Abstract: In the process of drinking water preparation, the amount of coagulant added directly affects the quality of water and drinking safety. However, the coagulation process is influenced by many environmental fac...
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Automatic identification of adventitious respiratory sound has still been a challenging problem in recent years. To address this challenge, we propose an adventitious respiratory sound classification network (ARSC-Net...
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ISBN:
(纸本)9781665429825
Automatic identification of adventitious respiratory sound has still been a challenging problem in recent years. To address this challenge, we propose an adventitious respiratory sound classification network (ARSC-Net), which combines residual block with channel-spatial attention for accurate classification. Specifically, we extract two types of features from adventitious respiratory sound, including Mel-Frequency Cepstral Coefficients (MFCCs) and Mel-spectrogram. The two types of features are entered into the parallel encoders paths with residual attention for extracting feature representation, and then fused into a channel-spatial attention module to adaptively focus on the important features between channel and spatial part for the classification task. Moreover, the channel-spatial attention can enhance the feature representation, in which the channel attention explores the inter-channel relationship of the spectrums, and then the inter-spatial correlation mapping is generated by the spatial attention introduced serially. We evaluate our proposed method on ICBHI 2017 database. Experimental results show that our proposed method achieves encouraging predictive performance with an accuracy of 80.0% for identifying abnormal sounds from normal sounds, and with an accuracy of 92.4% for distinguishing crackles from wheezes. In addition, our method also achieves a score of 56.76% for the four-class sound classification of adventitious sounds and outperforms several state-of-the-art methods.
Mild Cognitive Impairment (MCI) detection is paramount in Alzheimer’s disease (AD) prevention. Existing methods have not sufficiently leveraged both subject correlation and brain region topological information into M...
Mild Cognitive Impairment (MCI) detection is paramount in Alzheimer’s disease (AD) prevention. Existing methods have not sufficiently leveraged both subject correlation and brain region topological information into MCI detection. To solve the limitation, this study proposes a hybrid region and population hypergraph neural network for MCI detection. Specifically, we use two-view selection on both signals and brain regions, and an improved metric learning-based module to improve the quality of hypergraphs. Additionally, a population-to-region hypergraph neural network is designed to leverage information from brain region topology and subject correlation for MCI detection. Experimental results show that our proposed method achieves accuracies of 87.98%, 81.05%, 86.42% and 82.95% for MCI detection, early MCI detection, late MCI detection and MCI classification on the AD Neuroimaging Initiative dataset, respectively, outperforming several state-of-the-art methods. Furthermore, experimental results on the Autism Brain Imaging Data Exchange dataset validating its generalization to other brain disorder detection tasks.
The local search methods have been widely used to solve the clustering problems. In practice, local search algorithms for clustering problems mainly adapt the singleswap strategy, which enables them to handle large-sc...
The local search methods have been widely used to solve the clustering problems. In practice, local search algorithms for clustering problems mainly adapt the singleswap strategy, which enables them to handle large-scale datasets and achieve linear running time in the data size. However, compared with multi-swap local search algorithms, there is a considerable gap on the approximation ratios of the single-swap local search algorithms. Although the current multi-swap local search algorithms provide small constant approximation, the proposed algorithms tend to have large polynomial running time, which cannot be used to handle large-scale datasets. In this paper, we propose a multi-swap local search algorithm for the k-means problem with linear running time in the data size. Given a swap size t, our proposed algorithm can achieve a (50(1 + 1/t) + ε)-approximation, which improves the current best result 509 (ICML 2019) with linear running time in the data size. Our proposed method, compared with previous multi-swap local search algorithms, is the first one to achieve linear running time in the data size. To obtain a more practical algorithm for the problem with better clustering quality and running time, we propose a sampling-based method which accelerates the process of clustering cost update during swaps. Besides, a recombination mechanism is proposed to find potentially better solutions. Empirical experiments show that our proposed algorithms achieve better performances compared with branch and bound solver (NeurIPS 2022) and other existing state-of-the-art local search algorithms on both small and large datasets.
Background:The direct-to-consumer genetic testing(DTC-GT)industry has exploded in recent years,initiated by market pioneers from the United States and quickly followed by companies from Europe and *** addition to thei...
