Reinforced Metapath Optimization in Heterogeneous Information Networks for Drug-Target Interaction Prediction.

Graph neural networks offer an effective avenue for predicting drug-target interactions. In this domain, researchers have found that constructing heterogeneous information networks based on metapaths using diverse biological datasets enhances prediction performance. However, the performance of such methods is closely tied to the selection of metapaths and the compatibility between metapath subgraphs and graph neural networks.

Co/Ce-MOF-Derived Oxygen Electrode Bifunctional Catalyst for Rechargeable Zinc-Air Batteries.

Improving the practicality of rechargeable zinc-air batteries relies heavily on the development of oxygen electrode catalysts that are low-cost, durable, and highly efficient in performing dual functions. In the present study, a catalyst with atomic Ce and Co distribution on a nitrogen-doped carbon substrate was prepared by doping the rare earth elements Ce and Co into a metal-organic framework precursor. Rare earth element Ce, known for its unique structure and excellent oxygen affinity, was utilized to regulate the catalytic activity.

Partial Alleviation of Homologous Superinfection Exclusion of SeMNPV Latently Infected Cells by G1 Phase Infection and G2/M Phase Arrest.

Viral infection can regulate the cell cycle, thereby promoting viral replication. Hijacking and altering the cell cycle are important for the virus to establish and maintain a latent infection. Previously, Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV)-latently infected P8-Se301-C1 cells, which grew more slowly than Se301 cells and interfered with homologous SeMNNPV superinfection, were established.

Machine Learning-Assisted Study of RENC-Doped Graphene as Potential Electrocatalysts for Oxygen Electrode Reactions.

In the application of renewable energy, the oxidation-reduction reaction (ORR) and oxygen evolution reaction (OER) are two crucial reactions. Single-atom catalysts (SACs) based on metal-doped graphene have been widely employed due to their high activity and high atom utilization efficiency. However, the catalytic activity is significantly influenced by different metals and local coordination, making it challenging to efficiently screen through either experimental or density functional theory (DFT) calculations.

RT-SRTS: Angle-agnostic real-time simultaneous 3D reconstruction and tumor segmentation from single X-ray projection.

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiration limits its accuracy. Recently, 3D imaging from a single X-ray projection has received extensive attention as a promising approach to address this issue. However, current methods can only reconstruct 3D images without directly locating the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirements of motion control in radiotherapy.

LoGo-GR: A Local to Global Graphical Reasoning Framework for Extracting Structured Information From Biomedical Literature.

In the biomedical literature, entities are often distributed within multiple sentences and exhibit complex interactions. As the volume of literature has increased dramatically, it has become impractical to manually extract and maintain biomedical knowledge, which would entail enormous costs. Fortunately, document-level relation extraction can capture associations between entities from complex text, helping researchers efficiently mine structured knowledge from the vast medical literature.

A novel network security situation assessment model based on multiple strategies whale optimization algorithm and bidirectional GRU.

The rapid development of the internet has brought about a comprehensive transformation in human life. However, the challenges of cybersecurity are becoming increasingly severe, necessitating the implementation of effective security mechanisms. Cybersecurity situational awareness can effectively assess the network status, facilitating the formulation of better cybersecurity defense strategies.

Spatiotemporally continuous PM dataset in the Mekong River Basin from 2015 to 2022 using a stacking model.

With the potential to cause millions of deaths, PM pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM pollution and the existing PM studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM concentrations.

Mechanistic study of Eu single atoms occupying four vacancy centers as potential electrocatalysts for the oxygen reduction reaction.

The oxygen reduction reaction (ORR) on the oxygen electrode plays a critical role in rechargeable metal-air batteries, and the development of electrochemical energy storage and conversion technologies for the ORR is of great significance. In this study, the catalytic performance of rare earth-doped graphene (EuNC-Gra) as an electrocatalyst for the ORR was investigated. The results showed that a majority of the catalysts exhibited good ORR catalytic activity under acidic conditions, with some approaching or even surpassing commercial Pt-based catalysts ( = 0.

TrGPCR: GPCR-Ligand Binding Affinity Prediction Based on Dynamic Deep Transfer Learning.

