关键词： 中药化学成分   机器学习方法   靶点识别  

摘要： 与有效成分相作用靶点的识别是从分子水平阐明中药作用机理的关键步骤。本文提出1种新的思路来解决中药化学成分的靶点识别问题。该方法基于小分子化合物的结构,计算表征分子组成、电荷分布、拓扑、几何结构及物理化学性质的分子描述符,经BestFirst搜索策略和CfsSubsetEval评估策略相结合的方法筛选出与靶点作用活性相关的分子描述符。采用径向基神经网络、朴素贝叶斯和随机森林3种机器学习方法构建一系列靶点的识别模型,后期将所建模型整合成靶点识别系统,进而预测中药有效成分的作用靶点。采用10折交叉验证,3种方法得到总的预测正确率分别为83.33%～95.71%、84.62%～96.43%、82.14%～95.59%,识别过程在(0.02～0.19)秒完成。实验结果证明该方法不但简单有效,更主要的是满足面向中药化学成分的靶点识别任务对辨识效率的要求。

关键词： 情绪监控   情绪监控预警模型   机器学习方法   预警指标  

摘要： 突发事件所引发的群体情绪可能会导致衍生群体事件的发生,所以有必要对群体情绪演化进行监控预警。本文对突发事件发生、发展过程中群体情绪的演化进行了研究,建立了面向突发事件的情绪层次模型以及情绪监控预警模型。通过支持向量机、贝叶斯文本分类算法、n元语言模型三种机器学习算法,以及CH I(卡方检验)、DF(文档频率)、IG(信息增益)三种特征选择方法对突发事件相关的微博评论进行了情绪分类的实验分析,实验结果表明支持向量机、IG取得的分类性能最优,得到的分类结果可以满足实际应用的需求。进而对王家岭矿难、山西疫苗两个突发事件进行了实际案例研究,建立并分析了情绪层次演化曲线。最后提出了群体情绪演化预警指标以及预警模式的概念。

关键词： Schizophrenia   Clozapine   EEG   Treatment-efficacy prediction   Machine learning   Psychiatry  

摘要： Objective: To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia. Methods: Pre-treatment EEG data are collected in 23 + 14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results. First, a feature selection scheme is employed to select a reduced subset of features extracted from the subjects' EEG that is most statistically relevant to our treatment-response prediction. These features are then entered into a classifier, which is realized in the form of a kernel partial least squares regression method that performs response prediction. Various scales, including the positive and negative syndrome scale (PANSS) are used as treatment-response indicators. Results: We determined that a set of discriminating EEG features do exist. A low-dimensional representation of the feature space showed significant clustering into clozapine responder and non-responder groups. The minimum level of performance of the proposed prediction methodology, tested over a range of conditions using the leave-one-out cross-validation method using the original 23 subjects, with further testing in an independent sample of 14 subjects, was 85%. Conclusions: These findings indicate that analysis of pre-treatment EEG data can predict the clinical response to clozapine in treatment resistant schizophrenia. Significance: If replicated in a larger population, this novel approach to EEG analysis may assist the clinician in determining treatment-efficacy. (C) 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

关键词： Network reconstruction   Biotic interaction   Spatial autocorrelation   Bio-climate envelope  

摘要： The complexity of ecosystems is staggering, with hundreds or thousands of species interacting in a number of ways from competition and predation to facilitation and mutualism. Understanding the networks that form the systems is of growing importance, e.g. to understand how species will respond to climate change, or to predict potential knock-on effects of a biological control agent. In recent years, a variety of summary statistics for characterising the global and local properties of such networks have been derived, which provide a measure for gauging the accuracy of a mathematical model for network formation processes. However, the critical underlying assumption is that the true network is known. This is not a straightforward task to accomplish, and typically requires minute observations and detailed field work. More importantly, knowledge about species interactions is restricted to specific kinds of interactions. For instance, while the interactions between pollinators and their host plants are amenable to direct observation, other types of species interactions, like those mentioned above, are not, and might not even be clearly defined from the outset. To discover information about complex ecological systems efficiently, new tools for inferring the structure of networks from field data are needed. In the present study, we investigate the viability of various statistical and machine learning methods recently applied in molecular systems biology: graphical Gaussian models, L1-regularised regression with least absolute shrinkage and selection operator (LASSO), sparse Bayesian regression and Bayesian networks. We have assessed the performance of these methods on data simulated from food webs of known structure, where we combined a niche model with a stochastic population model in a 2-dimensional lattice. We assessed the network reconstruction accuracy in terms of the area under the receiver operating characteristic (ROC) curve, which was typically in the range between

关键词： Artificial neural networks   Phosphatases   Support vector machines   Kinase inhibitors   Malignant tumors   Cell cycle and cell division   Machine learning   Stem cell therapy  

摘要： Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://***/cdkipred.

