关键词： 随机临床试验   肉毒杆菌   人工智能   偏头痛   机器学习方法   特定疾病   科研工作   偶然性  

摘要： 科学家们开发了一种可以处理大量数据以帮助现有药物找到其新用途的机器学习方法。这项科研工作的目的是为了加快药物再利用的步伐,药物再利用并不稀奇,例如最初被批准用于治疗眼疾的肉毒杆菌注射液,现在被用于治疗偏头痛和美容减皱。但是,要获得这些新用途,通常需要进行偶然性、耗时且昂贵的随机临床试验,以确保被认为对特定疾病有效的药物可用于治疗其他疾病。

关键词： Phasor measurement units  

摘要： As Phasor Measurement Units (PMUs) become widely deployed, power systems can take advantage of the large amount of data provided by PMUs and leverage the advances in big data analytics to improve real-time monitoring and diagnosis. In this paper, we develop practical analytics that are not tightly coupled with the power flow analysis and state estimation, as these tasks require detailed and accurate information about the power system. We focus on power line outage identification, and use a machine learning framework to locate line outages. The same framework is used for the prediction of both single line and multiple line outages. We investigate a range of machine learning algorithms and feature extraction methods. The algorithms are designed to capture the essential dynamic characteristics of the power system when the topology change occurs abruptly. The proposed methods use only voltage phasor angles obtained by continuously monitoring the buses. We tested the proposed methods on their prediction performance under different levels of noise and missingness. It is shown that the proposed methods have better tolerance for noisy data and incomplete data when compared to the previous work that involves solving power flow equations or state estimation equations. © 2020 Elsevier Inc.

关键词： COVID-19   Masked face   Deep transfer learning   Classical machine learning  

摘要： The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy.

关键词： Copy number alteration   gene expression   cancer   machine learning   bioinformatics  

摘要： Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgram level, which are not avaiblable in certain situations like clinical biopsies or single-cell genomes. Here, we developed an alternative method-CNAPE to computationally infer copy number alterations from gene expression data. A prior knowledge-aided machine learning model was proposed, trained and tested on 9,740 cancer samples from The Cancer Genome Atlas. We then applied CNAPE to study gliomas, the most common and aggressive brain cancer in adult. Particularly, using RNA sequencing data, CNAPE respectively predicted DNA copy number of chromosomes, chromosomal arms, and 12 commonly altered genes, and achieved over 80 percent accuracy in almost all broad regions and some focal regions. CNAPE was developed as an easy-to-use tool at https://***/WangLabHKUST/CNAPE.

关键词： 机器学习   流感预测   公共卫生风险   风险预测  

摘要： 【目的】探索应用机器学习预测流感这类公共卫生风险的可行性和有效性。【方法】首先,收集2009-2016年兰州市的流感和气象数据,拆分成2009-2015年和2016年两组,分别作为训练和验证数据;然后,分别基于SARIMA、Kalman Filter和VAR建立三种机器学习预测方法,并设计两种多方法联合预测策略;最后,评估、比较上述方法(策略)的预测性能。【结果】在设定的全期、爆发期和稳定期三种场景下,SARIMA、VAR和Kalman Filter方法的预测效果分别为最佳(RMSE分别为11.68、19.23和1.60;R^(2)分别为0.932、0.923和0.956);多方法联合策略可进一步提升三种场景下的预测效果,其中联合策略Comb_2的表现更好(RMSE分别为10.82、14.68和1.38;R^(2)分别为0.942、0.934和0.963)。【局限】相关数据限制,主要考虑了气象一类外部相关因素。【结论】应用机器学习预测流感等公共卫生风险具有可行性和有效性,且潜力巨大。但目前面临的主要困境是多源数据缺乏,需要从技术、组织和制度层面打破数据壁垒,推动数据共享与开放。

关键词： Leaf area index   leaf/canopy chlorophyll content   radiative transfer model   look-up table   machine learning algorithms  

摘要： Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change ***,we compared a radiative transfer model(RTM)inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content(LCC),canopy chlorophyll content(CCC),and leaf area index(LAI),in a mixed temperate *** accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park,Germany,was evaluated using in situ measurements collected *** RTM inversion using merit function resulted in estimations of LCC(R^(2)=0.26,RMSE=3.9µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.33 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.73 m^(2)/m^(2)),comparable to the estimations based on the machine learning method Random forest regression of LCC(R^(2)=0.34,RMSE=4.06µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.34 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.75 m^(2)/m^(2)).Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit *** performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.

