一种基于遗传算法的PPI网络全局比对算法

蛋白质相互作用网络比对在识别同源蛋白质或者蛋白质功能模块、蛋白质功能预测等方面具有十分重要的生物学意义.通常从拓扑特性和生物特性两个方面来衡量网络比对的结果，而现有的网络比对算法很难同时取得好的拓扑特性和生物特性.基于此，本文提出一种新的网络比对算法NABG.NABG利用最小度启发式算法计算节点在网络中的重要性，并基于重要性得分计算节点对的拓扑相似性，引入节点对的序列相似信息，使拓扑和生物相似性高的蛋白质对被比对上；基于结合了节点相似性和边保守性的目标函数，使用遗传算法模拟生物进化过程来优化比对结果.NABG分别在合成网络和真实网络上进行了实验，并与MGANA++、PROPER、SPINAL等算法作比较分析.实验结果表明，NABG的比对结果在拓扑指标以及生物指标上能保持均衡的高指标且更具有生物学意义.     

蛋白质序列比对下的分子虚拟筛选方法

分子虚拟筛选方法旨在找到一种可以与受体蛋白质进行相互作用并适当修改其生物学行为的活性分子.大多数分子虚拟筛选方法的先决条件是已知蛋白质的结构或小分子结合物.然而对于大多数蛋白质而言,这些信息都是未知的.因此,本文提出了一种名为Screener的基于蛋白质序列比对和活性分子相似性评估的分子虚拟筛选方法.Screener首先从受体蛋白质的序列出发,生成位置特异性频率矩阵特征、二级结构特征以及溶剂可及性特征,利用I-LBR程序对受体蛋白质的潜在结合位点残基进行预测;其次,根据预测的结合位点残基以及相关特征信息构建模板蛋白质库;然后,将所有与任意模板蛋白质相互作用的活性分子收集起来构成潜在的种子分子库;最后,利用分子2D指纹之间的相似性来对待筛选分子集进行排序,完成分子虚拟筛选.在基准测试集DUD40和DUD-E65上,Screener的平均EF^1%分别为16.6和25.7,HR^1%分别为44.1和67.6.基准测试结果表明Screener的虚拟筛选平均性能优于基于对接的虚拟筛选方法AutoDock Vina及基于结构比对的虚拟筛选方法FINDSIT...     

引入序列信息的残基相互作用网络比对算法

残基相互作用网络比对,对于研究蛋白质结构与功能的关系具有重要意义.在基于网络拓扑信息进行网络比对的MAGNA算法基础上,将蛋白质的序列信息（即残基匹配度）引入到其优化函数中,确定拓扑信息和序列信息对比对的影响程度,提出适合于残基相互作用网络比对的SI-MAGNA算法.实验结果表明,SI-MAGNA算法比现有的基于网络拓扑信息的经典比对方法（GRAAL、MI-GRAAL、MAGNA和CytoGEDEVO）具有更高的边正确性（edge correctness,简称EC）.最后,使用SI-MAGNA算法对来自不同耐热温度的生物的同源蛋白质进行网络比对和分析,探索蛋白质结构对其热稳定性的影响.     

预测同源寡聚蛋白质的支持向量机方法

计算实验表明蛋白质一级结构包含着四级结构信息。本文用支持向量机方法从蛋白质一级结构出发区分同源二聚体和非同源二聚体。蛋白质原始序列的子序列分布用于支持向量机的输入向量，从而充分考虑了蛋白质序列的信息。当子序列的长度为3时，10次交叉验证的总预测准确率达到84．9％，在相同的数据集上，比原有的决策树方法提高了15．0％。实验表明残基顺序对同源寡聚蛋白质的识别起重要作用，而支持向量机方法是蛋白质四级结构预测的强有力工具。     

搜索比对算法研究

在蛋白质结构预测算法中同源建模被认为是当前最成功的预测算法,文中指出了同源建模算法存在的缺陷,并且针对这一缺陷设计出改进算法。基于结构信息的目标模板比对算法,对搜索敏感度和比对准确度等方面有所提高。     

基于进化保守性的蛋白质相互作用位点预测

为了从蛋白质结构数据库中提取经验知识,进行蛋白质作用位点预测,提出了以蛋白质序列谱作为特征向量,采用支持向量机算法进行训练和预测蛋白质相互作用位点的方法。从蛋白质一级序列出发,以序列上邻近残基的序列谱为输入特征向量,采用支持向量机方法构建预测器,来预测蛋白质相互作用位点,预测精度达到70.47%,相关系数CC=0.1919。实验结果表明,利用蛋白质序列谱,结合支持向量机算法进行蛋白质相互作用位点预测的方法是有效的。     

基于残基序列信息的蛋白质相互作用位点预测

蛋白质相瓦作用位点在细胞进程中有着非常重要的作用.尽管利用高通量方法发现蛋白质相瓦作用位点取得很大的成功,仍需要计算方法辅助预测实验中的相互作用位点.本文提出了基于残基序列谱、进化率和疏水性的预测异源蛋白质复合物作用位点的两种向量表示方法并以支持向量机实现预测.其中,提出新的向量表示法取得更好的预测性能.文中的数据集由66个异源复合物蛋白质链组成.     

