Associating genes with diseases is a fundamental challenge in human health with applications of understanding disease properties and developing precision medicine. Over the past decades, biomedical articles increase e...
ISBN:
(数字)9781728123486
ISBN:
(纸本)9781728123493
Associating genes with diseases is a fundamental challenge in human health with applications of understanding disease properties and developing precision medicine. Over the past decades, biomedical articles increase explosively, which contain a great number of gene-disease associations (GDAs). Association extraction requires annotated corpus of high accuracy, but manual labeling is time consuming and labor intensive. This paper proposes a distant supervision-based method, to automatically label corpus for GDAs extraction. Compared with the manually annotated gold corpus, the automatic labeled corpus has much larger scale and better quality. It improves the performance of state-of-the-art extraction models, with AUC of 0.96, and F1 of 90%. To the best of our knowledge, this is the first study of automatic labeling GDAs in the field of precision medicine. We extracted GDAs using new corpora from 115,261 PubMed abstracts about 29 lung cancers, and finally discovered 296 new genes/proteins related to lung cancers. These findings indicate new directions for drug design.
OD flows provide important information for traffic management and planning. The prediction of dynamic OD matrices gives the possibility to apply anticipatory traffic management measures. In this paper, we propose an O...
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OD flows provide important information for traffic management and planning. The prediction of dynamic OD matrices gives the possibility to apply anticipatory traffic management measures. In this paper, we propose an OD prediction approach based on the data obtained by Automated Number Plate Recognition (ANPR) cameras. The principal component analysis (PCA) is applied to reduce the dimension of the original OD matrices and to separate the main structure patterns from the noisier components. A state-space model is established for the main structure patterns and the structure deviations, and is incorporated in the Kalman filter framework to make predictions. We further propose three K-Nearest Neighbour (K-NN) based long-term pattern recognition approaches. The proposed approaches are validated with field ANPR data from Changsha city, P.R. China. The results show that the observed OD flows can be accurately predicted by our proposed approaches. Which prediction method performs best depends on the quality of the available data: for regular, periodic OD matrices the Kalman filter is better, for irregular OD matrices the pattern recognition that looks at different time periods in the historical data, gives better results.
The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expr...
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In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribut...
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The rising of bigdata and the Internet has brought about tremendous changes in travel. The rapid development of computer technology, network technology, wireless communication technology, portable devices, and locati...
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The rising of bigdata and the Internet has brought about tremendous changes in travel. The rapid development of computer technology, network technology, wireless communication technology, portable devices, and location-based services provides an opportunity for the application of GPS technology to travel behavior survey. GPS technology has become a new technology to study urban residents’ travel behavior and to identify urban residents’ travel modes. This paper delivery a travel mode recognition method for urban residents’ GPS travel data. Through the process of GPS data preprocessing, trajectory recognition and feature extraction, the recognition algorithm is designed to identify seven urban common travel modes, which are walking, bicycle, car, bus, taxi, subway and urban rail. In this paper, a trajectory recognition algorithm based on transition points is used to segment the trajectory of a single travel mode by identifying the transition points and pedestrian sections. The accuracy of the trajectory recognition process is about 79.8% by validating the open data set. For the extracted single trajectory, the Bagged Trees combined model is used to identify the travel mode with an accuracy of about 76.2%.
The problem of vehicle delay is one of the higher complaint rates among the passenger opinions of bus companies, and it is also one of the important reasons why citizens give up choosing public transportation. Estimat...
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ISBN:
(数字)9781728182780
ISBN:
(纸本)9781728182797
The problem of vehicle delay is one of the higher complaint rates among the passenger opinions of bus companies, and it is also one of the important reasons why citizens give up choosing public transportation. Estimating bus arrival time is of great significance for improving passenger satisfaction and improving the problem of urban traffic congestion. In this paper, the vehicle GPS data were used to classify the data according to different scenarios, and then the BP neural network was trained and compared with the artificial colony optimization BP neural network. The comparison results show that artificial bee colony optimization BP neural network algorithm is superior to BP neural network algorithm, so we select the better artificial bee colony optimization BP neural network algorithm to establish a vehicle arrival time prediction model.
The time resolution of the existing traffic flow prediction model is too big to be applied to adaptive signal timing *** on the view of the platoon dispersion model,the relationship between vehicle arrival at the down...
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The time resolution of the existing traffic flow prediction model is too big to be applied to adaptive signal timing *** on the view of the platoon dispersion model,the relationship between vehicle arrival at the downstream intersection and vehicle departure from the upstream intersection was ***,a high-resolution traffic flow prediction model based on deep learning was *** departure flow rate from the upstream and the arrival flow rate at the downstream intersection was taking as the input and output in the proposed model,***,the parameters of the proposed model were trained by the field data,and the proposed model was implemented to forecast the arrival flow rate of the downstream *** show that the proposed model can better capture the fluctuant traffic flow and reduced MAE,MRE,and RMSE by 9.53%,39.92%,and 3.56%,respectively,compared with traditional models and algorithms,such as Robertson's model and artificial neural ***,the proposed model can be applied for realtime adaptive signal timing optimization.
Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evid...
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