A5100688233- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Computational Drug Discovery Methods
- Genomics and Phylogenetic Studies
- Protein Structure and Dynamics
- MicroRNA in disease regulation
- Bioinformatics and Genomic Networks
- Cancer-related molecular mechanisms research
- Circular RNAs in diseases
- Antimicrobial Peptides and Activities
- Extracellular vesicles in disease
- Machine Learning in Materials Science
- Fractal and DNA sequence analysis
- RNA modifications and cancer
- Gene expression and cancer classification
- Biochemical and Structural Characterization
- Artificial Intelligence in Healthcare
- vaccines and immunoinformatics approaches
- Receptor Mechanisms and Signaling
- Advanced biosensing and bioanalysis techniques
- Pharmacovigilance and Adverse Drug Reactions
- Metabolomics and Mass Spectrometry Studies
- Dialysis and Renal Disease Management
- Body Composition Measurement Techniques
- Biosensors and Analytical Detection
Quzhou University
2015-2025
University of Electronic Science and Technology of China
2013-2025
Ministry of Education of the People's Republic of China
2025
Shandong University
2025
University of International Business and Economics
2025
Jiangsu University
2025
Affiliated Hospital of Jiangsu University
2025
Northwest University
2025
China Geological Survey
2025
Nanjing University of Aeronautics and Astronautics
2025
The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive time-consuming. Recently, deep learning methods have achieved notable performance improvements DTA prediction. However, one challenge for learning-based models appropriate representations drugs targets, especially the lack effective exploration target representations. Another how to comprehensively capture interaction information between different...
De novo drug design is crucial in advancing discovery, which aims to generate new drugs with specific pharmacological properties. Recently, deep generative models have achieved inspiring progress generating drug-like compounds. However, the prioritize a single target generation for intervention, neglecting complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target that simultaneously targets can enhance anti-tumor efficacy...
Protein-protein interactions (PPIs) are central to a lot of biological processes. Many algorithms and methods have been developed predict PPIs protein interaction networks. However, the application most existing is limited since they difficult compute rely on large number homologous proteins marks partners. In this paper, we propose novel sequence-based approach with multivariate mutual information (MMI) feature representation, for predicting via Random Forest (RF).Our method constructs...
Drug-side effect association contains the information on marketed medicines and their recorded adverse drug reactions. Traditional experimental method is time consuming expensive. All associations of drugs side-effects are seen as a bipartite network. Therefore, many computational approaches have been developed to deal with this problem, which used predict new potential associations. However, lots methods did not consider multiple kernel learning (MKL) algorithm, can integrate sources...
Identification of protein-protein interactions (PPIs) is a difficult and important problem in biology. Since experimental methods for predicting PPIs are both expensive time-consuming, many computational have been developed to predict interaction networks, which can be used complement approaches. However, these limitations overcome. They need large number homology proteins or literature applied their method. In this paper, we propose novel matrix-based protein sequence representation...
Relationship of accurate associations between non-coding RNAs and diseases could be great help in the treatment human biomedical research. However, traditional technology is only applied on one type RNA or a specific disease, experimental method time-consuming expensive. More computational tools have been proposed to detect new based known ncRNA disease information. Due ncRNAs (circRNAs, miRNAs lncRNAs) having close relationship with progression various diseases, it critical for developing...
Targeted drugs have been applied to the treatment of cancer on a large scale, and some patients certain therapeutic effects. It is time-consuming task detect drug-target interactions (DTIs) through biochemical experiments. At present, machine learning (ML) has widely in large-scale drug screening. However, there are few methods for multiple information fusion. We propose kernel-based triple collaborative matrix factorization (MK-TCMF) method predict DTIs. The kernel matrices (contain...
Detecting potential associations between drugs and diseases plays an indispensable role in drug development, which has also become a research hotspot recent years. Compared with traditional methods, some computational approaches have the advantages of fast speed low cost, greatly accelerate progress predicting drug-disease association. In this study, we propose novel similarity-based method low-rank matrix decomposition based on multi-graph regularization. On basis factorization L2...
UDP-glycosyltransferases (UGTs) constitute the largest glycosyltransferase family in plant kingdom. They are responsible for transferring sugar moieties onto various small molecules to control many metabolic processes. However, their physiological significance plants is largely unknown. Here, we revealed function and mechanism of two Arabidopsis UGT genes, UGT73C3 UGT73C4, which can be strongly induced by Pseudomonas syringae pv. tomato DC3000. Their overexpression significantly enhanced...