关键词： 机器学习   神经网络   支持向量机   随机森林   矿产勘查  

摘要： 机器学习是目前热门的研究课题,机器学习算法在地学研究中的应用越来越多。其中以神经网络、支持向量机、随机森林等为代表的算法已大量使用在矿产勘查中,这些方法的使用大大提高了矿产勘查数据的处理效率,为矿产勘查数据的处理提供了新的途径和思路。但这些方法的使用中也经常容易出现一些问题,必须熟悉每种方法的原理和特点,才能更好的应用。本文简要介绍了机器学习在矿产勘查中的应用,就几种常用的算法进行了深入介绍;提出了应用机器学习方法处理矿产勘查数据时应该注意避免的几个主要问题。

关键词： 机器学习   合成生物学   生物元件设计   生物网络设计  

摘要： 机器学习的目标是设计可以根据先验知识和观测数据不断改进其性能的算法.该算法可以帮助机器从大量的数据中提取知识,从而提升其在特定任务上的性能.作为数据驱动的方法,机器学习可以有效利用高通量实验技术产生的大批量生物数据,实现合成生物体的功能预测与智能化设计,改变合成生物学的研究范式.本文首先介绍机器学习在合成生物学领域广泛应用的几个模型及方法,如支持向量机、神经网络、生成式对抗网络、深度强化学习等.然后介绍机器学习方法在合成生物学领域的典型应用,如启动子预测、酶催化设计、代谢途径构建、基因线路设计等.本文综述面向合成生物学的机器学习方法及应用,并试图启发读者如何选择和设计机器学习方法用于合成生物学的研究.

关键词： Earth's outer radiation belt   relativistic electron flux   solar wind   prediction   machine learning   artificial neural networks   random forest   gradient boosting  

摘要： The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respectively. The prediction uses an approximation approach based on various machine learning methods, namely, artificial neural networks (ANNs), decision tree (random forest), and gradient boosting. A comparison of the skill scores of short-range forecasts with the lead time of 1 to 24 hours showed that the best results were demonstrated by ANNs. For medium-range forecasting, the accuracy of prediction of the fluences of relativistic electrons in the Earth's outer radiation belt three to four days ahead increases significantly when the predicted values of the solar wind velocity near the Earth obtained from the UV images of the Sun of the AIA (Atmospheric Imaging Assembly) instrument of the SDO (Solar Dynamics Observatory) are included to the list of the input parameters.

关键词： Rock   Electrical conductivity   Machine learning   XGBoost  

摘要： Machine learning (ML) methods are used for predicting the electrical conductivity of brine-saturated rocks. Three relevant ML methods including the extreme gradient boosting regression (XGB), the artificial neural networks (ANN) and the support vector machine regression (SVM) methods are considered and compared. The basic features such as the porosity, electrical conductivity of pore fluid aswell as the normalized cation-exchange capacity of the solid phase are considered for such prediction. A relevant laboratorymeasurement dataset available in literature is considered to train and test the ML models. All the three considered ML methods provide very good predictive capacity on the testing set. But the XGB method ismore robust and extremely fast to train comparing to the ANNmethod and the SVMmethods. A sensitivity analysiswas also realized to clarify the effect of themodel parameters including the number of estimators, the learning rate and the regularization parameters. A set of optimal parameters issue from such analysis is provided for the winning XGB model. Using those optimal parameters, the XGB method predicts an average R-squared score of 0.96 for different random splitting between the training and testing sets. (C) 2020 Elsevier B.V. All rights reserved.