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Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

Ramprasad et al. | Mar 18, 2020

Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

In this study, the authors test whether providing a larger dataset of glass transition temperatures (Tg) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and Tg is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's Tg, providing valuable improvements to this useful platform.

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Bacteroides thetaiotaomicron Growth Numbers Are Unchanged in the Presence of Yogurt

Phan et al. | Dec 29, 2016

<i>Bacteroides thetaiotaomicron</i> Growth Numbers Are Unchanged in the Presence of Yogurt

Disruptions to the microbiome, specifically the imbalance in the two major phyla, the Firmicutes and the Bacteroidetes, have been linked to the development of obesity. This study explored whether or not Fage plain total 0% Greek yogurt, which contains live and active bacterial cultures belonging to the Firmicute phylum, could decrease the numbers of Bacteroides thetaiotaomicron, an organism found in the human gut that belongs to the Bacteroidetes phylum.

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