Integration of Genomic Data for Inferring Protein Complexes from Global Protein–Protein Interaction Networks

Protein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. One important objective of modern biology is the extraction of functional modules, such as protein complexes from global protein interaction networks. This paper describes how seven genomic features and four experimental interaction data sets were combined using a Bayesian-networks-based data integration approach to infer PPI networks in yeast. Greater coverage and higher accuracy were achieved than in previous high-throughput studies of PPI networks in yeast. A Markov clustering algorithm was then used to extract protein complexes from the inferred protein interaction networks. The quality of the computed complexes was evaluated using the hand-curated complexes from the Munich Information Center for Protein Sequences database and gene-ontology-driven semantic similarity. The results indicated that, by integrating multiple genomic information sources, a better clustering result was obtained in terms of both statistical measures and biological relevance.     

Finding Outbreak Trees in Networks with Limited Information

Real-time control of infectious disease outbreaks represents one of the greatest epidemiological challenges currently faced. In this paper we address the problem of identifying contagion patterns responsible for the spread of a disease in a network, which can be applied in real-time to evaluate an ongoing outbreak. We focus on the scenario where limited information, i.e. infection reports which may or may not include the actual source, is available during an ongoing outbreak and we seek the most likely infection tree that spans at least a set of known infected nodes. This problem can be represented using a maximum likelihood constrained Steiner tree model where the objective is to find a spanning tree with an assignment of integer nodes weights. We propose a novel formulation and solution method based on a two-step heuristic which (1) reduces the initial graph using a polynomial time algorithm designed to find feasible infection paths and (2) solves an exact mixed integer linear programming reformulation of the maximum likelihood model on the resulting subgraph. The proposed methodology can be applied to outbreaks which may evolve from multiple sources. Simulated contagion episodes are used to evaluate the performance of our solution method. Our results show that the approach is computationally efficient and is able to reconstruct a significant proportion of the outbreak, even in the context of low levels of information availability.     

Detecting Overlapping Community Structures in Networks

Community structure has been recognized as an important statistical feature of networked systems over the past decade. A lot of work has been done to discover isolated communities from a network, and the focus was on developing of algorithms with high quality and good performance. However, there is less work done on the discovery of overlapping community structure, even though it could better capture the nature of network in some real-world applications. For example, people are always provided with varying characteristics and interests, and are able to join very different communities in their social network. In this context, we present a novel overlapping community structures detecting algorithm which first finds the seed sets by the spectral partition and then extends them with a special random walks technique. At every expansion step, the modularity function Q is chosen to measure the expansion structures. The function has become one of the popular standards in community detecting and is defined in Newman and Girvan (Phys. Rev. 69:026113, 2004). We also give a theoretic analysis to the whole expansion process and prove that our algorithm gets the best community structures greedily. Extensive experiments are conducted in real-world networks with various sizes. The results show that overlapping is important to find the complete community structures and our method outperforms the C-means in quality.     

Pathway Preserving Representation of Metabolic Networks

Improvements in biological data acquisition and genomes sequencing now allow to reconstruct entire metabolic networks of many living organisms. The size and complexity of these networks prohibit manual drawing and thereby urge the need of dedicated visualization techniques. An efficient representation of such a network should preserve the topological information of metabolic pathways while respecting biological drawing conventions. These constraints complicate the automatic generation of such visualization as it raises graph drawing issues. In this paper we propose a method to lay out the entire metabolic network while preserving the pathway information as much as possible. That method is flexible as it enables the user to define whether or not node duplication should be performed, to preserve or not the network topology. Our technique combines partitioning, node placement and edge bundling to provide a pseudo‐orthogonal visualization of the metabolic network. To ease pathway information retrieval, we also provide complementary interaction tools that emphasize relevant pathways in the entire metabolic context.     

Finding the Iris Using Convolutional Neural Networks

Journal of Computer and Systems Sciences International - Iris-based identification is a well-known and widely used technology. However, its use in mobile devices is limited due to the algorithmic...     

