关键词： 机器学习算法   地震数据   研究人员   学习工具   应用   哥伦比亚大学   机器学习方法   地震记录  

摘要： 哥伦比亚大学的一项新研究发现,机器学习算法可以从世界上最古老和最大的地热储层之一的加州盖瑟斯三年的地震记录中挑选出不同类型的地震。地震的重复模式似乎与注水流入下层热岩的季节性上升和下降相吻合,这表明地震与引起岩石滑动或破裂的机械过程相关联。＂这是研究地震的全新方式,＂研究人员指出：＂这些机器学习方法从原始数据中挑出了非常细微的差异,我们正在尝试去解释。＂

关键词： Inhomogeneous media   Scattering   Artificial neural network   Support vector machine   Fuzzy logic  

摘要： Encountering with a nonlinear second-order differential equation including epsilon(r) and mu(t), spatial distributions, while computing the fields inside inhomogeneous media, persuaded us to find their known distributions that give exact solutions. Similarities between random distributions of electric properties and known functions lead us to estimate them using three mathematical tools of artificial neural networks (ANNs), support vector machines (SVMs) and Fuzzy Logic (FL). Assigning known functions after fitting with minimum error to arbitrary inputs using results of machine learning networks leads to achieve an approximate solution for the field inside materials considering boundary conditions. A comparative study between the methods according to the complexity of the structures as well as the accuracy and the calculation time for testing of unforeseen inputs, including classification, prediction and regression is presented. We examined the extracted pairs of epsilon(r) and mu(t), with ANN, SVM networks and FL and got satisfactory outputs with detailed results. The application of the presented method in zero reflection subjects is exemplified.

关键词： Redox reactions   Chemical reactions   Peptides and proteins   Hydrolysis   Machine learning  

摘要： Drug metabolism is a complex procedure in the human body, including a series of enzymatically catalyzed reactions. However, it is costly and time consuming to investigate drug metabolism experimentally;computational methods are hence developed to predict drug metabolism and have shown great advantages. As the first step, classification of metabolic reactions and enzymes is highly desirable for drug metabolism prediction. In this study, we developed multi classification models for prediction of reaction types catalyzed by oxidoreductases and hydrolases, in which three reaction fingerprints were used to describe the reactions and seven machine learnings algorithms were employed for model building. Data retrieved from KEGG containing 1055 hydrolysis and 2510 redox reactions were used to build the models, respectively. The external validation data consisted of 213 hydrolysis and 512 redox reactions extracted from the Rhea database. The best models were built by neural network or logistic regression with a 2048-bit transformation reaction fingerprint. The predictive accuracies of the main class, subclass, and superclass classification models on external validation sets were all above 90%. This study will be very helpful for enzymatic reaction annotation and further study on metabolism prediction.

关键词： Machine learning   natural language processing   patient-reported symptoms   electronic health record   breast cancer   palliative care  

摘要： Context. Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped. Objectives. To create machine learning algorithms capable of extracting patient-reported symptoms from free-text electronic health record notes. Methods. The data set included 103,564 sentences obtained from the electronic clinical notes of 2695 breast cancer patients receiving paclitaxel-containing chemotherapy at two academic cancer centers between May 1996 and May 2015. We manually annotated 10,000 sentences and trained a conditional random field model to predict words indicating an active symptom (positive label), absence of a symptom (negative label), or no symptom at all (neutral label). Sentences labeled by human coder were divided into training, validation, and test data sets. Final model performance was determined on 20% test data unused in model development or tuning. Results. The final model achieved precision of 0.82, 0.86, and 0.99 and recall of 0.56, 0.69, and 1.00 for positive, negative, and neutral symptom labels, respectively. The most common positive symptoms were pain, fatigue, and nausea. Machine-based labeling of 103,564 sentences took two minutes. Conclusion. We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications. (C) 2018 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

关键词： 上位性   统计类方法   机器学习方法   信息熵方法   两阶段分析  

摘要： 随着现代经济和社会的发展,科学技术日新月异,更新的速度十分快。从基因分型技术的发展情况来看,将其应用到复杂疾病的研究上已不再是难题。现阶段相关研究人员以影响复杂疾病的重要遗传因素之一,即上位性为依据,展开了相关方面的探讨与分析。笔者以此为切入点,在原有的研究方法的基础上针对上位性检测方法进行了详细的研究。

