That is what Hanover has done,” said Auro Nair, executive vice president of JAX. “What you want is some degree of intelligence incorporated into the system that can go out and not just be efficient, but also be effective and relevant in terms of how it can filter information. The bottleneck is filtering through the more than 4,000 papers published every day in biomedical journals to find the subset of about 200 related to cancer, read them and update CKB with the relevant information on the mutation, drug and patient response. This information is also useful to translational and clinical researchers, Mockus noted. One application of this knowledgebase allows oncologists to discover what, if any, matches exist between a patient’s known cancer-related genomic mutations and drugs that target them as they explore and weigh options for treatment, including enrollment in clinical trials for drugs in development. Mockus and her colleagues are using Microsoft’s machine reading technology to curate CKB, which stores structured information about genomic mutations that drive cancer, drugs that target cancer genes and the response of patients to those drugs. Peter Lee, corporate vice president of Microsoft Healthcare. “For something that really matters like cancer treatment where there are thousands of new research papers being published every day, we actually have a shot at having the machine read them all and help a board of cancer specialists answer questions about the latest research,” he said. While this machine reading technology is in the early stages of development, researchers have found they can make progress by narrowing the focus to specific areas such as clinical oncology, explained Peter Lee, corporate vice president of Microsoft Healthcare in Redmond, Washington. That’s why Mockus and her colleagues at JAX are collaborating with computer scientists working on Microsoft’s Project Hanover who are developing AI technology that enables machines to read complex medical and research documents and highlight the important information they contain. “Because there is so much data and so many complexities, without embracing and incorporating artificial intelligence and machine learning to help in the interpretation of the data, progress will be slow,” she said. The challenge is to find the most relevant cancer-related information from the 4,000 or so biomedical research papers published each day, according to Susan Mockus, the associate director of clinical genomic market development with JAX’s genomic medicine institute in Farmington, Connecticut. The tool, called the Clinical Knowledgebase, or CKB, is a searchable database where subject matter experts store, sort and interpret complex genomic data to improve patient outcomes and share information about clinical trials and treatment options. To harness this potential, researchers at The Jackson Laboratory, an independent, nonprofit biomedical research institution also known as JAX and headquartered in Bar Harbor, Maine, developed a tool to help the global medical and scientific communities stay on top of the continuously growing volume of data generated by advances in genomic research. The potential for this new era of cancer treatment stems from advances in genome sequencing technology that enables researchers to more efficiently discover the specific genomic mutations that drive cancer, and an explosion of research on the development of new drugs that target those mutations. Biomedical researchers are embracing artificial intelligence to accelerate the implementation of cancer treatments that target patients’ specific genomic profiles, a type of precision medicine that in some cases is more effective than traditional chemotherapy and has fewer side effects.
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