一种基于折叠模式识别的蛋白质结构片段库构建方法

片段组装方法是从头预测蛋白质三维结构的一类重要方法.现有的基于序列相似的片段库质量限制了低同源目标的预测精度,所以寻找与天然结构更加拟合的已知蛋白质结构片段来构建高质量的片段库是片段组装方法的一项重要任务.本文利用SCOP数据库中的三维结构相似性,对SCOP的折叠模式进行预测,提取预测出的相同折叠模式的已知蛋白质结构的信息,生成保存残基信息的数据库(Vall库).然后将目标蛋白质序列分割成的残基片段与Vall库进行综合评价后生成一种新的片段库,该片段库可以用于一个骨架预测并行蚁群算法.将本文方法与蛋白质结构预测程序RosettaAbinitio的基于序列的片段库进行了比较,实验结果表明采用本文方法的片段库可以找到更接近天然构象的蛋白质结构.     

基于并行多类支持向量机的蛋白质结构预测*

在远同源检测的蛋白质结构预测方法中,基于支持向量机的方法取得了优于其他方法的高准确性,但这类方法只能完成对目标蛋白质作出是否属于特定蛋白质结构的判别,而实际应用中常需要直接给出具体的结构预测结果.提出一种基于多类支持向量机的蛋白质结构预测方法,通过采用加权一对多的多类分类方法对标准支持向量机输出结果进行综合评价,获得唯...     

基于交叉覆盖算法的蛋白质二级结构预测方法

蛋白质二级结构预测在蛋白质空间结构预测中起着承上启下的重要作用。近年来,大量的方法应用于二级结构预测中,其中,神经网络算法效果较好。但是,由于传统的神经网络存在结构复杂、学习速度慢、运行效率低、处理海量数据困难的缺陷,大大影响了预测的效果,因此,该文将一种基于构造性神经网络算法,也就是交叉覆盖算法应用于蛋白质二级结构预测中,另外,为了引入更多的同源家族结构的信息,采用了基于概率的Profile编码方式。通过实验证明将交叉覆盖算法运用在蛋白质二级结构预测中的可行性．并且比传统的神经网络方法有了更高的准确率。     

Alignment-free cancelable fingerprint templates based on local minutiae information.

To replace compromised biometric templates, cancelable biometrics has recently been introduced. The concept is to transform a biometric signal or feature into a new one for enrollment and matching. For making cancelable fingerprint templates, previous approaches used either the relative position of a minutia to a core point or the absolute position of a minutia in a given fingerprint image. Thus, a query fingerprint is required to be accurately aligned to the enrolled fingerprint in order to obtain identically transformed minutiae. In this paper, we propose a new method for making cancelable fingerprint templates that do not require alignment. For each minutia, a rotation and translation invariant value is computed from the orientation information of neighboring local regions around the minutia. The invariant value is used as the input to two changing functions that output two values for the translational and rotational movements of the original minutia, respectively, in the cancelable template. When a template is compromised, it is replaced by a new one generated by different changing functions. Our approach preserves the original geometric relationships (translation and rotation) between the enrolled and query templates after they are transformed. Therefore, the transformed templates can be used to verify a person without requiring alignment of the input fingerprint images. In our experiments, we evaluated the proposed method in terms of two criteria: performance and changeability. When evaluating the performance, we examined how verification accuracy varied as the transformed templates were used for matching. When evaluating the changeability, we measured the dissimilarities between the original and transformed templates, and between two differently transformed templates, which were obtained from the same original fingerprint. The experimental results show that the two criteria mutually affect each other and can be controlled by varying the control parameters of the changing functions.     

PSIST: A scalable approach to indexing protein structures using suffix trees

Approaches for indexing proteins and for fast and scalable searching for structures similar to a query structure have important applications such as protein structure and function prediction, protein classification and drug discovery. In this paper, we develop a new method for extracting local structural (or geometric) features from protein structures. These feature vectors are in turn converted into a set of symbols, which are then indexed using a suffix tree. For a given query, the suffix tree index can be used effectively to retrieve the maximal matches, which are then chained to obtain the local alignments. Finally, similar proteins are retrieved by their alignment score against the query. Our results show classification accuracy up to 50% and 92.9% at the topology and class level according to the CATH classification. These results outperform the best previous methods. We also show that PSIST is highly scalable due to the external suffix tree indexing approach it uses; it is able to index about 70,500 domains from SCOP in under an hour.     

