关键词： 西安电子科技大学   机器学习方法   学术交流   生物系统   Human   实验室工作   学术报告   基因变异  

摘要： 美国北卡罗来纳大学施兴华博士日前来西安电子科技大学访问交流并做了题为“A Machine Learning Approach to Understanding Human Genetic Variation”的学术报告，详细介绍了机器学习方法在人类基因变异中的应用，并与师生们进行了学术交流，就相关问题进行了深入讨论，同时介绍了美国北卡罗来纳大学以及自己实验室工作科研的情况．施兴华博士主要研究在系统层面基因结构对生物过程及复杂表型的影响，研究工作涉及复杂疾病、复杂网络分析和生物系统的数学模型．

关键词： 丙型肝炎病毒NS5B聚合酶   非核苷抑制剂   线性逐步回归分析   偏最小二乘法   遗传算法   支持向量机  

摘要： 对89个苯并异噻唑和苯并噻嗪类丙型肝炎病毒(HCV)NS5B聚合酶非核苷抑制剂进行了定量构效关系(QSAR)研究.采用遗传算法组合偏最小二乘(GA-PLS)和线性逐步回归分析(LSRA)两种特征选择方法选择最优描述符子集,然后建立多元线性回归和偏最小二乘线性回归模型.并首次尝试使用遗传算法耦合支持向量机方法(GA-SVM)对两种特征选择方法所选的描述符子集分别建立非线性支持向量机回归模型.三种机器学习方法所建模型均得到比较满意的预测效果.采用LSRA所选的6个描述符建立的三个QSAR模型对于测试集的相关系数为0.958-0.962,GA-SVM法给出最好的预测精度(0.962).采用GA-PLS所选的7个描述符建立的三个QSAR模型对于测试集的相关系数为0.918-0.960,偏最小二乘回归模型的结果最好(0.960).本工作提供了一种有效的方法来预测丙型肝炎病毒抑制剂的生物活性,该方法也可以扩展到其他类似的定量构效关系研究领域.

关键词： optimization theory   Osuna-Platt algorithm   statistical classification   cardiac SPECT imaging   medical image analysis  

摘要： This article presents a method based on support vector machines (SVMs) and the Osuna-Platt algorithm used to diagnose the ischemic heart disease. It also includes the necessary concepts from the optimization theory which make it possible to formulate the problem for support vectors and the Osuna-Platt algorithm applied. Next, a principal component analysis (PCA) algorithm used is also presented. Heart images acquired using Single Photon Emission Computed Tomography (SPECT) have been used in the experimental part. Results of classifying cardiac SPECT images using SVM, PCA and neural networks are compared here with those obtained using another method of machine learning - CLIP3 - a combination of the decision tree algorithm and the rule induction algorithm. Tests against an SPECT image database have shown that SVMs are generally more accurate and specific, while the PCA algorithm is the most sensitive for all data sets analysed in this research project.

关键词： Algorithms   Energy   Neural networks   Molecular modeling   Molecules  

摘要： The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.

关键词： Anatomy and physiology   Biochemical simulations   Biology   Computational biology   Computational neuroscience   Computer science   Computerized simulations   Data mining   Engineering   Medicine   Neurological system   Neurophysiology   Neuroscience   Research Article   Signal processing   Single neuron function   Synapses  

摘要： Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is there

关键词： 数据隐藏   可逆   插值   直方图平移  

摘要： 提出一种基于插值预测和块分类模型的低失真可逆数据隐藏算法。首先,对原始图像进行插值预测,得到预测误差图像;然后根据误差图像建立直方图,使得产生的直方图更陡峭,峰值点出现频率更大;接着对误差图像进行分块,利用块分类模型,在隐藏块内利用直方图平移隐藏信息,尽可能地减少平移点的数量;最后,相应地修改原始像素的灰度值,得到低失真的隐秘图像。实验结果表明,本文算法不仅可以实现可逆数据隐藏,而且隐藏效果较好,有效载荷较高。

关键词： Accuracy Rate   Hepatitis Virus Infection   Routine Pathology   Tree Ensemble   Single Decision Tree  

