Asymptotic Behavior of Bandwidth Selected by the Cross-Validation Method for Local Polynomial Fitting

Background: Identification of protein subcellular location is an important problem because the subcellular location is highly related to protein function. It is fundamental to determine the locations with biology experiments. However, these experiments are of high costs and time-consuming. The alternative way to address such problem is to design effective computational methods. Objective: To date, several computational methods have been proposed in this regard. However, these methods mainly adopted the features derived from proteins themselves. On the other hand, with the development of network technique, several embedding algorithms have been proposed, which can encode nodes in the network into feature vectors. Such algorithms connected the network and traditional classification algorithms. Thus, they provided a new way to construct models for the prediction of protein subcellular location. Method: In this study, we analyzed features produced by three network embedding algorithms (DeepWalk, Node2vec and Mashup) that were applied on one or multiple protein networks. Obtained features were learned by one machine learning algorithm (support vector machine or random forest) to construct the model. The cross-validation method was adopted to evaluate all constructed models. Results: After evaluating models with the cross-validation method, embedding features yielded by Mashup on multiple networks were quite informative for predicting protein subcellular location. The model based on these features were superior to some classic models. Conclusion: Embedding features yielded by a proper and powerful network embedding algorithm were effective for building the model for prediction of protein subcellular location, providing new pipelines to build more efficient models.

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A simple bootstrap bandwidth selector for local polynomial fitting

Journal of Statistical Computation and Simulation ◽

10.1080/00949650802352019 ◽

2009 ◽

Vol 79 (12) ◽

pp. 1425-1439 ◽

Cited By ~ 5

Author(s):

Yuanhua Feng ◽

Siegfried Heiler

Keyword(s):

Local Polynomial Fitting ◽

Polynomial Fitting ◽

Local Polynomial ◽

Bandwidth Selector

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Estimation of Heat-Attributable Mortality Using the Cross-Validated Best Temperature Metric in Switzerland and South Korea

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18126413 ◽

2021 ◽

Vol 18 (12) ◽

pp. 6413

Author(s):

Jae Young Lee ◽

Martin Röösli ◽

Martina S. Ragettli

Keyword(s):

South Korea ◽

Minimum Temperature ◽

Cross Validation ◽

Specific Model ◽

Apparent Temperature ◽

Maximum Temperature ◽

Attributable Mortality ◽

Validation Method ◽

Attributable Fractions ◽

The Cross

This study presents a novel method for estimating the heat-attributable fractions (HAF) based on the cross-validated best temperature metric. We analyzed the association of eight temperature metrics (mean, maximum, minimum temperature, maximum temperature during daytime, minimum temperature during nighttime, and mean, maximum, and minimum apparent temperature) with mortality and performed the cross-validation method to select the best model in selected cities of Switzerland and South Korea from May to September of 1995–2015. It was observed that HAF estimated using different metrics varied by 2.69–4.09% in eight cities of Switzerland and by 0.61–0.90% in six cities of South Korea. Based on the cross-validation method, mean temperature was estimated to be the best metric, and it revealed that the HAF of Switzerland and South Korea were 3.29% and 0.72%, respectively. Furthermore, estimates of HAF were improved by selecting the best city-specific model for each city, that is, 3.34% for Switzerland and 0.78% for South Korea. To the best of our knowledge, this study is the first to observe the uncertainty of HAF estimation originated from the selection of temperature metric and to present the HAF estimation based on the cross-validation method.

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