关键词： Breast ultrasound   Breast cancer   Adaptive boosting   Computer-aided diagnosis   Artificial intelligence  

摘要： The goal of this study was to devise a machine learning methodology as a viable low-cost alternative to a second reader to help augment physicians' interpretations of breast ultrasound images in differentiating benign and malignant masses. Two independent feature sets consisting of visual features based on a radiologist's interpretation of images and computer-extracted features when used as first and second readers and combined by adaptive boosting (AdaBoost) and a pruning classifier resulted in a very high level of diagnostic performance (area under the receiver operating characteristic curve = 0.98) at a cost of pruning a fraction (20%) of the cases for further evaluation by independent methods. AdaBoost also improved the diagnostic performance of the individual human observers and increased the agreement between their analyses. Pairing AdaBoost with selective pruning is a principled methodology for achieving high diagnostic performance without the added cost of an additional reader for differentiating solid breast masses by ultrasound. (C) 2015 World Federation for Ultrasound in Medicine & Biology.

关键词： remote sensing   soil moisture   biomass   retrieval algorithms   machine learning   artificial neural networks   SVM   regression   biophysical parameters  

摘要： The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.

关键词： 转录因子   转录因子家族分类   转录因子预测   植物转录因子数据库  

摘要： 转录因子在植物生长发育和应对胁迫等过程中具有重要调控作用,基因组水平上系统预测植物转录因子是研究其功能和演化的基础。通过深入全面的文献调研,总结了一套完整的植物转录因子家族分类规则,开发了转录因子预测流程,构建了转录因子预测平台。基于该流程,从83种绿色植物中预测到129 288个转录因子,分属58个家族。对预测到的转录因子,从家族和个体两个层次进行了详尽注释,构建了植物转录因子数据库Plant TFDB(http://***)。Plant TFDB提供了覆盖绿色植物主要谱系的转录因子,其中大部分为具有重要经济价值的单子叶和双子叶植物,已成为植物转录因子功能和演化研究的重要信息资源和分析平台。

关键词： virtual screening   compounds classification   extreme entropy machine   extremely randomized trees  

摘要： Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called extremely randomized methods'Extreme Entropy Machine and Extremely Randomized Treesfor their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their non-extreme' competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

关键词： Receiver Operating Characteristic   Machine Learning   Classification Accuracy   Prior Distribution   Test Group  

摘要： P9

关键词： Decision trees   Delayed graft function   Discriminant analysis   Kidney transplantation   Logistic models   Machine learning   Predictive analysis   ROC curve   Sensitivity and specificity   Support vector machines  

摘要： Background: Predictive models for delayed graft function (DGF) after kidney transplantation are usually developed using logistic regression. We want to evaluate the value of machine learning methods in the prediction of DGF. Methods: 497 kidney transplantations from deceased donors at the Ghent University Hospital between 2005 and 2011 are included. A feature elimination procedure is applied to determine the optimal number of features, resulting in 20 selected parameters (24 parameters after conversion to indicator parameters) out of 55 retrospectively collected parameters. Subsequently, 9 distinct types of predictive models are fitted using the reduced data set: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs;using linear, radial basis function and polynomial kernels), decision tree (DT), random forest (RF), and stochastic gradient boosting (SGB). Performance of the models is assessed by computing sensitivity, positive predictive values and area under the receiver operating characteristic curve (AUROC) after 10-fold stratified cross-validation. AUROCs of the models are pairwise compared using Wilcoxon signed-rank test. Results: The observed incidence of DGF is 12.5 %. DT is not able to discriminate between recipients with and without DGF (AUROC of 52.5 %) and is inferior to the other methods. SGB, RF and polynomial SVM are mainly able to identify recipients without DGF (AUROC of 77.2, 73.9 and 79.8 %, respectively) and only outperform DT. LDA, QDA, radial SVM and LR also have the ability to identify recipients with DGF, resulting in higher discriminative capacity (AUROC of 82.2, 79.6, 83.3 and 81.7 %, respectively), which outperforms DT and RF. Linear SVM has the highest discriminative capacity (AUROC of 84.3 %), outperforming each method, except for radial SVM, polynomial SVM and LDA. However, it is the only method superior to LR. Conclusions: The discriminative capacities of LDA, l

关键词： siRNA   有效性预测   机器学习方法   在线设计  

摘要： siRNA设计是RNAi研究中的一个重要部分。由于靶向基因可分割成数以千计的候选siRNA,找到其中最有效的siRNA具有一定的挑战性。本文融合特征分析研究成果和多机器学习方法,设计并实现了一个siRNA在线设计系统。将目标RNA的二级结构作为影响siRNA干扰效率的评分因素,以挑选靶向合适位置的siRNA序列。对于给定的目标基因,系统经过设计得出若干高效siRNA序列的沉默效率及其相关信息。实验测试结果表明,本系统具有较高的siRNA有效性预测精度。

关键词： Power estimation   Energy profiling   Fuzzy neural networks   Linux  

摘要： Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.

关键词： Coal mining data   Data mining   Class label prediction   Naive bays classifier   Artificial neural network   Decision tree model  

摘要： For issue that coal mining data tends to be incomplete, noisy and inconsistent, some popular classifiers are applied to predict class label of coal mining dataset. Noise and bad points are rejected from coal mining data which will be exchanged to input format suitable for mining. Then different classifiers are used to classify class label after extracting features. In the end, classification results are analyzed and knowledge assimilation is done. Experiment results show that decision tree model gives 88% of accuracy to correctly predict class label whereas neural network model predicts 85% correct class label. This research provides a powerful class label prediction tool as well as increasing knowledge of data classification models.