Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength.

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes.

Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.

Insight into how mutations affect protein stability is crucial for protein engineering, understanding genetic diseases, and exploring protein evolution. Numerous computational methods have been developed to predict the impact of amino acid substitutions on protein stability. Nevertheless, comparing these methods poses challenges due to variations in their training data.

Advanced Composite Solid Electrolytes for Lithium Batteries: Filler Dimensional Design and Ion Path Optimization.

Composite solid electrolytes are considered to be the crucial components of all-solid-state lithium batteries, which are viewed as the next-generation energy storage devices for high energy density and long working life. Numerous studies have shown that fillers in composite solid electrolytes can effectively improve the ion-transport behavior, the essence of which lies in the optimization of the ion-transport path in the electrolyte. The performance is closely related to the structure of the fillers and the interaction between fillers and other electrolyte components including polymer matrices and lithium salts.

[Detection of IgG protein in human urine based on vertical flow paper microfluidic chip].

The kidney is the body's most important organ and the protein components in urine could be detected for diagnosing certain diseases. The amount of IgG protein in urine could be used to determine the degree of kidney function damage. IgG protein in human urine was detected by vertical flow paper-based microfluidic chip, double-antibody sandwich immunoreaction, and cell phone image processing.