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Background:The direct-to-consumer genetic testing(DTC-GT)industry has exploded in recent years,initiated by market pioneers from the United States and quickly followed by companies from Europe and *** addition to their primary objective of providing ancestry and health information to customers,DTC-GT services have emerged as a valuable data resource for large-scale population and genetics ***:We assessed DTC-GT market leaders in the *** China,user participation in research,and academic reports based on this *** also investigated DTC-GT end-user value by tracing key updates of companies provided via health risk reports and evaluating their predictive *** then assessed the replicability of several genome-wide association studies(GWAS)based on a Chinese DTC-GT ***:As recent entrants to the market,Chinese DTC-GT serv ice providers have published less academic research than their Western counterparts;however,a larger proportion of Chinese users consent to participate in research *** increases in user volume and resultant report updates led to reclassification of some users'polygenic risk levels,but within a reasonable scale and with increased predictive *** among GWAS using the Chinese DTC-GT biobank varied by studied trait,population background,and sample ***:We speculate that the rapid growth in DTC-GT services,particularly in non-Caucasian populations,will yield an important and much-needed resource for biobanking,large-scale genetic studies,clinical trials,and post-clinical applications.
Business process management is the end-to-end business process modeling, analysis, and optimization to achieve business goals. Business Process Modeling Notation (BPMN) is usually used to represent business process mo...
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Business process management is the end-to-end business process modeling, analysis, and optimization to achieve business goals. Business Process Modeling Notation (BPMN) is usually used to represent business process models. However, BPMN cannot support the intuitive modeling for some typical tasks in complex business scenarios of the Industrial Internet context, increasing the communication cost between modelers and business personnel. The current solutions try to extend the attributes of BPMN elements with annotations, but a large number of annotations makes business process models complex and cumbersome. This paper proposes to extend new business process modeling elements for Industrial Internet application scenarios based on BPMN extension mechanism, generating an extended business process modeling language, namely BPMN++. And an extended process modeling tool is implemented, which is available online. A case study over a real-world Industrial Internet process validates the usefulness of this extension mechanism and the well understandability of business process models.
Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is...
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Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal int...
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Alternative splicing has a significant impact in the process of gene regulation and it greatly raises the complexity of protein species and functions. At present, the gene function has been extensively studied, and th...
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ISBN:
(纸本)9781665429825
Alternative splicing has a significant impact in the process of gene regulation and it greatly raises the complexity of protein species and functions. At present, the gene function has been extensively studied, and there are multiple public databases of gene function annotations. Due to the high cost and the large number of isoforms, it is currently impossible to conduct extensive experimental analysis on isoform functions. Functions at the level of splicing isoforms remain largely unknown. Computational prediction provides an alternative to functional annotation of splice isoforms. Here we present GraphIsoFun, a novel computational approach to predict isoform function by integrating both gene and isoform-level expression information with graph neural network. It first constructs a heterogeneous co-expression network with both genes and isoforms being nodes. This network integrates the rich association between isoforms and genes, and provides a bridge for connecting isoform and gene functions. Then GraphIsoFun uses the graph neural network to mine the information of the co-expression network and obtains the final prediction score of isoforms for each function. The experiments on multiple datasets show that GraphIsoFun surpasses existing methods based on different evaluation indicators.
Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (...
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ISBN:
(纸本)9781665429825
Accurate prediction of essential proteins by using computational methods can effectively reduce the cost of wet-lab experiments. Existing computational methods usually rely on constructed protein-protein interaction (PPI) networks with different kinds of biological data. However, high-quality PPI networks and other biological data are not available for all proteins. Thus, it is very necessary and valuable to develop accurate methods for fast and effective prediction of essential proteins by using only protein sequences. We propose EPGBDT, a machine learning ensemble model, to improve the performance of essential protein prediction by using only protein sequences. EP-GBDT has an ensemble structure that combines multiple Gradient Boosting Decision Tree (GBDT) base classifiers. In addition, to reduce the effects of imbalanced dataset, EP-GBDT uses a sampling technique. The results show that EP-GBDT outperforms state-of-the-art sequence-based methods and network-based centrality measures. The source code and datasets can be downloaded from https://***/CSUBioGroup/EP-GBDT.
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