AI News, Publications artificial intelligence

Publication QA: Using Artificial Intelligence to Predict Drug Interactions and More Effective Treatments for Tuberculosis

Sherman, Sriram Chandrasekaran Published in mBio: October 2019 Read the publication on mBio The Sherman lab is using experimental and computational systems biology tools to understand how the pathogen Mycobacterium tuberculosis (MTB) responds to stresses encountered during infection and to drug exposure.

We are studying this pathogen in part to devise new treatment strategies for the disease that it causes: tuberculosis (TB), which is the world’s deadliest infectious disease, killing approximately 2 million people each year.

Third, the input data needed to calculate predictions for new drugs are highly modular, requiring only the bacterial response for the new individual drug to predict interactions between all possible combinations with previously profiled drugs.

We are also working with Dr. Rafael Hernandez (CGIDR faculty) to extend the underlying modeling approach to be able to predict efficacy for drug regimens treating other recalcitrant pathogens, including Mycobacteria absessus, a pathogen related to MTB that commonly infects cystic fibrosis patients in the U.S. Seattle Children’s CGIDR contributing authors:

Artificial Intelligence in Cancer - Baishideng Publishing Group

The research interests focus on biotherapy of cancer, tumor microenvironment, statistical genetics, biomarker research, and deep learning of medical imaging.

Ma Lao serves as editor in chief of special issues of magazines such as Frontiers in Pharmacology, Contrast media and molecular imaging and some magazines.

The research interests focus on biotherapy of cancer, tumor microenvironment, statistical genetics, biomarker research, and deep learning of medical imaging.

  • On 14. april 2021

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