Evidential deep learning for trustworthy prediction of enzyme commission number.

The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel classification problem with thousands of classes. Here, we demonstrate that a novel evidential deep learning model (named ECPICK) makes trustworthy predictions of enzyme commission (EC) numbers with data-driven domain-relevant evidence, which results in significantly enhanced predictive power and the capability to discover potential new motif sites.

Micro environment data on the Daeungbojeon hall and the Palsangjeon hall of the Beopjusa temple in Republic of Korea.

This article provides long-term environmental change data for wooden buildings; it also reflects environmental data provided by the Korea Meteorological Administration. In the case of field survey, data logger was installed on the left rear and right front sides of the buildings. Datasets on the Beopjusa temple were collected at 1 h intervals in each building.

Penicillin Binding Protein from Isolated from Nuruk for Food Biopreservative.

Background: Korean traditional nuruk, consisting of a variety of microorganisms, is widely used in traditional liquor materials. The present study evaluated the antimicrobial activity of strains isolated from Korean traditional nuruk in 2016.

Methods: The strain was isolated from Korea traditional nuruk and performed antimicrobial activities using the paper disc test and phylogenetic analysis using 16S rRNA sequencing.