Researchers at Tufts University and the University of Maryland, Baltimore County have used a machine-learning artificial intelligence (AI) platform to predict three reagents that produced a previously unseen cancer-like phenotype in tadpoles.
They employed a computer model to explain how to realize partial melanocyte conversion to a metastatic form using one or more interventions.
The model predicted an exact mixture of altanserin, reserpine, and VP16-XlCreb1 would yield such results. The model anticipated the segment of tadpoles that would retain normal melanocytes within 1% of the in vivo outcomes while aggregating the percentage of tadpoles that exhibited partial or full conversion in vivo.
The model ran 576 virtual experiments, all but one of which were unsuccessful.
"Such approaches are a key step for regenerative medicine, where a major obstacle is the fact that it is usually very hard to know how to manipulate the complex networks discovered by bioinformatics and wet lab experiments in such a way as to reach a desired therapeutic outcome," says Tufts professor Michael Levin. "For almost any problem where a lot of data are available, we can use this model-discovery platform to find a model and then interrogate it to see what we have to do to achieve result X."
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