Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated ...Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated in diverse pathological conditions.Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development.However,traditional experimental methods are time-consuming and costly.Although various computational approaches have been proposed,challenges remain in feature learning,predictive accuracy,and generalization.Here,we present m6A-PSRA,a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways.The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity.This ensures that the biological context conveyed by nucleotide order is retained.A bidirectional long short-term memory network(BiLSTM)then processes the encoded matrix,capturing both forward and backward dependencies between bases to resolve contextual correlations.The second branch employs a k-mer tokenization strategy(k=3),decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns.A pre-trained Doc2vec model maps these subsequences into fixeddimensional vectors,reducing feature dimensionality while extracting latent global semantic information via context learning.Both branches integrate residual networks(ResNet)and a self-attention mechanism:ResNet mitigates vanishing gradients through skip connections,preserving feature integrity,while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction.This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans,mice,and rats,m6A-PSRA consistently outperformed existing methods in accuracy(ACC)and area under the curve(AUC),achieving>90%ACC and>95%AUC in every tissue tested,indicating strong cross-species and cross-tissue adaptability.Validation on independent datasets—including three human cell lines(MOLM1,HEK293,A549)and a long-sequence dataset(m6A_IND,1001 nt)—confirmed stable performance across varied biological contexts and sequence lengths.Ablation studies demonstrated that the dual-branch architecture,residual network,and self-attention mechanism each contribute critically to performance,with their combination reducing interference between pathways.Motif analysis revealed an enrichment of m6A sites in guanine(G)and cytosine(C),consistent with known regulatory patterns,supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features,achieving high prediction accuracy and robust generalization across tissues and species,providing an efficient computational tool for m6A methylation site prediction.展开更多
Background:The threat of avian influenza a subtype avian influenza A(H9N2)virus remains a significant concern,necessitating the exploration of novel antiviral agents.This study employs network pharmacology and computa...Background:The threat of avian influenza a subtype avian influenza A(H9N2)virus remains a significant concern,necessitating the exploration of novel antiviral agents.This study employs network pharmacology and computational analysis to investigate the potential of kuwanons,a natural compounds against H9N2 influenza virus.Methods:Leveraging comprehensive databases and bioinformatics tools,we elucidate the molecular mechanisms underlying Kuwanons pharmacological effects against H9N2 influenza virus.Network pharmacology identifies H9N2 influenza virus targets and compounds through integrated protein-protein interaction and Kyoto Encyclopedia of Genes and Genomes analyses.Molecular docking studies were performed to assess the binding affinities and structural interactions of Kuwanon analogues with key targets,shedding light on their potential inhibitory effects on viral replication and entry.Results:Compound-target network analysis revealed complex interactions(120 nodes,163 edges),with significant interactions and an average node degree of 2.72.Kyoto Encyclopedia of Genes and Genomes analysis revealed pathways such as Influenza A,Cytokine-cytokine receptor interaction pathway in H9N2 influenza virus.Molecular docking studies revealed that the binding free energy for the docked ligands ranged between-5.2 and-9.4 kcal/mol for the human interferon-beta crystal structure(IFNB1,Protein Data Bank:1AU1)and-5.4 and-9.6 kcal/mol for Interleukin-6(IL-6,PDB:4CNI).Conclusion:Our findings suggest that kuwanon exhibits promising antiviral activity against H9N2 influenza virus by targeting specific viral proteins,highlighting its potential as a natural therapeutic agent in combating avian influenza infections.展开更多
This paper presents a new method for finding the natural frequency set of a linear time invariant network. In the paper deriving and proving of a common equation are described. It is for the first time that in the co...This paper presents a new method for finding the natural frequency set of a linear time invariant network. In the paper deriving and proving of a common equation are described. It is for the first time that in the common equation the natural frequencies of an n th order network are correlated with the n port parameters. The equation is simple and dual in form and clear in its physical meaning. The procedure of finding the solution is simplified and standardized, and it will not cause the loss of roots. The common equation would find wide use and be systematized.展开更多
