Researchers Developed A Sophisticated AI Algorithm That Can Identify Therapeutic Targets For Both Glioblastoma Multiforme (GBM) And Other Cancers

Vasyl - - illustrative purposes only, not the actual person

Researchers from the University of Miami Miller School of Medicine recently developed a new AI algorithm that can perform advanced computational analysis. In doing so, possible therapeutic targets for glioblastoma multiforme (GBM), as well as other forms of cancer, can be identified.

This finding could have significant impacts on the future of GBM treatment– since GBM is a fast-growing, aggressive, and often fatal brain cancer.

Additionally, the researcher’s work may pave the way for advanced treatments of certain lung, breast, and pediatric cancers.

“Our work represents translational science that offers immediate opportunities to change the way glioblastoma patients are routinely managed in the clinic,” said Antonio Iavarone, the study’s senior author.

“Our algorithm offers applications to precision cancer medicine, giving oncologists a new tool to battle this deadly disease and other cancers as well.”

The AI algorithm is called Substrate Phosphosite-based Inference for Network of Kinases– otherwise known as SPHINKS.

SPHINKS actually used deep-machine learning to enable the research team to identify two protein kinases known as PKCδ and DNAPKcs. Then, the AI algorithm helped the scientists experimentally validate the protein kinases’ association with tumor progression among two GBM subtypes.

In other words, this validation enabled the team to identify PKCδ and DNAPKcs as viable targets for potential cancer therapies.

Protein kinases are already currently used as vital targets in precision cancer medicine in order to tailor a patient’s cancer treatment to their specific needs. Clinicians target drugs toward the most active kinases– which the study’s authors refer to as “master kinases.”

Vasyl – – illustrative purposes only, not the actual person

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