Machine learning offers high-definition glimpse of how genomes organize in single cells
— Read on phys.org/news/2021-10-machine-high-definition-glimpse-genomes-cells.html

From the current article: -“Within the microscopic boundaries of a single human cell, the intricate folds and arrangements of protein and DNA bundles dictate a person’s fate: which genes are expressed, which are suppressed, and—importantly—whether they stay healthy or develop disease.

Despite the potential impact these bundles have on human health, science knows little about how genome folding happens in the cell nucleus and how that influences the way genes are expressed. But a new algorithm developed by a team in Carnegie Mellon University’s Computational Biology Department offers a powerful tool for illustrating the process at an unprecedented resolution.

The algorithm, known as Higashi, is based on hypergraph representation learning—the form of machine learning that can recommend music in an app and perform 3D object recognition.

School of Computer Science doctoral student Ruochi Zhang led the project with Ph.D. candidate Tianming Zhou and Jian Ma, the Ray and Stephanie Lane Professor of Computational Biology. Zhang named Higashi after a traditional Japanese sweet, continuing a tradition he began with other algorithms he developed.”-