关键词： 隧道   围岩分类   高斯过程   机器学习  

摘要： 针对现有围岩分类方法的局限性,基于工程实例,利用分类性能优异的高斯过程机器学习模型建立围岩类别与其主要影响因素之间的非线性映射关系,进而提出一种基于高斯过程的隧道围岩分类模型,实现不同情况下围岩分类的合理识别。将该模型应用于川藏公路二郎山隧道围岩分类,研究结果表明,隧道围岩分类的高斯过程机器学习模型是科学可行的,与人工神经网络模型、支持向量机模型相比较,该模型具有参数自适应化的优点,能方便快捷地给出合理可靠且具有概率意义的围岩分类评价结果,可对围岩分类结果的不确定性或可信度进行定量化评价。

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： Follicular lymphoma   microscopic images   machine learning   pixel classification  

摘要： It is not very often to see a symbol-based machine learning approach to be used for the purpose of image classification and recognition. In this paper we will present such an approach, which we first used on the follicular lymphoma images. Lymphoma is a broad term encompassing a variety of cancers of the lymphatic system. Lymphoma is differentiated by the type of cell that multiplies and how the cancer presents itself. It is very important to get an exact diagnosis regarding lymphoma and to determine the treatments that will be most effective for the patient's condition. Our work was focused on the identification of lymphomas by finding follicles in microscopy images provided by the Laboratory of Pathology in the University Hospital of Tenerife, Spain. We divided our work in two stages: in the first stage we did image pre-processing and feature extraction, and in the second stage we used different symbolic machine learning approaches for pixel classification. Symbolic machine learning approaches are often neglected when looking for image analysis tools. They are not only known for a very appropriate knowledge representation, but also claimed to lack computational power. The results we got are very promising and show that symbolic approaches can be successful in image analysis applications.

关键词： Geostatistics   Kriging   Random forest   Spatially continuous variable   Spatial prediction   Support vector machine  

摘要： Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFOK, RFIDS, SVMOK and SVMIDS), using mud content samples in the southwest Australian margin. We also tested the sensitivity of the combined methods to input variables and the accuracy of averaging predictions of the most accurate methods. The accuracy of the methods was assessed using a 10-fold cross-validation. The spatial patterns of the predictions of the most accurate methods were also visually examined for their validity. This study confirmed the effectiveness of RF, in particular its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables. Averaging the predictions of the most accurate methods showed no significant improvement in the predictive accuracy. Visual examination proved to be an essential step in assessing the spatial predictions. This study has opened an alternative source of methods for spatial interpolation of environmental properties. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： Quantum chemical topology   Force field   Multipole moment   Polarisation   Atoms in molecules   Machine learning  

摘要： We describe an intramolecularly polarisable multipolar electrostatic potential model for ethanol, which acts as a pilot molecule for this proof-of-concept study. We define atoms via the partitioning prescribed by quantum chemical topology (QCT). A machine learning method called Kriging is employed to capture the way atomic multipole moments vary upon conformational change. The multipole moments predicted by the Kriging models are used in the calculation of atom-atom electrostatic interaction energies. Charge transfer is treated in the same way as dipolar polarisation and the polarisation of higher rank multipole moments. This method enables the development of a new and more accurate force field. (C) 2011 Elsevier B.V. All rights reserved.

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： Artificial Intelligence   Cysteine/chemistry   Disulfides/chemistry   Eukaryota   Proteins/analysis   Proteins/chemistry  

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： Prediction Accuracy   Test Inhibitor   Radial Basis Function Kernel   Positive Class   Quadratic Kernel  

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.