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  

关键词： Machine learning   Maximum steady-state flux   Permeability   QSPR   Transdermal drug delivery  

摘要： The prediction of maximum steady state flux values of a chemical compound from its structural features plays an important role in design of transdermal drug delivery systems. In this study, we developed the quantitative structure property relationship (QSPR) models to estimate the maximum steady state flux of 245 drugs-like compounds through the polydimethylsiloxane membranes. A correlation-based feature selection was used for descriptor selection. The selected descriptors, surface tension, polarity, and count of hydrogen accept sites, which are interpretable and can be used to explain the permeability of chemicals. These descriptors are used for developing the QSPR prediction models by multiple linear regression, artificial neural network, support vector machine (SVM) and Instance-Based Learning algorithms using K nearest neighbor machine learning approaches. The models were assessed by internal and external validation. All four approaches yield the QSPR models with good statistics. The models developed by SVM have better prediction performance. These models can be useful for predicting the permeability new untested compounds. © 2015, The Korean Society of Pharmaceutical Sciences and Technology.

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.

关键词： Posttraumatic stress   Military   Prediction   Prevention   Machine learning   Support vector machines  

摘要： Background: Pro deployment identification of soldiers at risk for long-term posttraumatic stress psychopathology after home coming is important to guide decisions about deployment. Early post deployment identification can direct early interventions to those in need and thereby prevents the development of chronic psychopathology. Both hold significant public health benefits given large numbers of deployed soldiers, but has so far not been achieved. Here, we aim to assess the potential for pre- and early post deployment prediction of resilience or posttraumatic stress development in soldiers by application of machine learning (ML) methods. Methods: ML feature selection and prediction algorithms were applied to a prospective cohort of 561 Danish soldiers deployed to Afghanistan in 2009 to identify unique risk indicators and forecast long-term posttraumatic stress responses. Results: Robust pre- and early postdeployment risk indicators were identified, and included individual PTSD symptoms as well as total level of PTSD symptoms, previous trauma and treatment, negative emotions, and thought suppression. The predictive performance of these risk indicators combined was assessed by cross-validation. Together, these indicators forecasted long term posttraumatic stress responses with high accuracy (pre-deployment: AUC=0.84 (95% CI=0.81-0.87), post-deployment: AUC=0.88 (95% CI=0.85-0.91)). Limitations: This study utilized a previously collected data set and was therefore not designed to exhaust the potential of ML methods. Further, the study relied solely on self-reported measures. Conclusions: Pre-deployment and early post-deployment identification of risk for long-term posttraumatic psychopathology are feasible and could greatly reduce the public health costs of war. (C) 2015 Elsevier B.V. All rights reserved

关键词： 线性随机效应混合模型   机器学习方法   纵向数据   交叉验证   标准均方误差  

摘要： 本文针对牛奶中所含蛋白质的纵向数据,利用R软件,运用机器学习方法中的决策树、boost、bagging、随机森林、神经网络、支持向量机和传统处理纵向数据的线性随机效应混合模型做预测对比。变化训练集并进行八折交叉验证,对得到的标准均方误差分析可知:对于该数据,无论是长期预测(训练集更大)还是短期预测,传统的方法远远不如机器学习方法,机器学习方法有很好的稳健性。