In-Plane Bulk Photovoltaic Effect in a MoSe/NbOI Heterojunction for Efficient Polarization-Sensitive Self-Powered Photodetection.

Two-dimensional ferroelectric materials can generate a bulk photovoltaic effect, making them highly promising for self-powered photodetectors. However, their practical application is limited by a weak photoresponse due to a weak transition strength and wide band gap. In this study, we construct a van der Waals heterojunction using NbOI, which has significant in-plane polarization, with a highly absorbing MoSe layer.

Glass Disorder Modulated Luminescence in Zero-Dimensional Antimony-Chloride Coplanar Dimers for Optical Anti-counterfeiting.

Zero-dimensional metal halides have received wide attention due to their structural diversity, strong quantum confinement, and associated excellent photoluminescence properties. A reversible and tunable luminescence would be desirable for applications such as anti-counterfeiting, information encryption, and artificial intelligence. Yet, these materials are underexplored, with little known about their luminescence tuning mechanisms.

NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information.

The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing.

Identification of potential drug-targets by combining evolutionary information extracted from frequency profiles and molecular topological structures.

Identifying interactions among drug compounds and target proteins is the basis of drug research and plays a crucial role in drug discovery. However, determining drug-target interactions (DTIs) and potential protein-compound interactions by biological experiment-based method alone is a very complicated, expensive, and time-consuming process. Hence, there is an intense motivation to design in silico prediction methods to overcome these obstacles.

Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest.

Protein is an essential component of the living organism. The prediction of protein-protein interactions (PPIs) has important implications for understanding the behavioral processes of life, preventing diseases, and developing new drugs. Although the development of high-throughput technology makes it possible to identify PPIs in large-scale biological experiments, it restricts the extensive use of experimental methods due to the constraints of time, cost, false positive rate and other conditions.

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method.

RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. Unfortunately, due to time and cost constraints, it is difficult for biological experiments to determine the relationship between RNA and protein to a large extent. So there is an urgent need for reliable computational methods to quickly and accurately predict RNA-protein interaction.

A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.

Identifying the interaction between drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing drug-protein interactions (DTIs), the screening of targets not only takes a lot of time and money but also has high false-positive and false-negative rates. Therefore, it is imperative to develop effective automatic computational methods to accurately predict DTIs in the postgenome era.