关键词： machine learning in medicine   radiotherapy   prostate cancer   artificial neural networks   support vector machine  

摘要： Purpose: The goal of this study is to investigate the advantages of large scale optimization methods vs conventional classification techniques in predicting acute toxicity for urinary bladder and rectum due to prostate irradiation. Methods: Clinical and dosimetric data of 321 patients undergoing prostate conformal radiotherapy were recorded. Gastro-intestinal and genito-urinary acute toxicities were scored according to the Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer (RTOG/EORTC) scale. Patients were classified in two categories to separate mild (Grade < 2) from severe toxicity levels (Grade > 2). Machine learning methods at different complexity were implemented to predict toxicity as a function of multiple variables. The first approach consisted of a large scale optimization method, based on genetic algorithms (GAs) and artificial neural networks (ANN). The second approach was a binary classifier based on support vector machines (SVM). Results: The ANN and SVM-based solutions showed comparable prediction accuracy, exhibiting an area under the receiver operating characteristic (ROC) curve of 0.7. Different sensitivity and specificity features were measured for the two approaches. The ANN algorithm showed enhanced sensitivity if combined with appropriate classification criteria. Conclusions: The results demonstrate that high sensitivity in toxicity prediction can be achieved with optimized ANNs, that are put forward to represent a valuable support in medical decisions. Future studies will be focused on enlarging the available patient database to increase the reliability of toxicity prediction algorithms and to define optimal classification criteria. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3582947]

关键词： P2P流量   深度包检测   机器学习   传输层检测  

摘要： 如何快速而准确的检测出P2P流量,是如今网络管理中的1个重要的问题。现在常见的检测方法有基于端口检测法,DPI深度包检测,以及根据传输层特征来检测。DPI深度包检测方法需要及时跟新特征库,对于加密协议无法识别等缺陷限制了其应用。机器学习的传输层检测方法通过分析流的统计特征来检测P2P流量。较之DPI,该方法能检测出DPI无法检测出的加密的P2P流量以及特征库外的流量。本文提出了1种新的结合DPI与基于机器学习的传输层检测方法的模型,并通过实验验证该模型能弥补DPI方法的缺陷,提高检测的准确性。

关键词： 数据挖掘   朴素贝叶斯分类   数据预测   鸢尾花(Iris)数据集  

摘要： 朴素贝叶斯分类方法是数据库分类知识挖掘领域的一项基本技术,并具有广泛的应用。使用贝叶斯分类算法实现了对经典数据集Iris的分类。实践表明,朴素贝叶斯分类是一种有效的数据挖掘分类算法。

关键词： Hormone therapy   MRI   Random forest   WHIMS  

摘要： Use of conjugated equine estrogens (CEE) has been linked to smaller regional brain volumes in women aged >= 65 years;however, it is unknown whether this results in a broad-based characteristic pattern of effects. Structural magnetic resonance imaging was used to assess regional volumes of normal tissue and ischemic lesions among 513 women who had been enrolled in a randomized clinical trial of CEE therapy for an average of 6.6 years, beginning at ages 65-80 years. A multivariate pattern analysis, based on a machine learning technique that combined Random Forest and logistic regression with L-1 penalty, was applied to identify patterns among regional volumes associated with therapy and whether patterns discriminate between treatment groups. The multivariate pattern analysis detected smaller regional volumes of normal tissue within the limbic and temporal lobes among women that had been assigned to CEE therapy. Mean decrements ranged as high as 7% in the left entorhinal cortex and 5% in the left perirhinal cortex, which exceeded the effect sizes reported previously in frontal lobe and hippocampus. Overall accuracy of classification based on these patterns, however, was projected to be only 54.5%. Prescription of CEE therapy for an average of 6.6 years is associated with lower regional brain volumes, but it does not induce a characteristic spatial pattern of changes in brain volumes of sufficient magnitude to discriminate users and nonusers. (c) 2011 Elsevier Inc. All rights reserved.

关键词： 人工   神经网络   机器学习方法  

摘要： 机器学习方法经常被应用到解决医学和生物信息学的问题。本文列举了一些把机器学习方法应用到生物信息学领域的实例。比如:组建多重神经网络,运用该神经网络对4种不同形势的肿瘤患者进行分类。

关键词： genes   peroxisomal proteins   PTS1 tripeptides   EST database   novel plant peroxisomal targeting signal identification   experimental verification rate   plant peroxisomal proteome   specific targeting-enhancing pattern   Arabidopsis Proteins   Genes   In vivo   Crop (plant)   Tripeptides   targeting   Machine Learning   Proteomes   Plant Proteins   plants   Prediction methods   Peroxisomes  

摘要： In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

关键词： 数学建模   Adaboost算法   改进Haar特征   Radon变换   3次B样条   神经网络  

摘要： 首先,针对不同光照、复杂背景和投影失真的车牌图像建立基于Adaboost算法和改进Haar特征的车牌检测模型;然后,运用Radon变换进行车牌校正,并结合3次B样条小波变换和识别反馈模型对字符进行粗和精分割;最后,根据汉字和数字字母的不同结构特征,采用不同的算法提取特征,特别是针对车牌字符特点,训练汉字、字母和数字字母3种神经网络模型用于建立字符识别模型.实验结果表明该模型是实用的.

关键词： Fault diagnosis   Artificial neural network   Support vector machine  

摘要： Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on rolling bearings is used. The vibration response are obtained and analyzed for the various defects of ball bearings. The specific defects are considered as crack in outer race, inner race with rough surface and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that the machine learning algorithms mentioned above can be used for automated diagnosis of bearing faults. It is also observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Skin penetration   QSARs   QSPRs   Gaussian process regression  

摘要： Improving predictions of skin permeability is a significant problem for which mathematical solutions have been sought for around twenty years. However, the current approaches are limited by the nature of the models chosen and the nature of the dataset. This is an important problem, particularly with the increased use of transdermal and topical drug delivery systems. In this work, we apply K-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the skin permeability coefficient of penetrants. A considerable improvement, both statistically and in terms of the accuracy of predictions, over the current quantitative structure-permeability relationships (QSPRs) was found. Gaussian processes provided the most accurate predictions, when compared to experimentally generated results. It was also shown that using five molecular descriptors - molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups - can produce better predictions than when using only lipophilicity and the molecular weight, which is an approach commonly found with QSPRs. The Gaussian process regression with five compound features was shown to give the best performance in this work. Therefore, Gaussian processes would appear to provide a viable alternative to the development of predictive models for skin absorption and underpin more realistically mechanistic understandings of the physical process of the percutaneous absorption of exogenous chemicals. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Tetrahymena pyriformis toxicity   Quantitative structure-toxicity relationship   Substructure pattern recognition   Support vector machine   Machine learning   Information gain  

摘要： There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. (C) 2010 Elsevier Ltd. All rights reserved.