Yong-Jian Guan
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View article: LLM-Driven Adaptive Prompt Optimization Framework for ADS-B Anomaly Detection
LLM-Driven Adaptive Prompt Optimization Framework for ADS-B Anomaly Detection Open
The Automatic Dependent Surveillance-Broadcast (ADS-B) is a key component of the new-generation air traffic surveillance system. However, it is vulnerable to security threats due to its plaintext transmission and lack of authentication mec…
View article: MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules
MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules Open
According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-targe…
View article: MHESMMR: A multilevel model for Predicting the Regulation of miRNAs Expression by Small Molecules
MHESMMR: A multilevel model for Predicting the Regulation of miRNAs Expression by Small Molecules Open
According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-targe…
View article: LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model
LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model Open
LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA–protein interactions are expensive and time-consuming, considering that there ar…
View article: MFIDMA: A Multiple Information Integration Model for the Prediction of Drug–miRNA Associations
MFIDMA: A Multiple Information Integration Model for the Prediction of Drug–miRNA Associations Open
Abnormal microRNA (miRNA) functions play significant roles in various pathological processes. Thus, predicting drug–miRNA associations (DMA) may hold great promise for identifying the potential targets of drugs. However, discovering the as…
View article: SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes
SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes Open
Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computa…
View article: KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning
KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning Open
Emerging evidence has revealed that circular RNA (circRNA) is widely distributed in mammalian cells and functions as microRNA (miRNA) sponges involved in transcriptional and posttranscriptional regulation of gene expression. Recognizing th…
View article: BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network
BNEMDI: A Novel MicroRNA–Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network Open
As a novel target in pharmacy, microRNA (miRNA) can regulate gene expression under specific disease conditions to produce specific proteins. To date, many researchers leveraged miRNA to reveal drug efficacy and pathogenesis at the molecula…
View article: BioChemDDI: Predicting Drug–Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism
BioChemDDI: Predicting Drug–Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism Open
During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other’s mechanisms of action, correctly identifying potential drug–drug interactions (…
View article: BioDKG–DDI: predicting drug–drug interactions based on drug knowledge graph fusing biochemical information
BioDKG–DDI: predicting drug–drug interactions based on drug knowledge graph fusing biochemical information Open
The way of co-administration of drugs is a sensible strategy for treating complex diseases efficiently. Because of existing massive unknown interactions among drugs, predicting potential adverse drug–drug interactions (DDIs) accurately is …
View article: SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information
SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information Open
Non-coding RNAs (ncRNAs) take essential effects on biological processes, like gene regulation. One critical way of ncRNA executing biological functions is interactions between ncRNA and RNA binding proteins (RBPs). Identifying proteins, in…
View article: Prediction of Protein–Protein Interactions in Arabidopsis, Maize, and Rice by Combining Deep Neural Network With Discrete Hilbert Transform
Prediction of Protein–Protein Interactions in Arabidopsis, Maize, and Rice by Combining Deep Neural Network With Discrete Hilbert Transform Open
Protein–protein interactions (PPIs) in plants play an essential role in the regulation of biological processes. However, traditional experimental methods are expensive, time-consuming, and need sophisticated technical equipment. These draw…
View article: Sequence-Based Prediction of Plant Protein-Protein Interactions by Combining Discrete Sine Transformation With Rotation Forest
Sequence-Based Prediction of Plant Protein-Protein Interactions by Combining Discrete Sine Transformation With Rotation Forest Open
Protein-protein interactions (PPIs) in plants are essential for understanding the regulation of biological processes. Although high-throughput technologies have been widely used to identify PPIs, they are usually laborious, expensive, and …