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Speeding up the Consensus clustering methodology for microarray data analysis

algorithmS FOR MOLECULAR BIOLOGY 2011年第1期6卷 1-1页

作者： Giancarlo, Raffaele Utro, Filippo Univ Palermo Dipartimento Matemat & Informat I-90123 Palermo Italy IBM TJ Watson Res Ctr Yorktown Yeights NY USA

Background: The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e. g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of Consensus (Consensus clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, Consensus is a natural candidate for a speed-up. Results: Since the time-precision performance of Consensus depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for Consensus. That is, the closely related algorithm FC (Fast Consensus) that would have the same precision as Consensus with a substantially better time performance. The performance of FC has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, FC turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of Consensus. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guara

关键词： cluster algorithm Supplementary File cluster Solution Nonnegative Matrix Factorization Hierarchical cluster algorithm

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A systematic comparison of genome-scale clustering algorithms

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BMC BIOINFORMATICS 2012年第sup10期13卷 S7-S7页

作者： Jay, Jeremy J. Eblen, John D. Zhang, Yun Benson, Mikael Perkins, Andy D. Saxton, Arnold M. Voy, Brynn H. Chesler, Elissa J. Langston, Michael A. Univ Tennessee Knoxville TN 37996 USA Mississippi State Univ Mississippi State MS 39762 USA Linkoping Univ SE-58185 Linkoping Sweden Pioneer HiBred Int Inc Johnston IA 50131 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA Jackson Lab Bar Harbor ME 04609 USA

Background: A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods: For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each cluster's agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results: clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions: Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that furth

关键词： cluster algorithm Maximal Clique Rand Index Jaccard Similarity Average cluster Size

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Automatic summarization model based on clustering algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-6页

作者： Dai, Wenzhuo He, Qing Guizhou Univ Coll Big Data & Informat Engn Room 421Chongli BldgJiaxiu South RdWest Campus Guiyang 550025 Guizhou Peoples R China

Extractive document summary is usually seen as a sequence labeling task, which the summary is formulated by sentences from the original document. However, the selected sentences usually are high redundancy in semantic space, so that the composed summary are high semantic redundancy. To alleviate this problem, we propose a model to reduce the semantic redundancy of summary by introducing the cluster algorithm to select difference sentences in semantic space and we improve the base BERT to score sentences. We evaluate our model and perform significance testing using ROUGE on the CNN/DailyMail datasets compare with six baselines, which include two traditional methods and four state-of-art deep learning model. The results validate the effectiveness of our approach, which leverages K-means algorithm to produce more accurate and less repeat sentences in semantic summaries.

关键词： EDS cluster algorithm Semantic space

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Determining the trend of geometrical changes of a hydrotechnical object based on data in the form of LiDAR point clouds

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ARCHIVES OF CIVIL ENGINEERING 2024年第1期70卷 305-323页

作者： Kowalska, Maria Zaczek-Peplinska, Janina Piasta, Lukasz Warsaw Univ Technol Fac Geodesy & Cartog Pl Politech 1 PL-00661 Warsaw Poland

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult -to -interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the autho

关键词： LiDAR normal vectors point clouds terrestrial laser scanning deformations of hydrotech nical structures cluster algorithm

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A new gene-based association test for genome-wide association studies

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BMC proceedings 2009年第7期3 Suppl 7卷 S130页

作者： Alfonso Buil Angel Martinez-Perez Alexandre Perera-Lluna Leonor Rib Pere Caminal Jose Manuel Soria Unitat de Genomica de Malalties Complexes Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau Barcelona 08025 Spain. abuil@santpau.cat.

Genome-wide association studies are widely used today to discover genetic factors that modify the risk of complex diseases. Usually, these methods work in a SNP-by-SNP fashion. We present a gene-based test that can be applied in the context of genome-wide association studies. We compare both strategies, SNP-based and gene-based, in a sample of cases and controls for rheumatoid *** obtained different results using each strategy. The SNP-based test found the PTPN22 gene while the gene-based test found the PHF19-TRAF1-C5 region. That suggests that no single strategy performs better than another in all cases and that a certain underlying genetic architecture can be delineated more easily with one strategy rather than with another.

关键词： Rheumatoid Arthritis cluster algorithm Spectral cluster Ancestral Haplotype Rheumatoid Arthritis Susceptibility

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A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study

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PROTEOME SCIENCE 2011年第1期9卷 30-30页

作者： Albaum, Stefan P. Hahne, Hannes Otto, Andreas Haussmann, Ute Becher, Doerte Poetsch, Ansgar Goesmann, Alexander Nattkemper, Tim W. Univ Bielefeld Ctr Biotechnol CeBiTec D-4800 Bielefeld Germany Univ Bielefeld Fac Technol Biodata Min Grp D-4800 Bielefeld Germany Univ Bielefeld CeBiTec Bioinformat Resource Facil D-4800 Bielefeld Germany Tech Univ Munich Ctr Life & Food Sci Weihenstephan Chair Prote & Bioanalyt D-8000 Munich Germany Univ Greifswald Inst Microbiol D-17487 Greifswald Germany Ruhr Univ Bochum Bochum Germany

