MicroRNAs(miRNAs)play a key role in the prevention,diagnosis,and treatment of complex diseases.However,identifying miRNA-disease associations(MDAs)through traditional methods is costly and time-consuming.Recent studie...MicroRNAs(miRNAs)play a key role in the prevention,diagnosis,and treatment of complex diseases.However,identifying miRNA-disease associations(MDAs)through traditional methods is costly and time-consuming.Recent studies have reported numerous validated MDAs,forming the basis for the prediction of new MDAs using computational methods.In this study,we propose SAETNMDA,a computational method that applies fast kernel learning(FKL)and variant triplet networks to predict MDAs.First,miRNA and disease similarities are integrated into two kernels via FKL to enrich biological data.Next,feature representations are obtained by applying stacked autoencoders(SAEs)and triplet networks,enabling the identification of associated pairs by mapping them to nearby locations in the embedding space,while unassociated ones are mapped distantly.Finally,we utilize XGBoost(Extreme Gradient Boosting)to obtain predictive scores for MDAs from these features.SAETNMDA’s performance is evaluated with 5-fold cross-validation(5-fold-CV)and compared with other methods.It achieves the highest AUC and AUPR(0.9419,0.4749 for HMDD v2.0;0.9496,0.5355 for HMDD v3.2,respectively).The performance is also validated on an independent dataset and de novo miRNAs,with SAETNMDA achieving the highest AUC and AUPR in all validations.Case studies also demonstrate the robust predictive capability of our method,with the top 50 predicted miRNAs validated for each of the three diseases.These results highlight SAETNMDA as an efficient model for MDA prediction.SAETNMDA’s source code is available at https://github.com/npxquynhdhsp/SAETNMDA.展开更多
Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wi...Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.展开更多
The facial landmarks can provide valuable information for expression-related tasks.However,most approaches only use landmarks for segmentation preprocessing or directly input them into the neural network for fully con...The facial landmarks can provide valuable information for expression-related tasks.However,most approaches only use landmarks for segmentation preprocessing or directly input them into the neural network for fully connection.Such simple combination not only fails to pass the spatial information to network,but also increases calculation amounts.The method proposed in this paper aims to integrate facial landmarks-driven representation into the triplet network.The spatial information provided by landmarks is introduced into the feature extraction process,so that the model can better capture the location relationship.In addition,coordinate information is also integrated into the triple loss calculation to further enhance similarity prediction.Specifically,for each image,the coordinates of 68 landmarks are detected,and then a region attention map based on these landmarks is generated.For the feature map output by the shallow convolutional layer,it will be multiplied with the attention map to correct the feature activation,so as to strengthen the key region and weaken the unimportant region.Finally,the optimized embedding output can be further used for downstream tasks.Three embeddings of three images output by the network can be regarded as a triplet representation for similarity computation.Through the CK+dataset,the effectiveness of such an optimized feature extraction is verified.After that,it is applied to facial expression similarity tasks.The results on the facial expression comparison(FEC)dataset show that the accuracy rate will be significantly improved after the landmark information is introduced.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant Nos.62473149 and U22A2041the Natural Science Foundation of Hunan Province of China under Grant No.2022JJ30428.
文摘MicroRNAs(miRNAs)play a key role in the prevention,diagnosis,and treatment of complex diseases.However,identifying miRNA-disease associations(MDAs)through traditional methods is costly and time-consuming.Recent studies have reported numerous validated MDAs,forming the basis for the prediction of new MDAs using computational methods.In this study,we propose SAETNMDA,a computational method that applies fast kernel learning(FKL)and variant triplet networks to predict MDAs.First,miRNA and disease similarities are integrated into two kernels via FKL to enrich biological data.Next,feature representations are obtained by applying stacked autoencoders(SAEs)and triplet networks,enabling the identification of associated pairs by mapping them to nearby locations in the embedding space,while unassociated ones are mapped distantly.Finally,we utilize XGBoost(Extreme Gradient Boosting)to obtain predictive scores for MDAs from these features.SAETNMDA’s performance is evaluated with 5-fold cross-validation(5-fold-CV)and compared with other methods.It achieves the highest AUC and AUPR(0.9419,0.4749 for HMDD v2.0;0.9496,0.5355 for HMDD v3.2,respectively).The performance is also validated on an independent dataset and de novo miRNAs,with SAETNMDA achieving the highest AUC and AUPR in all validations.Case studies also demonstrate the robust predictive capability of our method,with the top 50 predicted miRNAs validated for each of the three diseases.These results highlight SAETNMDA as an efficient model for MDA prediction.SAETNMDA’s source code is available at https://github.com/npxquynhdhsp/SAETNMDA.
文摘Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.
文摘The facial landmarks can provide valuable information for expression-related tasks.However,most approaches only use landmarks for segmentation preprocessing or directly input them into the neural network for fully connection.Such simple combination not only fails to pass the spatial information to network,but also increases calculation amounts.The method proposed in this paper aims to integrate facial landmarks-driven representation into the triplet network.The spatial information provided by landmarks is introduced into the feature extraction process,so that the model can better capture the location relationship.In addition,coordinate information is also integrated into the triple loss calculation to further enhance similarity prediction.Specifically,for each image,the coordinates of 68 landmarks are detected,and then a region attention map based on these landmarks is generated.For the feature map output by the shallow convolutional layer,it will be multiplied with the attention map to correct the feature activation,so as to strengthen the key region and weaken the unimportant region.Finally,the optimized embedding output can be further used for downstream tasks.Three embeddings of three images output by the network can be regarded as a triplet representation for similarity computation.Through the CK+dataset,the effectiveness of such an optimized feature extraction is verified.After that,it is applied to facial expression similarity tasks.The results on the facial expression comparison(FEC)dataset show that the accuracy rate will be significantly improved after the landmark information is introduced.
文摘针对高分辨率的遥感影像的语义分割技术,本文提出了一种基于高分辨率上下文提取网络HRCNet(High-Resolution Context Extraction Network)的语义分割模型HRTAN(High-Resolution Triple Attention Network)。与普通的高分辨率语义分割网络相比,HRTAN融合了图卷积网络(GCN,Graph Convolutional Network)技术,同时创造性地增加了三注意力机制,加强了网络对遥感影像中实例边界的感知信息以及对全局信息的获取能力,并融合了上下采样得到的小尺度和大尺度特征信息,以提高模型的分割精度。最后,使用了ISPRS(International Society for Photogrammetry and Remote Sensing)的Potsdam遥感数据集和Vaihingen遥感数据集进行训练、验证以及测试。实验结果证明HRTAN与其他网络相比在遥感影像的语义分割方面具有优越的性能。
文摘特定辐射源识别(Specific emitter identification,SEI)通过分析设备信号硬件特征保障物联网数据安全。现有的深度学习方法在进行特定辐射源识别时,样本数量受限,过于依赖大量已标记样本,无法做到高区分度表征,存在识别性能差的问题。针对这些问题,提出了基于样本插值(Mixup)增强的少样本SEI方法。首先采用Mixup的增强方式来扩展无线电信号样本的数量解决标注样本不足的问题;其次,基于孪生神经网络与复数神经网络(Complex-valued neural networks,CVNN)构建变体三元组网络(Triplet margin network based on CVNN,CVNN-TMN)提高模型的泛化能力和区分度,实现了少样本场景下特定辐射源的精准识别。实验结果表明,与现有多种先进SEI方法对比,在训练集和测试集样本划分比例不同情况下,提出的CVNN-TMN识别精度整体有5%~30%的提升,表明所构建的CVNN-TMN模型在区分度上的优异表现。
