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Relevant bibliographies by topics / Biclusters

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Academic literature on the topic 'Biclusters'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Biclusters"

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YANG, JIONG, HAIXUN WANG, WEI WANG, and PHILIP S. YU. "AN IMPROVED BICLUSTERING METHOD FOR ANALYZING GENE EXPRESSION PROFILES." International Journal on Artificial Intelligence Tools 14, no. 05 (2005): 771–89. http://dx.doi.org/10.1142/s0218213005002387.

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Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church1 to capture the coherence of a subset of genes and a subset of conditions. A set of heuristic algorithms were also designed to either find one bicluster or a set of biclusters, which consist of iterations of masking null values and discovered biclusters, coarse and fine node deletion

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Bustamam, Alhadi, Titin Siswantining, Tesdiq P. Kaloka, and Olivia Swasti. "Application of BiMax, POLS, and LCM-MBC to Find Bicluster on Interactions Protein between HIV-1 and Human." Austrian Journal of Statistics 49, no. 3 (2020): 1–18. http://dx.doi.org/10.17713/ajs.v49i3.1011.

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Biclustering, in general, is a process of clustering genes and conditions simultaneously rather than clustering them separately. The purpose of biclustering is to discover a subset from experimental data. Further, biclustering results can be analyzed from a biological perspective. Biclustering can also be used for protein-protein interaction. In protein-protein interaction, biclustering can cluster interactions based on rows and columns. In this research, we applied three biclustering algorithms based on graph approach, Binary inclusion-Maximal (BiMax), local search framework based on pairs op

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Miao, Miao, Xue Qun Shang, Jia Cai Liu, and Miao Wang. "MRCluster: Mining Constant Row Bicluster in Gene Expression Data." Applied Mechanics and Materials 135-136 (October 2011): 628–33. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.628.

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Biclustering is one of the important techniques for gene expression data analysis. A bicluster is a set of genes coherently expressed for a set of biological conditions. Various biclustering algorithms have been proposed to find biclusters of different types. However, most of them are not efficient. We propose a novel algorithm MRCluster to mine constant row biclusters from real-valued dataset. MRCluster uses Apriori property and several novel pruning techniques to mine biclusters efficiently. We compare our algorithm with a recent approach RAP, and experimental results show that MRCluster is

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Wang, Miao, Xuequn Shang, Shaohua Zhang, and Zhanhuai Li. "Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth." International Journal of Knowledge Discovery in Bioinformatics 1, no. 4 (2010): 69–88. http://dx.doi.org/10.4018/jkdb.2010100104.

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DNA microarray technology has generated a large number of gene expression data. Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. It finds clusters of genes possessing similar characteristics together with biological conditions creating these similarities. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, the authors propose the FDCluster algorithm in order to mine frequent closed discriminative bicluster in multiple microarray

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Hu, Zhen, and Raj Bhatnagar. "Mining Low-Variance Biclusters to Discover Coregulation Modules in Sequencing Datasets." Scientific Programming 20, no. 1 (2012): 15–27. http://dx.doi.org/10.1155/2012/953863.

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High-throughput sequencing (CHIP-Seq) data exhibit binding events with possible binding locations and their strengths, followed by interpretation of the locations of peaks. Recent methods tend to summarize all CHIP-Seq peaks detected within a limited up and down region of each gene into one real-valued score in order to quantify the probability of regulation in a region. Applying subspace clustering techniques on these scores can help discover important knowledge such as the potential co-regulation or co-factor mechanisms. The ideal biclusters generated would contain subsets of genes and trans

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Liu, Xiangyu, Di Li, Juntao Liu, Zhengchang Su, and Guojun Li. "RecBic: a fast and accurate algorithm recognizing trend-preserving biclusters." Bioinformatics 36, no. 20 (2020): 5054–60. http://dx.doi.org/10.1093/bioinformatics/btaa630.

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Abstract Motivation Biclustering has emerged as a powerful approach to identifying functional patterns in complex biological data. However, existing tools are limited by their accuracy and efficiency to recognize various kinds of complex biclusters submerged in ever large datasets. We introduce a novel fast and highly accurate algorithm RecBic to identify various forms of complex biclusters in gene expression datasets. Results We designed RecBic to identify various trend-preserving biclusters, particularly, those with narrow shapes, i.e. clusters where the number of genes is larger than the nu

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Yin, Lu, Junlin Qiu, and Shangbing Gao. "Biclustering of Gene Expression Data Using Cuckoo Search and Genetic Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11 (2018): 1850039. http://dx.doi.org/10.1142/s0218001418500398.

