关键词： BP   网络   支持向量机   打浆度   PLC  

摘要： 打浆度是衡量磨浆过程质量达标的一个重要因素,针对其难以在线测量,引入基于BP网络和支持向量机的打浆度软测量方法。仿真结果表明,支持向量机比BP网络的泛化能力强,此方法更能反映打浆度的真实值。

关键词： 激素敏感脂肪酶抑制剂   机器学习方法   变量筛选  

摘要： 对激素敏感脂肪酶,我们构建了表征分子组成、电荷、拓扑、几何结构及物理化学性质等特征的1559个描述符,通过Fischer Score排序过滤和Monte Carlo模拟退火法相结合进行变量筛选得到35个描述符,然后分别用支持向量学习机(SVM)、人工神经网络(ANN),k-近邻(k-NN),连续核密度估计(CKD)和逻辑回归(LR)等机器学习方法建立了激素敏感脂肪酶抑制剂的分类预测模型。对于训练集的200个样本,通过五重交叉验证,各机器学习方法对正样本,负样本和总样本的平均预测精度分别在78.0%-94.0%,69.0%-91.0%和73.5%-92.5%;通过y-scrambling方法验证SVM模型是否偶然相关,结果正样本,负样本和总样本的平均预测精度分别在60.0%-74.0%,58.0%-71.0%和61.0%-69.5%,明显低于实际所建模型的预测精度,表明所建模型不存在偶然相关;对52个没有参与建模的外部独立测试样本,各机器学习方法对正样本,负样本和总样本的预测精度分别在84.6%-92.3%,88.5%-92.3%和86.5%-92.3%。所建模型中,SVM,CKD和LR较好,且明显高于其他文献报道结果。

关键词： MASS-SPECTROMETRY   SECONDARY STRUCTURE   ALPHA-HELICES   QUANTIFICATION   PROTEINS   DATABASE   MODEL  

摘要： Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or "Q-peptides," to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide "detectability" in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository. Molecular & Cellular Proteomics 10: 10.1074/mcp.M110.003384, 1-12, 2011.

关键词： 蛋白质折叠   机器学习   结构预测  

摘要： 蛋白质的生物功能是由它们的空间折叠结构决定的,理解蛋白质的折叠过程是生物信息学领域中极具挑战性的问题之一。近年来,各种优化方法用于蛋白质空间折叠结构预测。这些方法仍存在着不足,算法在变量数目增大时,难以收敛到全局最优解,并容易产生早熟收敛,从而影响求解精度和效率。针对蛋白质结构预测模型多变量多极值的特点,文章采用机器学习方法对蛋白质进行折叠结构预测。

关键词： RECURSIVE NEURAL-NETWORKS   CONNECTIVITY PREDICTION   FEATURE VECTORS   CYSTEINES   STATE   GENERATION   SEQUENCE  

摘要： Motivation: Disulfide bonds stabilize protein structures and play relevant roles in their functions. Their formation requires an oxidizing environment and their stability is consequently depending on the redox ambient potential, which may differ according to the subcellular compartment. Several methods are available to predict cysteine-bonding state and connectivity patterns. However, none of them takes into consideration the relevance of protein subcellular localization. Results: Here we develop DISLOCATE, a two-step method based on machine learning models for predicting both the bonding state and the connectivity patterns of cysteine residues in a protein chain. We find that the inclusion of protein subcellular localization improves the performance of these predictive steps by 3 and 2 percentage points, respectively. When compared with previously developed methods for predicting disulfide bonds from sequence, DISLOCATE improves the overall performance by more than 10 percentage points.

关键词： INHIBITORS   SEQUENCE   LIKENESS   SEARCH  

摘要： Background: We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features. Results: A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided. Conclusions: In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.

关键词： 机器学习方法   分子描述符   递归变量消除法   支持向量机   丙型肝炎病毒  

摘要： 在丙型肝炎病毒(HCV)的基因复制和蛋白质成熟的过程中,非结构蛋白5B(NS5B)作为RNA依赖的RNA聚合酶起到了重要的作用.抑制NS5B聚合酶可以阻止丙型肝炎病毒的RNA复制,因此成为一种治疗丙型肝炎的有效方法.通过计算机方法进行虚拟筛选和预测NS5B聚合酶抑制剂已经变得越来越重要.本文主要采用机器学习方法(支持向量机(SVM)、k-最近相邻法(k-NN)和C4.5决策树(C4.5DT))对已知的丙型肝炎病毒NS5B蛋白酶抑制剂与非抑制剂建立分类预测模型.1248个结构多样性化合物(552个NS5B抑制剂与696个非NS5B抑制剂)被用于测试分类预测系统,并用递归变量消除法选择与NS5B抑制剂相关的性质描述符以提高预测精度.独立验证集的总预测精度为84.1%-85.0%,NS5B抑制剂的预测精度为81.4%-91.7%,非NS5B抑制剂的预测精度为78.2%-87.2%.其中支持向量机给出最好的NS5B抑制剂预测精度(91.7%);C4.5决策树给出最好的非NS5B抑制剂预测精度(87.2%);k-最近相邻法给出最好的总预测精度(85.0%).研究表明机器学习方法可以有效预测未知数据集中潜在的NS5B抑制剂,并有助于发现与其相关的分子描述符.

关键词： Floods  

摘要： More and more natural disasters are happening every year: floods, earthquakes, volcanic eruptions, etc. In order to reduce the risk of possible damages, governments all around the world are investing into development of Early Warning Systems (EWS) for environmental applications. The most important task of the EWS is identification of the onset of critical situations affecting environment and population, early enough to inform the authorities and general public. This paper describes an approach for monitoring of flood protections systems based on machine learning methods. An Artificial Intelligence (AI) component has been developed for detection of abnormal dike behaviour. The AI module has been integrated into an EWS platform of the UrbanFlood project (EU Seventh Framework Programme) and validated on real-time measurements from the sensors installed in a dike.

关键词： VERBAL WORKING-MEMORY   MAJOR DEPRESSION   FACIAL EXPRESSIONS   NEURAL RESPONSES   SAD FACES  

摘要： There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. (C) 2010 Elsevier Inc. All rights reserved.

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。