Machine-Learning Algorithm Uses Time-Series Data to Reveal Underlying Gene Regulatory Networks in Cells

Biologists have long understood the various parts within the cell. But how these parts interact with and respond to each other is largely unknown. "We want to understand how cells make decisions, so we can control the decisions they make," said Northwestern University's Neda Bagheri (photo), PhD. "A cell might decide to divide uncontrollably, which is the case with cancer. If we understand how cells make that decision, then we can design strategies to intervene." To better understand the mysterious interactions that occur inside cells, Dr. Bagheri and her team have designed a new machine learning algorithm that can help connect the dots among the genes' interactions inside cellular networks. Called "Sliding Window Inference for Network Generation," or SWING, the algorithm uses time-series data to reveal the underlying structure of cellular networks. Supported by the National Science Foundation, the National Institutes of Health, and Northwestern's Biotechnology Training Program, the research was published online on February 12, 2018 in PNAS. Justin Finkle and Jia Wu, graduate students in Dr. Bagheri's laboratory, served as co-first authors of the paper, which is titled “Windowed Granger Causal Inference Strategy Improves Discovery of Gene Regulatory Networks.” In biological experiments, researchers often perturb a subject by altering its function and then measure the subject's response. For example, researchers might apply a drug that targets a gene's expression level and then observe how the gene and downstream components react. But it is difficult for those researchers to know whether the change in genetic landscape was a direct effect of the drug or the effect of other activities taking place inside the cell.
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