Parallel computation in biological sequence analysis

A massive volume of biological sequence data is available in over 36 different databases worldwide, including the sequence data generated by the Human Genome project. These databases, which also contain biological and bibliographical information, are growing at an exponential rate. Consequently, the computational demands needed to explore and analyze the data contained in these databases is quickly becoming a great concern. To meet these demands, we must use high performance computing systems, such as parallel computers and distributed networks of workstations. We present two parallel computational methods for analyzing these biological sequences. The first method is used to retrieve sequences that are homologous to a query sequence. The biological information associated with the homologous sequences found in the database may provide important clues to the structure and function of the query sequence. The second method, which helps in the prediction of the function, structure, and evolutionary history of biological sequences, is used to align a number of homologous sequences with each other. These two parallel computational methods were implemented and evaluated on an Intel IPSC/860 parallel computer. The resulting performance demonstrates that parallel computational methods can significantly reduce the computational time needed to analyze the sequences contained in large databases     

Parallel divide and conquer bio-sequence comparison based on Smith-Waterman algorithm

Biological sequence (e.g. DNA sequence) can be treated as strings over some fixed alphabet of characters (a, c, t and g)[1]. Sequence alignment is a procedure of comparing two or more sequences by searching for a series of individual characters that are in the same order in the sequences. Two-sequence alignment, pair-wise alignment, is a way of stacking one sequence above the other and matching characters from the two sequences thaat lie in the same column: identical characters are placed in …     

基于直接连接的分布式数据库查询优化实现方法研究

首先对分布式数据库查询执行代价模型进行分析,然后对直接连接中的连接运算的方法、连接关系的传输方法和执行场地等问题进行研究,并计算所有评估方法的执行代价,从中选择出最小执行代价的执行方法,最终确定了执行的场地、连接的方法和传输方法.     

基于网格中心点的点在多边形内的高效判定

提出一种基于均匀网格的点在多边形内的高效判定算法.它首先建立均匀网格,并从左至右依次计算每个网格单元中心点的位置属性.每个单元中心点的位置属性直接依据其左侧邻接单元已知位置属性的中心点快速获得.在判定点的位置时,确定被测点所在单元,并依据该单元中心点的位置属性判定被测点的位置属性.由于预处理和判定时均利用邻近点的已知位置属性来确定未知点位置属性,可以很好地进行局部化的计算.因此,新方法比现有方法快很多,并且其预处理时间复杂度也由同类网格算法的O(N3/2)下降为O(N).同时,新方法可以统一处理含有自相交及重叠边的非流形多边形.实验结果表明,相比于其他基于均匀网格的方法,新方法可将预处理的速度提高几倍,将判断计算的速度提高十几到几十倍.其速度甚至优于具有该问题最低判定计算时间复杂度O(logN)的基于凸剖分的判定算法.     

改进的本体概念相似度计算模型

本体映射能很好地解决语义网中的本体异构性问题,其核心在于计算本体概念的相似度。针对现有的概念相似度计算的精度和查准率不高,提出一种改进的概念相似度计算模型。首先利用本体特征之间的偏序关系建立形式背景和概念格,然后在结构层次求出概念间的交不可约元集,并通过对集合里各元素的语义关系进行量化计算出概念间的相似度。实例和分析结果表明,改进的概念相似度计算模型在F-Score上有明显提高。     

QFT Templates for Plants With a High Number of Uncertainty Parameters

When uncertainty is given in parametric form, the QFT templates computation requires time if the number of parameters is high. The latest methods do not solve the problem efficiently. The present note shows a new way to calculate templates by means of analytic functions. The discrete template is replaced by an analytic template, so operations are done with functions, not with points. In such a way, the computation time is reduced and the results can be used to solve other problems such as bounds computation     

Bayesian classification for data from the same unknown class

In this paper, we address the problem of how to classify a set of query vectors that belong to the same unknown class. Sets of data known to be sampled from the same class are naturally available in many application domains, such as speaker recognition. We refer to these sets as homologous sets. We show how to take advantage of homologous sets in classification to obtain improved accuracy over classifying each query vector individually. Our method, called homologous naive Bayes (HNB), is based on the naive Bayes classifier, a simple algorithm shown to be effective in many application domains. RNB uses a modified classification procedure that classifies multiple instances as a single unit. Compared with a voting method and several other variants of naive Bayes classification, HNB significantly outperforms these methods in a variety of test data sets, even when the number of query vectors in the homologous sets is small. We also report a successful application of HNB to speaker recognition. Experimental results show that HNB can achieve classification accuracy comparable to the Gaussian mixture model (GMM), the most widely used speaker recognition approach, while using less time for both training and classification.