Machine Learning Discovers Previously Unknown Genes in New UNM-Led Study

In a groundbreaking study conducted by University of New Mexico researchers, scientists have harnessed the power of machine learning to identify a set of previously unknown genes associated with autophagy, a vital cellular process involved in recycling and maintaining cellular health.

Leveraging a state-of-the-art machine learning model, the study identified 193 genes as potential contributors to autophagy machinery. These previously overlooked “dark genes” represent promising avenues for unraveling the mysteries of autophagy and its role in cellular functioning and complex diseases such as Alzheimer's, said UNM neuroscientist Elaine Bearer, MD, PhD.

“This is another form of unbiased, data-driven science,” Bearer said. “What machine learning is allowing us to do is avoid the guesswork and do discovery science in a non-hypothesis-driven way.”

The study, entitled "Autophagy Dark Genes: Can We Find Them with Machine Learning?" was recently published in the journal Natural Sciences, and aimed to identify an autophagy-related gene set by combining diverse biological features and datasets and plugging the data into an artificial intelligence algorithm.

“The idea was, ‘Can we find these dark, hidden, secret genes with an artificial intelligence investigation?’” Bearer said.

The answer is yes, machine learning can guide genomics research to gain a more complete annotation of complex processes.

But machine learning is not the end of the task, Bearer emphasizes. Once the artificial intelligence has identified something, it’s up to scientists to validate both the process and the results.

To accomplish this, a research team at UNM employed the MetaPath/XGBoost (MPxgb) machine learning model, which was trained using data from 17 different sources. The artificial intelligence research investigation began in 2019, led by Tudor Oprea, MD, PhD, former director for Screening Informatics for UNM’s Center for Molecular Discovery and Drug Discovery Core and a member at the UNM Comprehensive Cancer Center.

Mohsen Ranjbar, PharmD, a UNM graduate student in chemistry and chemical biology, took Oprea’s research and conducted a validation search, combing through the Autophagy Database and through research publication databases, like PubMed, to see if the model demonstrated high accuracy in distinguishing already-known autophagy-associated genes.