Finding Reliable Shortest Paths in Road Networks Under Uncertainty

The aim of this study is to investigate the solution algorithm for solving the problem of determining reliable shortest paths in road networks with stochastic travel times. The availability of reliable shortest paths enables travelers, in the face of travel time uncertainty, to plan their trips with a pre-specified on-time arrival probability. In this study, the reliable shortest path between origin and destination nodes is determined using a multiple-criteria shortest path approach when link travel times follow normal distributions. The dominance conditions involved in such problems are established, thereby reducing the number of generated non-dominated paths during the search processes. Two solution algorithms, multi-criteria label-setting and A* algorithms, are proposed and their complexities analyzed. Computational results using large scale networks are presented. Numerical examples using data from a real-world advanced traveller information system is also given to illustrate the applicability of the solution algorithms in practice.     

A Two-Criterion Lexicographic Algorithm for Finding All Shortest Paths in Networks

An algorithm for finding all shortest paths in an undirected network is considered. The following two criteria are used: the minimum number of arcs in a path and a minimum path length. The algorithm is analyzed for complexity, and it is empirically shown that, with increasing the network density, its computational efficiency becomes higher than that of the Floyd algorithm adequately modified to find the shortest path with the use of a two-step criterion.     

Finding the Consensus Shape for a Protein Family

We define and prove properties of the consensus shape for a protein family, a protein-like structure that provides a compact summary of the significant structural information for a protein family. If all members of the protein family exhibit a geometric relationship between corresponding α-carbons, then that relationship is preserved in the consensus shape. In particular, distances and angles that are consistent across family members are preserved. For the consensus shape, the spacing between successive α-carbons is variable, with small distances in regions where the members of the protein family exhibit significant variation and large distances (up to the standard spacing of about 3.8 Å) in regions where the family members agree. Despite this non-protein-like characteristic, the consensus shape preserves and highlights important structural information. We describe an iterative algorithm for computing the consensus shape and prove that the algorithm converges. As a by-product we produce a multiple structure alignment for the family members. We present the results of experiments in which we build consensus shapes for several known protein families.     

Finding the Consensus Shape for a Protein Family

We define and prove properties of the consensus shape for a protein family, a protein-like structure that provides a compact summary of the significant structural information for a protein family. If all members of the protein family exhibit a geometric relationship between corresponding -carbons, then that relationship is preserved in the consensus shape. In particular, distances and angles that are consistent across family members are preserved. For the consensus shape, the spacing between successive -carbons is variable, with small distances in regions where the members of the protein family exhibit significant variation and large distances (up to the standard spacing of about 3.8 Å) in regions where the family members agree. Despite this non-protein-like characteristic, the consensus shape preserves and highlights important structural information. We describe an iterative algorithm for computing the consensus shape and prove that the algorithm converges. As a by-product we produce a multiple structure alignment for the family members. We present the results of experiments in which we build consensus shapes for several known protein families.     

Evolutionary Dynamics on Scale-Free Interaction Networks

There has been a recent surge of interest in studying dynamical processes, including evolutionary optimization, on scale-free topologies. While various scaling parameters and assortativities have been observed in natural scale-free interaction networks, most previous studies of dynamics on scale-free graphs have employed a graph-generating algorithm that yields a topology with an uncorrelated degree distribution and a fixed scaling parameter. In this paper, we advance the understanding of selective pressure in scale-free networks by systematically investigating takeover times under local uniform selection in scale-free topologies with varying scaling exponents, assortativities, average degrees, and numbers of vertices. We demonstrate why the so-called characteristic path length of a graph is a nonlinear function of both scaling and assortativity. Neither the eigenvalues of the adjacency matrix nor the effective population size was sufficient to account for the variance in takeover times over the parameter space that was explored. Rather, we show that 97% of the variance of logarithmically transformed average takeover times, on all scale-free graphs tested, could be accounted for by a planar function of: 1) the average inverse degree (which captures the effects of scaling) and 2) the logarithm of the population size. Additionally, we show that at low scaling exponents, increasingly positive assortativities increased the variability between experiments on different random graph instances, while increasingly negative assortativities increased the variability between takeover times from different initial conditions on the same graph instances. We explore the mechanisms behind our sometimes counterintuitive findings, and discuss potential implications for evolutionary computation and other relevant disciplines.      

