CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computationa...
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CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.
Considering the recent developments in the digital environment,ensuring a higher level of security for networking systems is *** security approaches are being constantly developed to protect against evolving *** ensem...
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Considering the recent developments in the digital environment,ensuring a higher level of security for networking systems is *** security approaches are being constantly developed to protect against evolving *** ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior *** research work aimed to create a more diverse and effective ensemble *** this end,selected six classification models,Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbor(KNN),Decision Tree(DT),Support Vector Machine(SVM),and Random Forest(RF)from existing study to run as independent *** the individual models were trained,a Correlation-Based Diversity Matrix(CDM)was created by determining their *** models for the ensemble were chosen by the proposed Modified Minimization Approach for Model Subset Selection(Modified-MMS)from Lower triangular-CDM(L-CDM)as *** proposed algorithm performance was assessed using the Network Security Laboratory—Knowledge Discovery in Databases(NSL-KDD)dataset,and several performance metrics,including accuracy,precision,recall,and *** selecting a diverse set of models,the proposed system enhances the performance of an ensemble by reducing overfitting and increasing prediction *** proposed work achieved an impressive accuracy of 99.26%,using only two classification models in an ensemble,which surpasses the performance of a larger ensemble that employs six classification models.
Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of ...
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Due to the significant increase in data transmission speed and gradual increase in Doppler frequency shift, channel estimation accuracy has become one of the most prioritized considerations in many cases. Specifically...
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Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of th...
Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.
To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the d...
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To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.
the basic concept of multicast was elaborated. Compared with unicast and multicast, multicast has the advantages of high transmission efficiency and low link load. An experimental multicast network was constructed bas...
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Detections of Ginkgoes are prerequisites for later counting and harvesting. Due to the uneven distribution of samples, the detection speed and accuracy of existing algorithms cannot adapt to the impact of complex envi...
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We study Voronoi games on temporal graphs as introduced by Boehmer et al. (IJCAI ’21) where two players each select a vertex in a temporal graph with the goal of reaching the other vertices earlier than the other pla...
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Plant diseases significantly threaten global food security and economic stability by reducing crop yields, increasing production costs, and exacerbating food shortages. Early and precise detection of plant diseases is...
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