基金supported by grants from The National Natural Science Foundation of China(12361104)Yunnan Fundamental Research Projects(202301AT070016,202401AT070036)+2 种基金the Youth Talent Program of Xingdian Talent Support Plan(XDYC-QNRC-2022-0514)the Yunnan Province International Joint Laboratory for Intelligent Integration and Application of Ethnic Multilingualism(202403AP140014)the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University(SMDAYB2023004)。
文摘Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated in diverse pathological conditions.Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development.However,traditional experimental methods are time-consuming and costly.Although various computational approaches have been proposed,challenges remain in feature learning,predictive accuracy,and generalization.Here,we present m6A-PSRA,a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways.The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity.This ensures that the biological context conveyed by nucleotide order is retained.A bidirectional long short-term memory network(BiLSTM)then processes the encoded matrix,capturing both forward and backward dependencies between bases to resolve contextual correlations.The second branch employs a k-mer tokenization strategy(k=3),decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns.A pre-trained Doc2vec model maps these subsequences into fixeddimensional vectors,reducing feature dimensionality while extracting latent global semantic information via context learning.Both branches integrate residual networks(ResNet)and a self-attention mechanism:ResNet mitigates vanishing gradients through skip connections,preserving feature integrity,while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction.This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans,mice,and rats,m6A-PSRA consistently outperformed existing methods in accuracy(ACC)and area under the curve(AUC),achieving>90%ACC and>95%AUC in every tissue tested,indicating strong cross-species and cross-tissue adaptability.Validation on independent datasets—including three human cell lines(MOLM1,HEK293,A549)and a long-sequence dataset(m6A_IND,1001 nt)—confirmed stable performance across varied biological contexts and sequence lengths.Ablation studies demonstrated that the dual-branch architecture,residual network,and self-attention mechanism each contribute critically to performance,with their combination reducing interference between pathways.Motif analysis revealed an enrichment of m6A sites in guanine(G)and cytosine(C),consistent with known regulatory patterns,supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features,achieving high prediction accuracy and robust generalization across tissues and species,providing an efficient computational tool for m6A methylation site prediction.
文摘Background:The threat of avian influenza a subtype avian influenza A(H9N2)virus remains a significant concern,necessitating the exploration of novel antiviral agents.This study employs network pharmacology and computational analysis to investigate the potential of kuwanons,a natural compounds against H9N2 influenza virus.Methods:Leveraging comprehensive databases and bioinformatics tools,we elucidate the molecular mechanisms underlying Kuwanons pharmacological effects against H9N2 influenza virus.Network pharmacology identifies H9N2 influenza virus targets and compounds through integrated protein-protein interaction and Kyoto Encyclopedia of Genes and Genomes analyses.Molecular docking studies were performed to assess the binding affinities and structural interactions of Kuwanon analogues with key targets,shedding light on their potential inhibitory effects on viral replication and entry.Results:Compound-target network analysis revealed complex interactions(120 nodes,163 edges),with significant interactions and an average node degree of 2.72.Kyoto Encyclopedia of Genes and Genomes analysis revealed pathways such as Influenza A,Cytokine-cytokine receptor interaction pathway in H9N2 influenza virus.Molecular docking studies revealed that the binding free energy for the docked ligands ranged between-5.2 and-9.4 kcal/mol for the human interferon-beta crystal structure(IFNB1,Protein Data Bank:1AU1)and-5.4 and-9.6 kcal/mol for Interleukin-6(IL-6,PDB:4CNI).Conclusion:Our findings suggest that kuwanon exhibits promising antiviral activity against H9N2 influenza virus by targeting specific viral proteins,highlighting its potential as a natural therapeutic agent in combating avian influenza infections.
文摘This paper presents a new method for finding the natural frequency set of a linear time invariant network. In the paper deriving and proving of a common equation are described. It is for the first time that in the common equation the natural frequencies of an n th order network are correlated with the n port parameters. The equation is simple and dual in form and clear in its physical meaning. The procedure of finding the solution is simplified and standardized, and it will not cause the loss of roots. The common equation would find wide use and be systematized.
