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Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

BMC BIOINFORMATICS 2011年第1期12卷 1-10页

作者： Kritikos, George D. Moschopoulos, Charalampos Vazirgiannis, Michalis Kossida, Sophia Acad Athens Bioinformat & Med Informat Team Biomed Res Fdn GR-11527 Athens Greece Athens Univ Econ & Business Dept Informat GR-10434 Athens Greece Univ Patras Pattern Recognit Lab Dept Comp Engn & Informat GR-26500 Patras Rio Greece Ecole Telecom ParisTech Inst Telecom Dept Informat & Reseaux Paris France

Background: Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied. Results: We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database. Conclusions: The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.

关键词： cluster algorithm Positive Predictive Value Weighting Scheme Spectral cluster Protein Interaction Network

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A method for calculating two-dimensional spatially extension distances and its clustering algorithm

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Procedia Computer Science 2023年 221卷 1187-1193页

作者： Yaocong Qin Xingsen Li Research Institute of Extenics and Innovation Methods GuangZhou 510006 China

This paper presents a novel method for calculating the extension distance between a point and a two-dimensional convex set. The proposed method builds upon existing techniques by employing a line traversal approach. By traversing the convex set with a line originating from the point of interest, the extension distance is computed based on the intersection points between the line and the set along the two coordinate axes. The weights assigned to the intersection points play a crucial role in the distance calculation. By summing the extension distances in both directions, the overall extension distance of the point with respect to the set is obtained. Furthermore, a clustering algorithm is introduced, which incorporates the concept of an interaction factor commonly used in engineering experiments. The multidimensional data is projected onto feature planes created by combining pairs of features. The calculation method for extension distances between points and convex sets in two dimensions is applied to determine the distances between points and cluster classes (or cluster centers). The distances on each feature plane are then weighted, summed, and analyzed to establish the relationships between points and cluster classes, thereby achieving clustering. The feasibility of the proposed algorithm is validated through comparative experiments k-means algorithm on the iris dataset and the wine dataset. The experimental results confirm the effectiveness and viability of the proposed approach.

关键词： extenics entenics distance cluster algorithm

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Investigating the impact of stop network designs on DRT operation

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Procedia Computer Science 2024年 238卷 742-748页

作者： Dariush Tamleh Tilmann Schlenther Kai Nagel Transport Systems Planning and Transport Telematics Technische Universität Berlin Kaiserin-Augusta-Allee 104 10553 Berlin Germany

Modern on-demand mobility concepts have the potential to drive the mobility transition. Increasing attention is being paid to DRT (Demand Responsive Transportation) services that enable the bundling of trips with the help of dynamic routing algorithms. Many providers use a stop-based operating concept for this purpose. The transport between so-called virtual stops promises a higher fraction of shared trips and shorter vehicle detours. The aim of this paper is to investigate multiple stop network designs and their impacts on the efficiency of the operation. For this purpose, three stop network generation approaches were derived from literature and applied to the simulation environment MATSim. A scenario in the city center of Berlin is simulated, where all inner car trips are replaced by DRT. Comparing the stop network design against a door-to-door service, the results suggest a trade-off between a reduction of vehicle kilometers (up to 18%) and increased walking distances (up to 250 meters on average).

关键词： Demand-responsive transport Stop Network Transport Simulation cluster algorithm Berlin

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Merged consensus clustering to assess and improve class discovery with microarray data

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BMC BIOINFORMATICS 2010年第1期11卷 590-590页

作者： Simpson, T. Ian Armstrong, J. Douglas Jarman, Andrew P. Univ Edinburgh Ctr Integrat Physiol Genes & Dev Grp Edinburgh EH8 9XD Midlothian Scotland

Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a large number of methods available to perform clustering, but it is often unclear which method is best suited to the data and how to quantify the quality of the classifications produced. Results: Here we describe an R package containing methods to analyse the consistency of clustering results from any number of different clustering methods using resampling statistics. These methods allow the identification of the the best supported clusters and additionally rank cluster members by their fidelity within the cluster. These metrics allow us to compare the performance of different clustering algorithms under different experimental conditions and to select those that produce the most reliable clustering structures. We show the application of this method to simulated data, canonical gene expression experiments and our own novel analysis of genes involved in the specification of the peripheral nervous system in the fruitfly, Drosophila melanogaster. Conclusions: Our package enables users to apply the merged consensus clustering methodology conveniently within the R programming environment, providing both analysis and graphical display functions for exploring clustering approaches. It extends the basic principle of consensus clustering by allowing the merging of results between different methods to provide an averaged clustering robustness. We show that this extension is useful in correcting for the tendency of clustering algorithms to treat outliers differently within datasets. The R package, clusterCons, is freely available at CRAN and sourceforge under the GNU public licence.

关键词： cluster algorithm cluster Structure cluster Number Consensus cluster Classify Gene Expression

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Efficient Simulation of Resistively Shunted Josephson Junctions

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Physical Review Letters 2005年第6期95卷 060201-060201页

作者： Philipp Werner Matthias Troyer Institut für theoretische Physik ETH Hönggerberg CH-8093 Zürich Switzerland

We present a cluster algorithm for resistively shunted Josephson junctions or similar physical systems, which dramatically improves sampling efficiency, and apply it to the superconductor-to-metal transition in a single junction. Measuring the temperature dependence of the zero bias resistance, we confirm that the critical point does not depend on the strength of the Josephson coupling. However, we find that the correlation exponents vary continuously along the phase boundary, indicating that the Schmid-Bulgadaev transition is a line of fixed points.

关键词： .Phase Boundary junctions cluster algorithm Resistively shunted

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Predicting Wafer-Level Package Reliability Life Using Mixed Supervised and Unsupervised Machine Learning algorithms

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MATERIALS 2022年第11期15卷 3897-3897页

作者： Su, Qing-Hua Chiang, Kuo-Ning Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 300 Taiwan

With the increasing demand for electronic products, the electronic package gradually developed toward miniaturization and high density. The most significant advantage of the Wafer-Level Package (WLP) is that it can effectively reduce the volume and footprint area of the package. An important issue in the design of WLP is how to quickly and accurately predict the reliability life under the accelerated thermal cycling test (ATCT). If the simulation approach is not adopted, it usually takes several ACTCs to design a WLP, and each ACTC will take several months to get the reliability life results, which increases development time considerably. However, simulation results may differ depending on the designer's domain knowledge, ability, and experience. This shortcoming can be overcome with artificial intelligence (AI). In this study, finite element analysis (FEA) is combined with machine learning algorithms, e.g., Kernel Ridge Regression (KRR), to create an AI model for predicting the reliability life of electronic packaging. Kernel Ridge Regression (KRR) combined with the K-means cluster algorithm provides a highly accurate and efficient way to obtain AI models for large-scale data sets.

关键词： Wafer-Level Package (WLP) Finite Element Analysis (FEA) machine learning Kernel Ridge Regression (KRR) cluster algorithm

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Knowledge guided Bayesian classification for dynamic multi-objective optimization

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Ye, Yulong Li, Lingjie Lin, Qiuzhen Wong, Ka-Chun Li, Jianqiang Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Dynamic multi-objective optimization problems (DMOPs) typically contain multiple conflicting objectives that vary over time, requiring the optimization algorithms to quickly track the changing Pareto-optimal solutions (POS). Recently, prediction-based dynamic multi-objective evolutionary algorithms (DMOEAs) using transfer learning ideas have been considered promising, which could accelerate the search towards the POS by transferring information from previous environments to estimate the location of the POS in the next environment. However, most of the existing methods only transfer the search experiences from one or two previous environments to construct the prediction model for the next moment, which may ignore the effective information in the earlier search process thereby degrading the prediction accuracy in some cases. In this paper, a knowledge-guided Bayesian classification for DMOEA, called KGB-DMOEA, is proposed, which aims to achieve a robust prediction by fully exploiting the information from all historical environments. Particularly, when an environmental change is detected, a knowledge reconstruction-examination strategy is designed to divide all historical optimal solutions into useful and useless ones, which are used as positive and negative samples, respectively, for training the prediction model in subsequent steps. Then, a non -linear probabilistic classifier, i.e., naive Bayesian classifier, is constructed by using the above training samples, which can fully mine the effective knowledge from all historical environments and predict a high-quality initial population for the new environment. Experimental results on multiple DMOPs test suits demonstrate that KGB-DMOEA is superior to several state-of-the-art DMOEAs in solving various DMOPs. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Dynamic multi-objective optimization Naive Bayesian classification Evolutionary algorithm cluster algorithm

