Laboratory Informatics Guide 2020
Using AI to fight cancer
Researchers at Microsoft and the Jackson Laboratory are using AI to curate and refine data that can be used to better treat cancer patients
Te use of Artificial Intelligence (AI) combined with large medical databases is providing biomedical researchers with a platform that can be used to develop cancer treatments that target patients’ specific genomic profiles. Researchers at Microsoſt’s Project
Hanover, are collaborating with the Jackson Laboratory (JAX) to develop a precision medicine initiative that is designed to help doctors diagnose patients faster and more accurately than ever before. By targeting treatments at particular
cancer mutations, developing personalised treatments for specific patients will not only make cancer more treatable but it will also provide a more effective treatment than today’s radiation or chemotherapy treatments. JAX researchers have created a tool called
the Clinical Knowledgebase (CKB). Te CKB is a searchable database that enables researchers to curate complex genomic data which can be used by doctors to more effectively treat patients and share information about clinical trials and treatment options. JAX-CKB can help increase clinician
confidence in completeness and accuracy of the information related to the patient’s tumour genomic profile.
For translational and clinical researchers,
JAX-CKB provides thousands of literature citations, FDA drug labels, and clinical trials relative to a tumour’s genomic mutation or profile, resulting in a clear and up- to-date picture of discoveries and active developments for a variety of biomarkers. Susan Mockus, associate director of
clinical genomics market development at JAX Genomic Medicine, is working with computer scientists on Microsoſt’s Project Hanover to develop AI technology designed to strengthen and accelerate the curation process. ‘Tere 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,’ explained Mockus.
Increasing the accuracy of medicinal data Te explosion in available data in many scientific disciplines is creating huge opportunities but the advent of big data also creates challenges in harnessing and effectively using the data that is available. Today medicine is imprecise because it is oſten based on the statistical probability of a drug or treatment working on people in
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
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www.scientific-computing.com/LIG20
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