides a unique tool that allows people to characterize what happened in the genome and to understand the functions that need to be investigated.”

Currently, it is used primarily for research purposes, Ma says. In the future, it may be used by clinicians and care delivery organizations to give people clear information and help them better understand the complexity of the cancer genome.

(This article appears courtesy of our sister publication, Health Data Management)

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