A5059750805- Machine Learning in Bioinformatics
- Protein Structure and Dynamics
- Computational Drug Discovery Methods
- RNA and protein synthesis mechanisms
- Genomics and Phylogenetic Studies
- Machine Learning in Materials Science
- Microbial Natural Products and Biosynthesis
- Horticultural and Viticultural Research
- Atmospheric aerosols and clouds
- Smart Agriculture and AI
- Remote-Sensing Image Classification
- Remote Sensing and Land Use
- Remote Sensing in Agriculture
- Fractal and DNA sequence analysis
- Bioinformatics and Genomic Networks
- Air Quality Monitoring and Forecasting
- Atmospheric chemistry and aerosols
- Spectroscopy and Chemometric Analyses
- Influenza Virus Research Studies
- Receptor Mechanisms and Signaling
University of Tsukuba
2023-2024
Suzhou University of Science and Technology
2021-2023
Quzhou University
2023
University of Electronic Science and Technology of China
2023
South China Agricultural University
2023
Identification of new indications for existing drugs is crucial through the various stages drug discovery. Computational methods are valuable in establishing meaningful associations between and diseases. However, most predict drug-disease based solely on similarity data, neglecting biological chemical information. These often use basic concatenation to integrate information from different modalities, limiting their ability capture features a comprehensive in-depth perspective. Therefore,...
Biologically important effects occur when proteins bind to other substances, of which binding DNA is a crucial one. Therefore, accurate identification protein-DNA residues for further understanding the interaction mechanism. Although wet-lab methods can accurately obtain location bound residues, it requires significant human, financial and time costs. There thus an urgent need develop efficient computational-based methods. Most current state-of-the-art are two-step approaches: first step...
Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in Therefore, it very important study which subject worthy study. Although traditional biotechnology has high precision, its cost and efficiency are increasingly unable meet the needs modern society. Machine learning methods can make up for deficiencies biological experimental techniques certain extent, but they not as simple fast deep data processing. In this paper, framework...
DNA-binding proteins (DBPs) have a significant impact on many life activities, so identification of DBPs is crucial issue. And it greatly helpful to understand the mechanism protein-DNA interactions. In traditional experimental methods, time-consuming and labor-consuming identify DBPs. recent years, researchers proposed lots different DBP methods based machine learning algorithm overcome shortcomings mentioned above. However, most existing cannot get satisfactory results. this paper, we...
Background: The prediction of drug-target interactions (DTIs) plays an essential role in drug discovery. Recently, deep learning methods have been widely applied DTI prediction. However, most the existing research does not fully utilize molecular structures compounds and sequence proteins, which makes these models unable to obtain precise effective feature representations. Methods: In this study, we propose a novel framework combining transformer graph neural networks for predicting DTIs....
Accurate litchi identification is of great significance for orchard yield estimations. Litchi in natural scenes have large differences scale and are occluded by leaves, reducing the accuracy detection models. Adopting traditional horizontal bounding boxes will introduce a amount background overlap with adjacent frames, resulting reduced accuracy. Therefore, this study innovatively introduces use rotation box model to explore its capabilities scenarios occlusion small targets. First, dataset...
<abstract> <p>The study of DNA binding proteins (DBPs) is great importance in the biomedical field and plays a key role this field. At present, many researchers are working on prediction detection DBPs. Traditional DBP mainly uses machine learning methods. Although these methods can obtain relatively high pre-diction accuracy, they consume large quantities human effort material resources. Transfer has certain advantages dealing with such problems. Therefore, present study, two...
G protein-coupled receptors (GPCRs) account for about 40% to 50% of drug targets. Many human diseases are related protein coupled receptors. Accurate prediction GPCR interaction is not only essential understand its structural role, but also helps design more effective drugs. At present, the mainly uses machine learning methods. Machine methods generally require a large number independent and identically distributed samples achieve good results. However, available that have been marked...
The growth state of flowers is affected by many factors such as temperature, humidity, and light. Therefore, the maintenance often requires more professional knowledge. Ordinary people are at a loss when face with various flower representations do not know where problem is. In response to above problems, this article proposes use deep learning identify status assist in successfully raising flowers. article, we propose that mainstream convolutional neural network has limitation only inputting...
In this paper, we study a large number of domestic and international forecasting theories information construction models on multi-source atmospheric particulate monitoring. It also describes the current research progress spatial temporal prediction matter concentration from both measurement point estimation remote sensing monitoring matter. Besides, it introduces environment under traditional stand-alone model cloud-based cloud collaboration model. The based single is upgraded to...