Tag: Sriram Chandrasekaran
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U-M BME Researchers Create an 8D-Model of Cancer Metabolism
In a new study, researchers at the University of Michigan Department of Biomedical Engineering (U-M BME) have harnessed machine learning to successfully predict the metabolic inner workings of nearly 1,000 different cancer cell lines, creating a roadmap that could lead to highly tailored, synergistic combination therapies.
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Researchers Develop AI Model to Target Safer, More Effective Drug Combinations for Antimicrobial Therapy
The research pioneers a custom AI model that predicts not only the efficacy of drug combinations against key pathogens, but also their risk of toxic side effects, addressing a major obstacle in advancing new therapies.
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MIDAS PODS Grant Fuels U-M BME Innovation in Drug Synergy Prediction Using AI
Many computational models work on the principle of “homophily”—assuming that similarly acting drugs will yield the best results. However, this assumption may overlook the complexity of biological systems.
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U-M BME Faculty Pioneer Online Educational Modules on AI Foundations
The development of these online learning modules marks the first step in a broader vision to create a comprehensive educational program focused on AI in BME.
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SCOOTI: Unlocking Cellular Objectives Through Machine Learning
The new tool, developed by Sriram Chandrasekaran, Associate Professor, Biomedical Engineering, and his PhD student Da-Wei Lin, is called SCOOTI (Single-cell Optimization Objective and Trade-off Inference).
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Multimodal AI model may guide personalized treatments for tuberculosis
AI approach helps researchers interpret large biomedical data sets.
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Sriram Chandrasekaran Receives Research Scout Grant
Grant to invest in ‘bold science’
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BME Faculty, Research Staff Honored With 2023 EBS Awards
The EBS is a cooperative program that works for the advancement of research and teaching in the Medical School Basic Sciences through the development of new research initiatives and recruitment of new faculty.
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How an AI solution can design new tuberculosis drug regimens
Dubbed INDIGO, short for INferring Drug Interactions using chemoGenomics and Orthology, the software tool has shown that the potency of tuberculosis drugs can be amplified when they are teamed with antipsychotics or antimalarials.
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A ‘decathlon’ for antibiotics puts them through more realistic testing
Surprise findings could upend the current drug discovery approach for treating one of the most dangerous hospital-borne infections.