摘要： Background: Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using a never before analysed data set from ACT Pathology (Canberra, Australia) relating to HBV and HCV patients. Results: It was easier to predict immunoassay positive cases than negative cases of HBV or HCV. While applying an ensemble-based approach rather than a single classifier had a small positive effect on the accuracy rate, this also varied depending on the virus under analysis. Finally, scaling data before prediction also has a small positive effect on the accuracy rate for this dataset. A graphical analysis of the distribution of accuracy rates across ensembles supports these findings. Conclusions: Laboratories looking to include machine learning as part of their decision support processes need to be aware that the infection outcome, the machine learning method used and the virus type interact to affect the enhanced laboratory diagnosis of hepatitis virus infection, as determined by primary immunoassay data in concert with multiple routine pathology laboratory variables. This awareness will lead to the informed use of existing machine learning methods, thus improving the quality of laboratory diagnosis via informatics analyses.

关键词： Biodiesel   Engine performance   Engine modeling   Engine optimization   Machine learning  

摘要： This study aims to determine optimal biodiesel ratio that can achieve the goals of fewer emissions, reasonable fuel economy and wide engine operating range. Different advanced machine learning techniques, namely ELM (extreme learning machine), LS-SVM (least-squares support vector machine) and RBFNN (radial-basis function neural network), are used to create engine models based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. Based on the engine models, two optimization methods, namely SA (simulated annealing) and PSO (particle swarm optimization), are employed and a flexible objective function is designed to determine the optimal biodiesel ratio subject to various user-defined constraints. A case study is presented to verify the modeling and optimization framework. Moreover, two comparisons are conducted, where one is among the modeling techniques and the other is among the optimization techniques. Experimental results show that, in terms of the model accuracy and training time, ELM with the logarithmic transformation is better than LS-SVM and RBFNN with/without the logarithmic transformation. The results also show that PSO outperforms SA in terms of fitness and standard deviation, with an acceptable computational time. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： LIBS   Fingerprints   Home-made explosives   Harmless   Machine learning   Decision tree  

摘要： During recent years laser-induced breakdown spectroscopy (LIBS) has been considered one of the techniques with larger ability for trace detection of explosives. However, despite of the high sensitivity exhibited for this application, LIBS suffers from a limited selectivity due to difficulties in assigning the molecular origin of the spectral emissions observed. This circumstance makes the recognition of fingerprints a latent challenging problem. In the present manuscript the sorting of six explosives (chloratite, ammonal, DNT, TNT, RDX and PETN) against a broad list of potential harmless interferents (butter, fuel oil, hand cream, olive oil,...), all of them in the form of fingerprints deposited on the surfaces of objects for courier services, has been carried out. When LIBS information is processed through a multi-stage architecture algorithm built from a suitable combination of 3 learning classifiers, an unknown fingerprint may be labeled into a particular class. Neural network classifiers trained by the Levenberg-Marquardt rule were decided within 3D scatter plots projected onto the subspace of the most useful features extracted from the LIBS spectra. Experimental results demonstrate that the presented algorithm sorts fingerprints according to their hazardous character, although its spectral information is virtually identical in appearance, with rates of false negatives and false positives not beyond of 10%. These reported achievements mean a step forward in the technology readiness level of LIBS for this complex application related to defense, homeland security and force protection. (C) 2013 Elsevier B.V. All rights reserved.

关键词： 黑色素瘤   计算机辅助诊断   机器学习  

摘要： 黑色素瘤的计算机辅助诊断是基于激光共聚焦扫描显微镜(CLSM)皮肤图像纹理特征,并引入机器学习的技术,为临床应用研发的一种能够准确、有效地识别在体恶性黑色素瘤新医学诊断方法,将常用的基于机器学习的ID3、分类与回归树(CART)和AdaBoost三种算法应用于良恶性黑色素瘤图像的特征识别,并对各种学习方法的性能进行比较。实验结果表明,AdaBoost算法具有较好的分类识别性能,不但提高了恶性黑色素瘤早期诊断的准确度,降低了良性黑色素瘤的误诊率,而且为临床上早期发现和诊断提供了客观依据。