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Big Data, A.I. Applied to Precision Medicine for Lung Disease

Human lungs illustration


10 July 2018. A medical analytics company and two academic scientists are applying artificial intelligence technologies to discover precise therapies for people with idiopathic pulmonary fibrosis, a chronic lung disease. Financial and intellectual property aspects of the collaboration between NuMedii Inc. in San Mateo, California, Naftali Kaminski at Yale University School of Medicine, and Ivan Rosas at Brigham and Women’s Hospital and Harvard Medical School were not disclosed.

Idiopathic pulmonary fibrosis is a progressive lung disease usually affecting people between the ages of 50 and 70. The disorder results in fibrosis or scar tissue building up in the lungs, limiting the ability of lungs to transfer oxygen to the blood stream. The most common symptoms are shortness of breath and a dry hacking cough, as well as a loss of appetite and weight loss in some cases. The scarring of lung tissue increases over time, often leading to other serious lung conditions, including lung cancer and blood clots in the lungs. From 13 to 20 per 100,000 people worldwide experience idiopathic pulmonary fibrosis. Some 100,000 people in the U.S. have the condition, with 30,000 to 40,000 new cases diagnosed each year. Most patients die within five years following diagnosis.

The collaboration is bringing together research by Kaminski and Rosas on idiopathic pulmonary fibrosis, with the big data analytics and artificial intelligence capabilities of NuMedii. Kaminiski is co-director of Yale’s Center for Precision Pulmonary Medicine, or P2Med, that aims to identify molecular characteristics of lung diseases to enable more precise patient-specific diagnostics and therapies. Rosas also studies molecular characteristics of patients with idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease, or COPD, particularly those indicators associated with aging that contributes to both conditions.

NuMedii offers computational services in its artificial intelligence for drug discovery platform that analyzes large quantities of genomic, research, and medical record data for precision medicine. The company’s systems access data from public academic and private company databases, as well as its own data stores, and applies artificial intelligence analytics, such as machine and deep learning algorithms. NuMedii’s analyses are used to highlight new biomarkers and targets for diagnostics, and therapeutics to meet personalized molecular properties.

In this project, NuMedii’s systems will analyze data from Yale’s P2Med center on idiopathic pulmonary fibrosis. The P2Med center performs single-cell sequencing of RNA, genetic material coded from DNA giving protein-production instructions to cells. The addition of high-volume RNA sequencing data to NuMedii’s analytical engines, under the direction of Kaminski and Rosas, is expected to produce more insights, as well as precise biomarkers and therapeutic targets for treating idiopathic pulmonary fibrosis, or IPF.

Kaminiski says in a NuMedii statement, “The data we will derive by molecularly profiling thousands of single cells in every patient’s sample will allow us to understand disease at an unprecedented resolution and should allow us to identify new cell types and biological mechanisms involved in IPF.” Rosas adds that “the opportunity to leverage our combined scientific research along with data analytics and drug discovery capabilities to facilitate the translation of our research into new precision therapies … will help patients with IPF and the physicians who treat them.”

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