关键词： QSAR   cytochrome P-450   machine learning   drug safety   drug design   support vector machine   artificial neural network   decision trees   k nearest neighbors   random forest  

摘要： The cytochrome P-450 (GYP) system plays an integral part in the metabolism of drugs and other xenobiotics. Knowledge of the structural features required for interaction with any of the different isoforms of the CYP system is therefore immensely valuable in early drug discovery. In this paper, we focus on three major isoforms (CYP 1 A2, CYP 2D6, and CYP 3A4) and present a data set of 335 structurally diverse drug compounds classified for their interaction (as substrate, inhibitor, or any interaction) with these isoforms. We also present machine learning models using a variety of commonly used methods (k-nearest neighbors, decision tree induction using the CHAID and CRT algorithms, random forests, artificial neural networks, and support vector machines using the radial basis function (RBF) and homogeneous polynomials as kernel functions). We discuss the physicochemical features relevant for each end point and compare it to similar studies. Many of these models perform exceptionally well, even with 10-fold cross-validation, yielding corrected classification rates of 81.7 to 91.9% for CYP 1A2, 89.2 to 92.9% for CYP 2D6, and 87.4 to 89.9% for CYP3A4. Our models help in understanding the structural requirements for CYP interactions and can serve as sensitive tools in virtual screenings and lead optimization for toxicological profiles in drug discovery.

关键词： ARTIFICIAL INTELLIGENCE   NEURAL NETWORKS (COMPUTER)   PHARMACOKINETICS   REMIFENTANIL  

摘要： This study compared the blood concentrations of remifentanil obtained in a previous clinical investigation with the predicted remifentanil concentrations produced by different pharmacokinetic models: a non-linear mixed effects model created by the software NONMEM (R);an artificial neural network (ANN) model;a support vector machine (SVM) model;and multi-method ensembles. The ensemble created from the mean of the ANN and the non-linear mixed effects model predictions achieved the smallest error and the highest correlation coefficient. The SVM model produced the highest error and the lowest correlation coefficient. Paired t-tests indicated that there was insufficient evidence that the predicted values of the ANN, SVM and two multi-method ensembles differed from the actual measured values at alpha = 0.05. The ensemble method combining the ANN and non-linear mixed effects model predictions outperformed either method alone. These results indicated a potential advantage of ensembles in improving the accuracy and reducing the variance of pharmacokinetic models.

关键词： 机器学习方法   生物信息学   Markov模型   支持向量机   学习技术   神经网络   图形模型   科学数据  

摘要： 机器学习技术例如Markov模型、支持向量机、神经网络、图形模型等，已经在生命科学数据的分析研究中广泛应用。本书收录了一些近期的机器学习方法，涵盖了生物信息学研究中的大部分问题。

关键词： Machine learning   Support vector machines   Topo-climatic mapping   Feature selection   Environmental modelling   Downscaling   Decision support systems   Natural hazards  

摘要： Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Fohn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

关键词： 文本分类   文本倾向性分类   图书评价   机器学习方法  

摘要： 介绍了文本分类技术和文本倾向性分类技术,并基于文本倾向性分类技术分析了图书评论中的信息,研究如何将机器学习方法应用在图书评论的倾向性分类中,提出了一种图书评价模型构建的解决方法.

关键词： 胃癌   基因表达谱   标志基因   特征选择  

摘要： 基于机器学习方法寻找和发现新的胃癌亚型分类的相关基因,可以为探讨胃癌发生的分子机制及其基因水平的诊断和治疗提供标志和依据。试验选用33例中国人的胃癌Oligo基因芯片数据,数据包括13例弥漫型胃癌样本2、0例肠型胃癌样本,基因向量为21 378个。采用基因表达差异显著性分析方法(SAM)、偏最小二乘VIP系数法(PLS)和基于巴氏距离的顺序前向搜索方法(BD-SFS)结合的多步骤降维方法,提取到20个能将弥漫型样本和肠型样本有效分开的特征基因。这些特征基因基于支持向量机(SVM),分类准确率可达到89.43%;基于分层聚类分析,准确率可达到93.94%。同时,基因生物学意义的分析结果显示,所选的大部分标志基因对于人类恶性肿瘤的诊断和分型有很重要的意义。

关键词： Stability number   M5 ' model tree   Breakwater armor blocks   Soft computing methods   Data learning  

