Democratizing Machine Learning Algorithms for Integrated Data-Sharing
On Artificial Intelligence and Ethics
The Tango of AI and Big Data
Transforming the Art Museum in the 21st Century.
Machine Learning: Enabling New Capabilities in Health and Beyond
Shiva Amiri, PhD, Director Research Infrastructure, 23andMe
Revolutionizing Disease Predictions Using Machine Learning
Ylan Kazi, Vice President, Data Science + Machine Learning, UnitedHealthcare
How AI and Machine Learning are Reinventing Healthcare Sector
Julius Bogdan, Director of Analytics and Data Innovation, SCL Health
Embedding AI in everything. Why we can't do without it now
Vishwa Kolla, AVP, Head of Advanced Analytics, John Hancock Insurance
Thank you for Subscribing to CIO Applications Weekly Brief
With Machine Learning and Library Approach Scientists Optimize SNAs
With their potential to treat a wide variety of diseases spherical nucleic acids (SNAs) are poised to revolutionize medicine, but before this nanostructures can reach its full ability scientists need to optimize their various components. For the optimization process, researchers from Northwestern University have developed a direct route to SNAs, to make them into a viable treatment option for multiple forms of cancer, genetic diseases, and neurological disorders.
SNAs are a category of personalized medicines, and optimizing them are challenging as their structures can vary in many ways. But the nanotechnology expert team of researchers developed a machine learning and library approach to optimally synthesize, analyze, and choose these SNA structures rapidly.
Developed at Northwestern, these nanostructures consist of balls like forms of DNA and RNA arranged on the surface of a nanoparticle. Researchers can digitally design SNAs to be precise, personalized treatments that shut off genes and cellular activity, and more recently, as vaccines that stimulate the body's immune system to treat diseases, including certain forms of cancer.
According to the research team, the research unveiled SNAs variation in structure leads to biological activities affecting the efficacy of these nanostructures. Because these relationships were not predicted, they would have gone unnoticed in a typical study of a small set of structures. For instance, their nanoparticle size or composition can decide their potential to stimulate an immune response. In light of this finding, researchers can rank the structural variables in order of significance and efficacy, and help establish design rules for SNA effectiveness.
This development process, which screened more than a thousand structures at a time, was aided by SAMDI-MS technology. The related study was published in Nature Biomedical Engineering titled " Addressing Nanomedicine Complexity with Novel High-Throughput Screening and Machine Learning."
Check this out: Top Artificial Intelligence Companies