关键词： Forecasting  

摘要： Deep learning is currently the most successful machine learning technique in a wide range of application areas and has recently been applied successfully in drug discovery research to predict potential drug targets and to screen for active molecules. However, due to (1) the lack of large-scale studies, (2) the compound series bias that is characteristic of drug discovery datasets and (3) the hyperparameter selection bias that comes with the high number of potential deep learning architectures, it remains unclear whether deep learning can indeed outperform existing computational methods in drug discovery tasks. We therefore assessed the performance of several deep learning methods on a large-scale drug discovery dataset and compared the results with those of other machine learning and target prediction methods. To avoid potential biases from hyperparameter selection or compound series, we used a nested cluster-cross-validation strategy. We found (1) that deep learning methods significantly outperform all competing methods and (2) that the predictive performance of deep learning is in many cases comparable to that of tests performed in wet labs (i.e., in vitro assays).

关键词： decision block   Small angle X ray scattering   macromolecules   inverse Fourier transform   Particle shape   Molecular weights   Machine Learning   Data analysis   Data sources   X ray scattering  

摘要： Small-angle x-ray scattering (SAXS) of biological macromolecules in solutions is a widely employed method in structural biology. SAXS patterns include information about the overall shape and low-resolution structure of dissolved particles. Here, we describe how to transform experimental SAXS patterns to feature vectors and how a simple k-nearest neighbor approach is able to retrieve information on overall particle shape and maximal diameter (D-max) as well as molecular mass directly from experimental scattering data. Based on this transformation, we develop a rapid multiclass shape-classification ranging from compact, extended, and flat categories to hollow and random-chain-like objects. This classification may be employed, e.g., as a decision block in automated data analysis pipelines. Further, we map protein structures from the Protein Data Bank into the classification space and, in a second step, use this mapping as a data source to obtain accurate estimates for the structural parameters (D-max,D- molecular mass) of the macromolecule under study based on the experimental scattering pattern alone, without inverse Fourier transform for D-max. All methods presented are implemented in a Fortran binary DATCLASS, part of the ATSAS data analysis suite, available on Linux, Mac, and Windows and free for academic use.

关键词： 星系形成   人脸识别   机器学习方法   哈勃空间望远镜   图像识别   天文学家   计算机模拟   学习算法  

摘要： 被叫作深度学习的机器学习方法被广泛应用在人脸识别和其他图像识别应用中，显示出其在星系天文研究领域的应用潜力。天文学家认为它可以帮助识别星系图片，解答星系形成和演化的诸多问题。在最新的一项研究中，科研人员使用计算机模拟星系形成来训练一种深度学习算法，然后用它识别哈勃空间望远镜拍摄到的星系图像，得到十分理想的结果。

关键词： 企业风险管理   ERM新框架   持续审计   智能持续审计   机器学习  

摘要： 本文基于COSO委员会2016年发布的企业风险管理新框架,构建了运用机器学习算法的嵌入式智能持续审计系统框架,并以W市国家电网公司的数据为例,借用Python软件,通过模拟仿真测试,阐释了嵌入式智能持续审计系统的基本算法、运行流程及实施效果。

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。