摘要： Predicting the stability of armor blocks of breakwaters and revetments is a very Important Issue in coastal and ocean engineering. Recently. soft computing tools such as artificial neural networks and fuzzy logic have been used to predict the stability number of armor blocks. However, these tools are not as transparent as empirical formulas. This study presents another Soft computing approach, i.e. model trees for predicting the stability number of armor blocks. The main advantage of model trees is that, unlike the other data learning tools. they are easier to use and more importantly they represent understandable mathematical rules. A total of 579 experimental test data from Van der Meer 1988 are used for developing the model. The conventional governing parameters were selected as the input variables and the obtained results were compared with those of measurements, empirical and soft computing models. Using statistical measures, it was shown that the developed models are more accurate than previous empirical and soft computing models. Furthermore, some simple rules are given for armor blocks' design (C) 2009 Elsevier Ltd. All rights reserved.

关键词： fermentative hydrogen production   optimization algorithm   response surface methodology   pH   nitrogen   cyanobacteria   Synechocystis sp PCC 6803  

摘要： The nitrogen (N) concentration and pH of culture media were optimized for increased fermentative hydrogen (H-2) production from the cyanobacterium, Synechocystis sp. PCC 6803. The optimization was conducted using two procedures, response surface methodology (RSM), which is commonly used, and a memory-based machine learning algorithm, Q(2), which has not been used previously in biotechnology applications. Both RSM and Q2 were successful in predicting optimum conditions that yielded higher H-2 than the media reported by Burrows et al., Int J Hydrogen Energy. 2008;33:6092-6099 optimized for N, S, and C (called EHB-1 media hereafter), which itself yielded almost 150 times more H-2 that? Synechocystis sp. PCC 6803 grown on sulfer-free BG-11 media. RSM predicted an optimum N concentration of 0.63 mM and pH of 7.77, which yielded 1.70 times more H-2 that? EHB-1 media when normalized to chlorophyll concentration (0.68 +/- 0.43 mu mol H-2 mg Chl(-1) h(-1)) and 1.35 times more when normalized to optical density (1.62 +/- 0.09 nmol H-2 OD730-1 h(-1)). Q2 predicted an optimum of 0.36 mM N and pH of 7.88, which yielded 1.94 and 1.27 times more H-2 than EHB-1 media when normalized to chlorophyll concentration (0.77 +/- 0.44 mu mol H-2 mg Chl(-1) h(-1)) and optical density (1.53 +/- 0.07 nmol H-2 OD730-1 h(-1)), respectively. Both optimization methods have unique benefits and drawbacks that are identified and discussed in this study. (D 2009 American Institute of Chemical Engineers Biotechnol. Prog., 25: 1009-1017, 2009

关键词： 人工神经网络   支持向量机   水质遥感   叶绿素a   太湖  

摘要： 为了比较评价人工神经网络和支持向量机2种机器学习算法在水质遥感中的应用能力,本研究首先从基础理论和学习目的入手,对比分析了2种机器学习算法的理论体系;其次,以太湖为例,基于MODIS遥感影像,构建了反演太湖叶绿素a浓度的2种机器学习方法模型,通过对模型的验证、稳定性和鲁棒性分析以及全湖反演结果对比3个方面评价了2种模型的泛化能力.验证结果表明,支持向量机模型对验证样本预测结果的均方差根和平均相对误差分别为5.85和26.5%,而人工神经网络模型的预测结果均方差和平均相对误差则高达13.04和46.8%;稳定性和鲁棒性评价亦说明,以统计学习理论为基础的支持向量机模型具有更加良好的稳定性、鲁棒性,空间泛化能力优于人工神经网络模型;2种机器学习算法对太湖叶绿素a的浓度分布反演结果基本一致,但人工神经网络模型因其学习目标设定和网络构建中的"过学习"等缺陷,造成了对东太湖以及湖心区叶绿素a的反演结果与实际监测结果差异较大.

关键词： 高斯过程   机器学习   水库水温垂直分布   模式识别  

摘要： 利用高斯过程机器学习模型建立水库水温垂直分布结构类型与其复杂影响因素之间的非线性映射关系,提出一种基于高斯过程机器学习的水库水温垂直分布结构类型的模式识别方法。工程实例应用表明,该方法具有模型参数自适应确定、容易实现且识别精度高等优点。