关键词： 联合模型   机器学习   殃残差   Cox-Snell残差  

摘要： 本文运用机器学习方法对纵向数据与生存数据建模,以机器学习方法代替纵向子模型中的线性随机效应模型;生存子模型仍运用Cox比例危险模型。与传统的建模方法做对比,此建模方法的生存子模型残差图诊断符合理论结果,纵向子模型的残差要比线性混合模型分散。

关键词： 大数据   频谱大数据   机器学习   数据挖掘   无线通信   物联网  

摘要： 随着移动互联网与物联网的迅猛发展,个人无线设备的数量呈现指数级增长,随之产生的海量频谱数据与日俱增,频谱大数据的存在已成事实。同时,频谱赤字也日益严峻。为提高频谱利用率,有效的频谱大数据处理显得十分重要。本文从无线通信的角度,首先给出了频谱大数据的定义并分析了它的基本特征;然后总结了一些对于频谱大数据分析与利用颇具前景的机器学习方法,如分布式和并行式学习、极速学习、核学习、深度学习、强化学习、博弈学习和迁移学习;最后给出了几个开放性话题和研究趋势。

关键词： in vitro fertilization   implantation prediction   embryo assessment   machine learning  

摘要： Background. Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. Objective. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Design. Retrospective cohort study. Data Source. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. Methods. For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods. Results. The naive Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively). Conclusions. A machine learning-based decision support system would be useful in improving the success rates of IVF treatment.

关键词： NEPHROTOXICOLOGY   PLURIPOTENT stem cells   MACHINE learning   DRUG side effects   RANDOM forest (Algorithms)  

摘要： The renal proximal tubule is a main target for drug-induced toxicity. The prediction of proximal tubular toxicity during drug development remains difficult. Any in vitro methods based on induced pluripotent stem cell-derived renal cells had not been developed, so far. Here, we developed a rapid 1-step protocol for the differentiation of human induced pluripotent stem cells (hiPSC) into proximal tubular-like cells. These proximal tubular-like cells had a purity of >90% after 8 days of differentiation and could be directly applied for compound screening. The nephrotoxicity prediction performance of the cells was determined by evaluating their responses to 30 compounds. The results were automatically determined using a machine learning algorithm called random forest. In this way, proximal tubular toxicity in humans could be predicted with 99.8% training accuracy and 87.0% test accuracy. Further, we studied the underlying mechanisms of injury and drug-induced cellular pathways in these hiPSC-derived renal cells, and the results were in agreement with human and animal data. Our methods will enable the development of personalized or disease-specific hiPSC-based renal in vitro models for compound screening and nephrotoxicity prediction.

关键词： 在线学习   机器学习   故障预警  

摘要： 通信设备故障预警技术已经不能满足针对通信网络的故障管理需求,以往通过规则库或专家系统实现故障预警系统时规则库或经验很难定义描述,本文在充分研究在线机器学习方法的基础上,提出了基于在线学习算法的通信设备故障预警技术,核心在于针对训练样本进行故障特征提取、训练分类器,然后用训练好的分类器去实时监测故障。

关键词： Machine Learning   Debates  

关键词： ADME   Absorption   Distribution   Metabolism   Excretion   Drug discovery   Machine learning   Molecular descriptors   QSAR  

摘要： In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties. (C) 2015 Published by Elsevier B.V.

关键词： Machine learning   Stress detection   Optical sensors   Data analysis   Plant diseases   Weed detection  

摘要： Effective crop protection requires early and accurate detection of biotic stress. In recent years, remarkable results have been achieved in the early detection of weeds, plant diseases and insect pests in crops. These achievements are related both to the development of non-invasive, high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. Several methods of machine learning have been utilized for precision agriculture such as support vector machines and neural networks for classification (supervised learning);k-means and self-organizing maps for clustering (unsupervised learning). These methods are able to calculate both linear and non-linear models, require few statistical assumptions and adapt flexibly to a wide range of data characteristics. Successful applications include the early detection of plant diseases based on spectral features and weed detection based on shape descriptors with supervised or unsupervised learning methods. This review gives a short introduction into machine learning, analyses its potential for precision crop protection and provides an overview of instructive examples from different fields of precision agriculture.

关键词： MATHEMATICAL models   ESTIMATION theory   DEMAND (Economic theory)   DEMAND functions (Economic theory)   REGRESSION analysis   MACHINE learning   BIG data  

摘要： We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.