Predicting G protein-coupled receptor (GPCR) -ligand binding affinity plays a crucial role in drug development. However, determining GPCR-ligand binding affinities is time-consuming and resource-intensive. Although many studies used data-driven methods to predict binding affinity, most of these methods required protein 3D structure, which was often unknown.

Extracting biomedical relation from cross-sentence text using syntactic dependency graph attention network.

In biomedical literature, cross-sentence texts can usually express rich knowledge, and extracting the interaction relation between entities from cross-sentence texts is of great significance to biomedical research. However, compared with single sentence, cross-sentence text has a longer sequence length, so the research on cross-sentence text information extraction should focus more on learning the context dependency structural information. Nowadays, it is still a challenge to handle global dependencies and structural information of long sequences effectively, and graph-oriented modeling methods have received more and more attention recently.

DNA protein binding recognition based on lifelong learning.

In recent years, research in the field of bioinformatics has focused on predicting the raw sequences of proteins, and some scholars consider DNA-binding protein prediction as a classification task. Many statistical and machine learning-based methods have been widely used in DNA-binding proteins research. The aforementioned methods are indeed more efficient than those based on manual classification, but there is still room for improvement in terms of prediction accuracy and speed.

Sublethal effects of chlorantraniliprole on growth, biochemical and molecular parameters in two chironomids, Chironomus kiiensis and Chironomus javanus.

Pesticide residues have serious environmental impacts on rice-based ecosystems. In rice fields, Chironomus kiiensis and Chironomus javanus provide alternative food sources to predatory natural enemies of rice insect pests, especially when pests are low. Chlorantraniliprole is a substitute for older classes of insecticides and has been used extensively to control rice pests.

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.

Membrane proteins are an essential part of the body's ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed.

A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction.

Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure and target protein sequence and so on. However, in the process of fusion, how to allocate the weight of heterogeneous information reasonably is a huge challenge.

Metabolism-based ventilation monitoring and control method for COVID-19 risk mitigation in gymnasiums and alike places.

Gymnasiums, fitness rooms and alike places offer exercise services to citizens, which play positive roles in promoting health and enhancing human immunity. Due to the high metabolic rates during exercises, supplying sufficient ventilation in these places is essential and extremely important especially given the risk of infectious respiratory diseases like COVID-19. Traditional ventilation control methods rely on a single CO sensor (often placed at return air duct), which is often difficult to reflect the human metabolic rates accurately, and thus can hardly control the infection risk instantly.

Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map.

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition.

RFLMDA: A Novel Reinforcement Learning-Based Computational Model for Human MicroRNA-Disease Association Prediction.

Numerous studies have confirmed that microRNAs play a crucial role in the research of complex human diseases. Identifying the relationship between miRNAs and diseases is important for improving the treatment of complex diseases. However, traditional biological experiments are not without restrictions.

G Protein-Coupled Receptor Interaction Prediction Based on Deep Transfer Learning.

G protein-coupled receptors (GPCRs) account for about 40% to 50% of drug targets. Many human diseases are related to G protein coupled receptors. Accurate prediction of GPCR interaction is not only essential to understand its structural role, but also helps design more effective drugs.

Research on RNA secondary structure predicting via bidirectional recurrent neural network.

Background: RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure.

Empirical Potential Energy Function Toward ab Initio Folding G Protein-Coupled Receptors.

Approximately 40-50 percent of all drugs targets are G protein-coupled receptors (GPCRs). Three-dimensional structure of GPCRs is important to probe their biophysical and biochemical functions and their pharmaceutical applications. Lacking reliable and high quality free function is one of the ugent problems of computational predicting the three-dimensional structure in this community.

Research on predicting 2D-HP protein folding using reinforcement learning with full state space.

Background: Protein structure prediction has always been an important issue in bioinformatics. Prediction of the two-dimensional structure of proteins based on the hydrophobic polarity model is a typical non-deterministic polynomial hard problem. Currently reported hydrophobic polarity model optimization methods, greedy method, brute-force method, and genetic algorithm usually cannot converge robustly to the lowest energy conformations.