用泛函网络求任意高阶多项式的近似根

本文提出了一种新的基于泛函网络的多项式求根模型及学习算法,而泛函网络的参数利用解线性不等式组,可得到所求任意高阶多项式近似根的一般参数表达式。文章还讨论了基于泛函网络的多项式求根学习算法实现的一些技术问题,相对传统方法,能够有效地获得任意多项式对应根的参数表达式。     

Building Small-World Peer-to-Peer Networks Based on Hierarchical Structures

Small-world (SW) networks possess two properties, namely low diameter and high clustering coefficient, which are often desired by large-scale peer-to-peer networks. Prior studies have shown that the construction of an SW network can be based on a d-regular graph, and each node in the graph maintains d local neighbors and a small constant number of long-distance contacts. However, it is commonly understood that it is difficult to construct a short route in an SW network, given source (s) and target (t) nodes, though an SW network guarantees that a short route from s to t exists. Prior work in [1] proposed a navigable SW network for a d-dimensional lattice such that a simple localized routing algorithm can be devised to route a message from s to t using O(log^{2}{cal X}) hops, where {cal X} is the number of nodes in the network. In this paper, we present a novel navigable SW network based on a hierarchical model. Compared to previous efforts, the novelty of our study presents 1) that our network construction based on a hierarchical model is decentralized, 2) that routing a message between any two nodes in our SW network takes logarithmic hopcount in expectation, 3) that our SW network has high cluster coefficient, and 4) that the performance of our proposal is mathematically provable. We support the performance of our proposal in this study through rigorous, thorough performance analysis and extensive simulations.     

基于球极坐标划分的蛋白质结构相似性比较

为了指导新药探索,减少耗费过高的实验次数,需要有一个准确、快速的分子相似性判定方法来选择待比较的分子.为此提出一种蛋白质结构相似性比较方法,使用空间球极坐标3个分量划分蛋白质所在区域,得到蛋白质基本组成元素的空间密度特征,进而判定2个蛋白质结构的相似性.实验结果表明,该方法可作为各种结构蛋白质相似性比较的辅助手段,不仅能较好地反映蛋白质分子的空间结构特征,而且还有令人满意的时间复杂性,对大分子的相似性比较及进一步分类将有重要意义.     

蛋白质作用网络中模体识别技术研究

生物网络是利用网络理论对生物系统进行建模,从而借助于网络的概念、属性和复杂网络研究的各种方法来理解生物系统的演化和行为。生物网络是生物信息学中一个崭新的研究领域,特别是蛋白质作用网络中网络模体具有很重要的生物意义。网络模体为在某个网络的多个不同部分出现的相互连接的子结构,其表达程度明显高于在随机网络中的表达。文中对模体识别技术进行了研究,系统阐述了模体识别技术的研究现状和各种技术方法,展望了模体识别技术的未来研究方向。识别大模体及将模体跟功能相结合将是该领域的发展方向。     

基于SNMP的网络拓扑发现与拓扑生成树的绘制

提出了基于SNMP的网络拓扑发现算法,给出了拓扑生成树的数据结构和绘制算法,并对指定子网存活主机的发现做出了算法改进,给出了对比结果。     

Oian基于神经网络的蛋白质序列分类研究

提出了基于Levenberg-Marquardt(LM)算法的BP神经网络对蛋白质序列进行家族分类的新方法．该方法采用二肽含量对蛋白质序列进行特征提取,根据影响因子评价特征的相对重要性,用改进的BP神经网络LM优化算法构造一个三层人工神经网络,通过对PIR数据库中三类家族的学习,该网络对未知蛋白质序列分类的准确率分别达到了98．9％．98．1％,97．8％。     

Application of Cascade Correlation Networks for Structures to Chemistry

We present the application of Cascade Correlation for structures to QSPR (quantitative structure-property relationships) and QSAR (quantitative structure-activity relationships) analysis. Cascade Correlation for structures is a neural network model recently proposed for the processing of structured data. This allows the direct treatment of chemical compounds as labeled trees, which constitutes a novel approach to QSPR/QSAR. We report the results obtained for QSPR on Alkanes (predicting the boiling point) and QSAR of a class of Benzodiazepines. Our approach compares favorably versus the traditional QSAR treatment based on equations and it is competitive with ad hoc MLPs for the QSPR problem.