关键词： Computation time   Genotype imputation   Imputation accuracy   Machine learning methods  

摘要： Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other *** running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation *** tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： 软件安全   结合语义   机器学习   方法   探讨  

摘要： 近年来,在计算机信息技术发展的同时,各种类型的软件已经被广泛地应用在社会生活和生产当中的多个领域。然而,在长期的发展过程中,信息系统的漏洞逐渐突显出来,病毒的种类也处于动态的变化过程中,所以,软件安全已经成为当前所需考虑的难题。而在此背景下,机器学习快速发展,并且在多个领域都被广泛应用。

关键词： Support vector regression   SPI   Drought forecasting   Wavelet transforms   Bootstrap   Boosting  

摘要： This study explored the ability of coupled machine learning models and ensemble techniques to predict drought conditions in the Awash River Basin of Ethiopia. The potential of wavelet transforms coupled with the bootstrap and boosting ensemble techniques to develop reliable artificial neural network (ANN) and support vector regression (SVR) models was explored in this study for drought prediction. Wavelet analysis was used as a pre-processing tool and was shown to improve drought predictions. The Standardized Precipitation Index (SPI) (in this case SPI 3, SPI 12 and SPI 24) is a meteorological drought index that was forecasted using the aforementioned models and these SPI values represent short and long-term drought conditions. The performances of all models were compared using RMSE, MAE, and R-2. The prediction results indicated that the use of the boosting ensemble technique consistently improved the correlation between observed and predicted SPIs. In addition, the use of wavelet analysis improved the prediction results of all models. Overall, the wavelet boosting ANN (WBS-ANN) and wavelet boosting SVR (WBS-SVR) models provided better prediction results compared to the other model types evaluated. (C) 2016 Elsevier B.V. All rights reserved.

摘要： Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsyproven invasive breast cancers from National Cancer Institute's multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators;area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER-, PR+ versus PR-, HER2+ versus HER2-, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P = 0.04 for lesions <= 2 cm;P = 0.02 for lesions >2 to <= 5 cm) as with the entire data set (P-value = 0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for highthroughput discrimination of breast cancer subtypes and may yield a quantitative pr

关键词： 功能注释   机器学习法   功能预测   基因  

摘要： 目前,基于计算机数学方法对基因的功能注释已成为热点及挑战,其中以机器学习方法应用最为广泛。生物信息学家不断提出有效、快速、准确的机器学习方法用于基因功能的注释,极大促进了生物医学的发展。本文就关于机器学习方法在基因功能注释的应用与进展作一综述。主要介绍几种常用的方法,包括支持向量机、k近邻算法、决策树、随机森林、神经网络、马尔科夫随机场、logistic回归、聚类算法和贝叶斯分类器,并对目前机器学习方法应用于基因功能注释时如何选择数据源、如何改进算法以及如何提高预测性能上进行讨论。

关键词： 强对流天气预警   SVM   不平衡数据分类   机器学习  

摘要： 用机器学习中有监督学习模型支持向量机SVM来进行强对流天气的识别和预报。强对流天气的发生可以看作是小概率事件,因此强对流天气的预警问题可以作为不平衡数据分类问题来处理。在SVM的应用上结合判别准则来对不平衡数据进行处理,更好的对强对流天气进行预警。本文从数据的获取、训练算法的选择、算法的应用、实验结果的评估几个方面进行了详细的描述。通过采用丹佛地区的数据进行大量试验,排除了不平衡数据对分类的干扰,提高了强对流天气识别的准确度。

关键词： Li-ion batteries   Crystal system   Machine learning   Classification methods   Monte Carlo   Ensemble methods   Optimization  

摘要： The system of crystal structure has a major effect on the physical and chemical properties of Li-ion silicate cathodes. Hence, the prediction of crystal system has a vital importance to estimate many other properties of cathodes for applications in batteries. Three major crystal systems (monoclinic, orthorhombic and triclinic) of silicate-based cathodes with Li-Si-(Mn, Fe, Co)-O compositions were predicted using wide range of classification algorithms in machine learning. The calculations are based on the results of density functional theory calculations from Materials Project. The strong correlation between the crystal system and other physical properties of the cathodes was confirmed based on the feature evaluation in the statistical models. In addition, the parameters of various classification methods were optimized to obtain the best accuracy of prediction. Ensemble methods including random forests and extremely randomized trees provided the highest accuracy of prediction among other classification methods in the Monte Carlo cross validation tests. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Sun: activity   Sun: flares  

摘要： Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections (CMEs). Usually, solar active regions that produce large flares will also produce a CME, but this is not always true. Despite advances in numerical modeling, it is still unclear which circumstances will produce a CME. Therefore, it is worthwhile to empirically determine which features distinguish flares associated with CMEs from flares that are not. At this time, no extensive study has used physically meaningful features of active regions to distinguish between these two populations. As such, we attempt to do so by using features derived from (1) photospheric vector magnetic field data taken by the Solar Dynamics Observatory's Helioseismic and Magnetic Imager instrument and (2) X-ray flux data from the Geostationary Operational Environmental Satellite's X-ray Flux instrument. We build a catalog of active regions that either produced both a flare and a CME (the positive class) or simply a flare (the negative class). We then use machine-learning algorithms to (1) determine which features distinguish these two populations, and (2) forecast whether an active region that produces an M-or X-class flare will also produce a CME. We compute the True Skill Statistic, a forecast verification metric, and find that it is a relatively high value of similar to 0.8 +/- 0.2. We conclude that a combination of six parameters, which are all intensive in nature, will capture most of the relevant information contained in the photospheric magnetic field.

关键词： methods: data analysis   novae, cataclysmic variables   supernovae: general   stars: variables: general   stars: variables: RR Lyrae  

摘要： The exploitation of present and future synoptic (multiband and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MultiLayer Perceptron with Quasi Newton Algorithm and K-Nearest Neighbours, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extragalactic objects and identification of supernovae.

摘要： The predictive value of rCTP over obstructive CAD was evaluated by area under the receiver operating characteristic curve (AUC) and category-free net reclassification improvement (NRI).