Background: Mass spectrometry-based proteomics has reached a stage where it is possible to comprehensively analyze the whole proteome of a cell in one experiment. Here, the employment of stable isotopes has become a standard technique to yield relative abundance values of proteins. In recent times, more and more experiments are conducted that depict not only a static image of the up-or down-regulated proteins at a distinct time point but instead compare developmental stages of an organism or varying experimental conditions. Results: Although the scientific questions behind these experiments are of course manifold, there are, nevertheless, two questions that commonly arise: 1) which proteins are differentially regulated regarding the selected experimental conditions, and 2) are there groups of proteins that show similar abundance ratios, indicating that they have a similar turnover? We give advice on how these two questions can be answered and comprehensively compare a variety of commonly applied computational methods and their outcomes. Conclusions: This work provides guidance through the jungle of computational methods to analyze mass spectrometry-based isotope-labeled datasets and recommends an effective and easy-to-use evaluation strategy. We demonstrate our approach with three recently published datasets on Bacillus subtilis [1,2] and Corynebacterium glutamicum [3]. Special focus is placed on the application and validation of cluster analysis methods. All applied methods were implemented within the rich internet application QuPE [4]. Results can be found at http://***.

关键词： cluster algorithm Hierarchical cluster Analysis cluster Number Abundance Ratio Corynebacterium Glutamicum

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Development of a genetic algorithm and its application to a bi-level problem of system cost optimal electricity price zone configurations

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ENERGY ECONOMICS 2021年 101卷 105422-105422页

作者： Felling, Tim Univ Duisburg Essen House Energy Markets & Finance Univ Str 12 D-45117 Duisburg Germany

The topic of alternative price zone configurations is frequently discussed in Central Western Europe where - so far - national borders coincide with borders of price zones. Reconfiguring these price zones is one option in order to improve congestion management, foster trading across borders of price zones and, thus, to increase welfare. In view of the significant increase in redispatch volumes and costs over the last years due to increasing feed-in from renewable energy sources in conjunction with delayed grid expansion, this topic has gained in importance. To determine these improved price zone configurations for a large-scale system like Central Western Europe, often either configurations based on expert guesses are considered or heuristics using approximate criteria like locational marginal prices are used to obtain price zones through clustering. In contrast, the present paper formulates a bi-level optimization problem of how to determine optimal configurations in terms of system costs and - given the size and nature of the problem - solves it with a specially developed genetic algorithm. Resulting price zone configurations are compared to both exogenously given, expert-based price zone configurations from the Entso-E bidding zone study and endogenously assessed configurations from a hierarchical cluster algorithm. Results show that the genetic algorithm achieves best results in terms of system costs. Moreover, the comparison with results from a hierarchical cluster analysis reveals important drawbacks of the latter methodology.

关键词： Price zone configuration Bidding zone configuration cluster algorithm Genetic algorithm Locational marginal prices

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A novel noise handling method to improve clustering of gene expression patterns

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BMC BIOINFORMATICS 2011年第s7期12卷 A3-A3页

作者： Bhattacharya, Anindya De, Rajat K. Univ Tennessee Hlth Sci Ctr Ctr Integrat & Translat Genom Memphis TN 38163 USA Indian Stat Inst Machine Intelligence Unit Kolkata India

An abstract of the article 'A Novel Noise Handling Method to Improve clustering of Gene Expression Patterns,' by Anindya Bhattacharya and Rajat K. De is presented.

关键词： Weighted Function cluster algorithm Similarity Measure Gene Expression Data Relevant Group

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Improved EM Method for Internet Traffic Classification

Improved EM Method for Internet Traffic Classification

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International Conference on Knowledge and Smart Technology

作者： Songyin Liu Jing Hu Shengnan Hao Tiecheng Song National Mobile Communications Research Laboratory Southeast University

ISBN: (纸本)9781467381406

Network traffic classification algorithm based on the machine learning has attracted more and more attention. Because the traditional EM algorithm has the disadvantage that the algorithm has the sensitivity of initial value and converge to local optimal point easily. This paper proposed a new improved EM algorithm based on the q-DAEM. The improved algorithm applies the EM algorithm to generate a constrained matrix, then combine the constrained matrix with the q-DAEM algorithm to reduce the search range, so that a better Gaussian mixture model can be derived from this algorithm. The algorithm was applied to the Moore datasets for evaluation, the experimental results show that this improved algorithm which applied to network traffic classification can lead to a higher precision and overall accuracy.

关键词： Expectation Maximization Gaussian mixture model Network traffic classification cluster algorithm

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A Smartphone-based System for Personal Data Management and Personality Analysis

A Smartphone-based System for Personal Data Management and P...

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IEEE International Conference on Ubiquitous Computing and Communications

作者： Ao Guo Jianhua Ma Graduate School of Computer and Information Sciences Hosei University Tokyo 184-0003 Japan

ISBN: (纸本)9781509001552

The data from or about an individual, called personal data, is continuously increasing due to popularity of smart phones, wearables and other ubiquitous devices. Such personal data can be used to model a user and even digitally clone a person, e.g., Cyber-I (cyber individual) that aims at creating a unique and comprehensive description for every individual to support various personalized services and applications. Due to heterogeneity and sensitivity of personal data, one important issue is how to effectively collect and manage person data with sufficient security protection. Another important issue is how to figure out an individual's character, i.e., personality from personal data. Therefore, this research is focused on personal data management and personality analysis in a smartphone based client-server system. The smartphone functions as not only a source of personal data but also a gateway to manage other wearables and communicate with a server that keeps personal data in a larger amount and a longer period. A multi-security mechanism is implemented to ensure data security in collection, transmission and storage. Personality analysis is made from data normalization, feature extraction and clustering, to personality computation based on sociological personality theories.

关键词： personal data personality system security cluster algorithm smartphone mongo

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