Dash's flexible Python framework makes it a powerful tool for scientific research and discovery. A recent study published in Nature, the international scientific journal, utilizes a machine learning tool built with Dash to help scientists predict the outcomes of certain genome editing methods.
The machine learning algorithm, called inDelphi, is published as a Dash application built by Max W. Shen, a computational biology PhD candidate at MIT.
Shen, with Mandana Arbab, PhD, of Harvard University, and eight other researchers, trained the inDelphi algorithm to predict the results of a specific mechanism of DNA repair called end-joining, which is known to create unpredictable and undesirable insertions and deletions during the genome editing process. These insertions and deletions can result in cell death. inDelphi makes this process more predictable, controllable and useful for scientists doing genomic editing.
Shen had worked before with computational biology tools that weren't user-friendly, and he didn't want to feel like he was "fighting the code" just to do his research. "I wanted to create a web app that is a joy to use. As a Python user, Dash was the obvious choice," said Shen.
Creating the initial, functioning inDelphi Dash app took just a few days, and Shen used the next several weeks to create a polished, elegant end-user experience.
"Not only has Dash been a great support for my scientific work, Dash has been artistically fulfilling to use," said Shen. "It was amazing to build a functioning web app in just a handful of days, especially one that was slick and beautiful straight out of the box."
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