关键词： Missing data   Statistical imputation techniques   Machine learning imputation methods   Survival analysis   Breast cancer prognosis   Early breast cancer  

摘要： Objectives: Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. This work evaluates the performance of several statistical and machine learning imputation methods that were used to predict recurrence in patients in an extensive real breast cancer data set Materials and methods. Imputation methods based on statistical techniques, e.g., mean, hot-deck and multiple imputation, and machine learning techniques, e.g. multi-layer perceptron (MLP), self-organisation maps (SOM) and k-nearest neighbour (KNN), were applied to data collected through the "El Alamo-I" project, and the results were then compared to those obtained from the listwise deletion (LD) imputation method. The database includes demographic, therapeutic and recurrence-survival information from 3679 women with operable invasive breast cancer diagnosed in 32 different hospitals belonging to the Spanish Breast Cancer Research Group (GEICAM). The accuracies of predictions on early cancer relapse were measured using artificial neural networks (ANNs), in which different ANNs were estimated using the data sets with imputed missing values Results: The imputation methods based on machine learning algorithms outperformed imputation statistical methods in the prediction of patient outcome. Friedman's test revealed a significant difference (p = 0.0091) in the observed area under the ROC curve (AUC) values, and the pairwise comparison test showed that the AUCs for MLP, KNN and SOM were significantly higher (p = 00053, p = 0 0048 and p = 00071, respectively) than the AUC from the LD-based prognosis model. Conclusion: The methods based on machine learning techniques were the most suited for the imputation of missing values and led to a significant enhancement of prognosis accuracy compared to imputation methods based on statistical procedures. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Computer-assisted diagnosis   Voice disorders   Vocal fold paresis   Phonovibrography   Feature extraction   Laryngeal high-speed video analysis  

摘要： The clinical diagnosis of voice disorders is based on examination of the rapidly moving vocal folds during phonation (f0: 80-300 Hz) with state-of-the-art endoscopic high-speed cameras. Commonly, analysis is performed in a subjective and time-consuming manner via slow-motion video playback and exhibits low inter- and intra-rater reliability. In this study an objective method to overcome this drawback is presented being based on Phonovibrography, a novel image analysis technique. For a collective of 45 normophonic and paralytic voices the laryngeal dynamics were captured by specialized Phonovibrogram features and analyzed with different machine learning algorithms. Classification accuracies reached 93% for 2-class and 73% for 3-class discrimination. The results were validated by subjective expert ratings given the same diagnostic criteria. The automatic Phonovibrogram analysis approach exceeded the experienced raters' classifications by 9%. The presented method holds a lot of potential for providing reliable vocal fold diagnosis support in the future. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

关键词： 网络安全   机器学习   入侵检测系统  

摘要： 入侵检测是一种保障网络安全的新技术,传统的入侵检测方法存在误报漏报及实时性差等缺点,将机器学习的技术引入到入侵监测系统之中以有效地提高系统性能具有十分重要的现实意义。将目前主要的基于机器学习的贝叶斯分类的方法、神经网络的方法、决策树方法与支持向量机的方法应用于入侵检测系统中,以kdd99公共数据集进行了仿真实验,仿真测试结果表明支持向量机方法(SVM)和神经网络方法具有较好的分类识别性能,适合用于入侵检测。

关键词： Root Mean Square Deviation   Multivariate Adaptive Regression Spline   Log2 Fold Change   Arginine Methylation   Multivariate Adaptive Regression Spline Model  

摘要： Background: In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a complex network that regulate the state of chromatin and processes that depend on it including transcription and DNA replication. Currently, a large number of these epigenetic modifications are being mapped in a variety of cell lines at different stages of development using high throughput sequencing by members of the ENCODE consortium, the NIH Roadmap Epigenomics Program and the Human Epigenome Project. An extremely promising and underexplored area of research is the application of machine learning methods, which are designed to construct predictive network models, to these large-scale epigenomic data sets. Results: Using a ChIP-Seq data set of 20 histone lysine and arginine methylations and histone variant H2A.Z in human CD4(+) T cells, we built predictive models of gene expression as a function of histone modification/variant levels using Multilinear (ML) Regression and Multivariate Adaptive Regression Splines ( MARS). Along with extensive crosstalk among the 20 histone methylations, we found H4R3me2 was the most and second most globally repressive histone methylation among the 20 studied in the ML and MARS models, respectively. In support of our finding, a number of experimental studies show that PRMT5-catalyzed symmetric dimethylation of H4R3 is associated with repression of gene expression. This includes a recent study, which demonstrated that H4R3me2 is required for DNMT3A-mediated DNA methylation-a known global repressor of gene expression. Conclusion: In stark contrast to univariate analysis of the relationship between H4R3me2 and gene expression levels, our study showed that the regulatory role of some modifications like H4R3me2 is masked by confounding variables, but can be elucidated by multivariate/systems-level approaches.

关键词： 太阳光球磁场   磁场复杂性和非势性   支持向量机   太阳质子事件预报  

摘要： 本文选取三个描述太阳活动区磁场复杂性和非势性的特征物理量纵向磁场最大水平梯度|-hBz|m,强梯度中性线长度L,孤立奇点数目η.对这三个参量统计计算后结果作为预报因子,应用支持向量机作为预报方法建立一个基于磁场特征物理量的太阳质子事件短期预报模型,该模型可以预报活动区未来24小时是否爆发太阳质子事件.2002和2003年连续两年的样本检测并和基于传统预报因子的模型进行了比对,结果显示预报模型具有较高的准确率和较低的虚报率,从而验证了太阳光球磁场参量作为太阳质子事件预报因子的有效性.