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CoXpress: differential co-expression in gene expression data

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BMC BIOINFORMATICS 2006年第1期7卷 509-509页

作者： Watson, Michael Inst Anim Hlth Informat Grp Newbury RG20 7NN Berks England

Background: Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed. Results: We have developed coXpress as a means of identifying groups of genes that are differentially co-expressed. The utility of coXpress is demonstrated using two publicly available microarray datasets. Our software identifies several groups of genes that are highly correlated under one set of biologically related experiments, but which show little or no correlation in a second set of experiments. The software uses a re-sampling method to calculate a p-value for each group, and provides several methods for the visualisation of differentially co-expressed genes. Conclusion: coXpress can be used to find groups of genes that display differential co-expression patterns in microarray datasets.

关键词： cluster algorithm Hierarchical cluster Analysis Acute Myeloid Leukaemia Acute Lymphoblastic Leukaemia Microarray Dataset

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clusterMaker: a multi-algorithm clustering plugin for Cytoscape

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BMC BIOINFORMATICS 2011年第1期12卷 1-14页

作者： Morris, John H. Apeltsin, Leonard Newman, Aaron M. Baumbach, Jan Wittkop, Tobias Su, Gang Bader, Gary D. Ferrin, Thomas E. Univ Calif San Francisco Dept Pharmaceut Chem San Francisco CA 94143 USA Stanford Univ Sch Med Inst Stem Cell Biol & Regenerat Med Stanford CA 94305 USA Buck Inst Age Res Novato CA USA Max Planck Inst Informat Saarbrucken Germany Univ Michigan Bioinformat Program Ann Arbor MI 48109 USA Univ Michigan Natl Ctr Integrat Biomed Informat Ann Arbor MI 48109 USA Univ Toronto Donnelly Ctr Toronto ON Canada Univ Toronto Dept Mol Genet Toronto ON Canada Univ Calif San Francisco Dept Bioengn & Therapeut Sci San Francisco CA 94143 USA

Background: In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL. Results: Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types;the identification of protein complexes in the yeast Saccharomyces cerevisiae;and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions: The Cytoscape plugin clusterMaker provides a number of clustering

关键词： cluster algorithm Fuzzy cluster Tree View Uncentered Correlation Genetic Interaction Data

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Agent-based Control System Methodology for Reconfigurable Bending Press Machine

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Procedia CIRP 2016年 57卷 362-367页

作者： Olukorede Tijani Adenuga Khumbulani Mpofu Adeyeri Michael Kanisuru Tshwane University of Technology Industrial Engineering Dept Pretoria 0001 South Africa

In the present day technology creation, automation is a promising technology towards the achievement of constructive, innovative and sustainable designs and products in industry 4. The control system of machines that involves the application of mechatronic objects or intelligent units which are envisioned as building blocks for design of the systems are rather configured than being designed. In order to meet dynamic customer needs, within minimal time frames, thereby achieving short-time-to-market. The crucial element in this scenario is that the customer drives the pace and direction of the manufacturing entity. The reality of the matter is that the manufacturer needs to produce customer-centric designed products, and this may be achieved through the use of machinery and a system configured to satisfy the need. Realizing this scenario, in this article, an automated agent-based control system methodology (ACSME) has been proposed for Reconfigurable Bending Press Machine (RBPM) application due to ongoing research. The proposed methodology will help manufacturer of RBPM to address the need for more flexible control systems and to demonstrate their industrial flexibility in several reconfigurable machines applications.

关键词： cluster algorithm Agent-based Approach Agent-based Control System Methodology (ACSME) Reconfigurable Bending Press Machine (RBPM)

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