Deep learning methods for protein function prediction.

Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.

Instrumental and Sensory Characteristics of Commercial Korean Rice Noodles.

In this study, the rheological properties of several commercial rice noodle strands were investigated. In the bending test, failure stress decreased as the cooking temperature increased from 80 to 90 °C, and the cooking time increased from 3 to 4 min for higher rice content noodles (>60%). The stress-relaxation test and sensory tests were carried out with bundles of noodles to investigate correlations with the bending test. The modulus of elasticity was higher at 80 than 90 °C. However, no correlation was found between cooking temperature and the rheological properties of lower rice content noodles. In the stress relaxation test, the deviation was larger due to the empty space in the bundle. In the correlation analysis, sensory stickiness was correlated with a modulus of elasticity in the bending test. Comparing the bending and stress-relaxation tests, each instrumental variable showed differences in the rheological properties of rice noodles in strands and bundles. However, the bending test measured with noodle strands seemed to be most suitable as a method of measuring the rheological properties of rice noodles.