关键词： 网络舆情   k-means聚类   多项logistc   判别分析   舆情预测  

摘要： [目的/意义]研究大数据环境下网络舆情预测模型,依据网络舆情的事件类型特征,探求不同类型事件的舆情特征,从而明晰网络舆情事件类型与舆情特征类型之间的关系,为网络舆情的风险防范提供理论基础和实践指导。[方法/过程]基于案例构建网络舆情多维指标体系,识别可观测的舆情事件分类因素,进而进行多维聚类来划分舆情事件类型,同时运用多项logistic回归分析,探求网络舆情事件类型与舆情特征类型之间的关系。[结果/结论]根据"事初防变"的风险管理理念,提供的多维动态分类与预测模型可以在舆情潜伏期依据其事件类型预测其舆情特征,为进一步研究提供借鉴。

关键词： day-ahead forecasting   extreme learning machines (ELMs)   feature selection   machine learning   support vector regression (SVR)   wind power market  

摘要： In liberalized markets, there usually exists a day-ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day-ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1-day-ahead forecast. The input-output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012-2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play.

关键词： Viruses  

摘要： Medicinal Wants are reported to possess antiviral activity, but finding the substances that are responsible for antiviral activity in the complex mixture of the plant extract is an extremely difficult task. In this paper a methodology related to the determination of the antiviral properties of medicinal plant extracts and based on phytochemical analysis, antiviral tests and machine [earning methods is described. 16 potentially antiviral medicinal Wants were selected, and their chemometric characteristics and antiviral properties were investigated. Three different analytical methods were used for chemical analysis: (i) spectrophotometry, (ii) capillary electrophoresis with contactess conductivity detection, and (iii) gas chromatography-mass spectrometry. 14 attributes were obtained describing the composition of the plant extracts. Viral growth inhibition properties were investigated and 8 candidate plant extracts were selected as being active against viruses. Infectious bronchitis virus was used as a mode[virus. Machine [earning techniques including deep neural network classification, classification and regression tree induction and hierarchical clusterization were used for mining the factors that are responsible for antiviral effects. It was determined that (i) phenolic compounds providing high radical scavenging activity and fractions containing high content of phenolic compounds are positively related to antiviral activity in plant extracts, (ii) hydrophilic compounds that are positively charged (pK(a) > 4.7) in acidic media and possess medium and Low electrophoretic mobility properties are negatively related to antiviral activity in medicinal Wants, (iii) phenolic acids with pK(a) Lower than 4.7 are not related to antiviral activity in the extracts, and (iv) volatile compounds in the extracts, including diversity, quantity and different volatility properties, do not affect the antiviral activity of plant extracts. Following the proposed methodological approach, i

关键词： Dengue   Modeling techniques   Weather   Relative humidity   Climate   Metropolitan Manila  

摘要： Background: Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting. Methods: Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 - December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis;observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated. Results: Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model. Conclusion: The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random for

摘要： All models of evolution of human behaviour depend on the correct identification and interpretation of bone surface modifications (BSM) on archaeofaunal assemblages. Crucial evolutionary features, such as the origin of stone tool use, meat-eating, food-sharing, cooperation and sociality can only be addressed through confident identification and interpretation of BSM, and more specifically, cut marks. Recently, it has been argued that linear marks with the same properties as cut marks can be created by crocodiles, thereby questioning whether secure cut mark identifications can be made in the Early Pleistocene fossil record. Powerful classification methods based on multivariate statistics and machine learning (ML) algorithms have previously successfully discriminated cut marks from most other potentially confounding BSM. However, crocodile-made marks were marginal to or played no role in these comparative analyses. Here, for the first time, we apply state-of-the-art ML methods on crocodile linear BSM and experimental butchery cut marks, showing that the combination of multivariate taphonomy and ML methods provides accurate identification of BSM, including cut and crocodile bite marks. This enables empirically-supported hominin behavioural modelling, provided that these methods are applied to fossil assemblages.