PNRI, Indivumed Launch Collaboration
News Aug 12, 2016
The Pacific Northwest Research Institute and Indivumed have announced that they have entered a strategic collaboration led by Dr. David Galas, a world renowned expert in computational biology. The collaboration is designed to harness the power of molecular and clinical cancer data for tailoring successful treatments for individual cancer patients.
Indivumed’s global Cancer Database and Biobank contains more than 4.5 million biological data points associated with greater than 600,000 discrete tumor, blood and urine samples obtained from more than 25,000 cancer patients. Indivumed stringently follows highly standardized processes for the collection and preservation of biospecimen samples from cancer patients, which results in the highest quality molecular and clinical datasets.
“Accessing Indivumed’s global Cancer Database is a great opportunity for us to better understand how all the pieces of a cancer patient’s biology fit together by detecting complex dependencies in this extensive data set.” said Dr. Galas. “Understanding the biology of cancer from the patient data will provide invaluable insight into various cancers and how to treat it in the most precise manner.”
The Galas Lab will apply its proprietary computational method for deciphering the biological complexity of human development and disease. “The method has been successfully used to analyze a wide variety of data ranging from RNA profiles in patients with autoimmune disease to childhood development,” said Dr. Nikita Sakhanenko, Senior Staff Scientist at PNRI. This collaboration will be the first application of the method in cancer.
Hartmut Juhl, Founder and Chief Executive Officer of Indivumed commented that “allowing David’s extremely innovative analytical platform to survey our Cancer Database will enable Indivumed and collaborative research partners to better understand the complexity of cancer and to translate their specific targets and biomarkers in the context of the clinical world of cancer. Overall, we hope to get closer to the development of a true precision medicine for cancer patients.”
An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.
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