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Biclustering analysis of gene expression data can reveal a large number of biologically significant local gene expression patterns. Therefore, a large number of biclustering algorithms apply meta-heuristic algorithms such as genetic algorithm (GA) and cuckoo search (CS) to analyze the biclusters. However, different meta-heuristic algorithms have different applicability and characteristics. For example, the CS algorithm can obtain high-quality bicluster and strong global search ability, but its local search ability is relatively poor. In contrast to the CS algorithm, the GA has strong local sea

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Panteli, Antiopi, Basilis Boutsinas, and Ioannis Giannikos. "On Set Covering Based on Biclustering." International Journal of Information Technology & Decision Making 13, no. 05 (2014): 1029–49. http://dx.doi.org/10.1142/s0219622014500692.

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In this paper, we present a clustering heuristic for solving demand covering models where the objective is to determine locations for servers that optimally cover a given set of demand points. This heuristic is based on the concept of biclusters and processes the set of demand points as well as the set of potential servers and determines biclusters that result in smaller problems. Given a coverage matrix, a bicluster is defined as a sub-matrix spanned by both a subset of rows and a subset of columns, such that rows are the most similar to each other when compared over columns. The algorithm st

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Zhang, Haokun, Yuanhua Shao, Weijun Chen, and Xin Chen. "Identifying Mitochondrial-Related Genes NDUFA10 and NDUFV2 as Prognostic Markers for Prostate Cancer through Biclustering." BioMed Research International 2021 (May 22, 2021): 1–15. http://dx.doi.org/10.1155/2021/5512624.

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Prostate cancer is currently associated with higher morbidity and mortality in men in the United States and Western Europe, so it is important to identify genes that regulate prostate cancer. The high-dimension gene expression profile impedes the discovery of biclusters which are of great significance to the identification of the basic cellular processes controlled by multiple genes and the identification of large-scale unknown effects hidden in the data. We applied the biclustering method MCbiclust to explore large biclusters in the TCGA cohort through a large number of iterations. Two biclus

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Xie, Juan, Anjun Ma, Yu Zhang, et al. "QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data." Bioinformatics 36, no. 4 (2019): 1143–49. http://dx.doi.org/10.1093/bioinformatics/btz692.

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Abstract Motivation The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed. Results We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), w

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Dissertations / Theses on the topic "Biclusters"

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Subramanian, Hema. "Summarization Of Real Valued Biclusters." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307442728.

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Fiaux, Patrick O. "Solving Intelligence Analysis Problems using Biclusters." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/31293.

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Analysts must filter through an ever-growing amount of data to obtain information relevant to their investigations. Looking at every piece of information individually is in many cases not feasible; there is hence a growing need for new filtering tools and techniques to improve the analyst process with large datasets. We present MineVis â an analytics system that integrates biclustering algorithms and visual analytics tools in one seamless environment. The combination of biclusters and visual data glyphs in a visual analytics spatial environment enables a novel type of filtering. This design

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Banerjee, Abhik. "Discovery of overlapping 1-closed biclusters." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1352396960.

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Oliveira, Saullo Haniell Galvão de 1988. "On biclusters aggregation and its benefits for enumerative solutions = Agregação de biclusters e seus benefícios para soluções enumerativas." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259072.

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Orientador: Fernando José Von Zuben<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-27T03:28:44Z (GMT). No. of bitstreams: 1 Oliveira_SaulloHaniellGalvaode_M.pdf: 1171322 bytes, checksum: 5488cfc9b843dbab6d7a5745af1e3d4b (MD5) Previous issue date: 2015<br>Resumo: Biclusterização envolve a clusterização simultânea de objetos e seus atributos, definindo mo- delos locais de relacionamento entre os objetos e seus atributos. Assim como a clusterização, a biclusterização tem uma vasta gama de apl

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Owens, Clifford Conley. "Mining Truth Tables and Straddling Biclusters in Binary Datasets." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/35745.

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As the world swims deeper into a deluge of data, binary datasets relating objects to properties can be found in many different fields. Such datasets abound in practically any area of interest, including biology, politics, entertainment, and education. This explosion calls for the definition of new types of patterns in binary data, as well as algorithms to find efficiently find these patterns. In this work, we introduce truth tables as a new class of patterns to be mined in binary datasets. Truth tables represent a subset of properties which exhibit maximal variability (and hence, suggest ind

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Sun, Maoyuan. "Visual Analytics with Biclusters: Exploring Coordinated Relationships in Context." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72890.

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Exploring coordinated relationships is an important task in data analytics. For example, an intelligence analyst may want to find three suspicious people who all visited the same four cities. However, existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. This work presents a visual analytics approach that applies biclusters to support coordinated relationships exploration. Each computed bicluster aggregates individual r

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Jin, Ying. "New Algorithms for Mining Network Datasets: Applications to Phenotype and Pathway Modeling." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/40493.

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Biological network data is plentiful with practically every experimental methodology giving â network viewsâ into cellular function and behavior. Bioinformatic screens that yield network data include, for example, genome-wide deletion screens, protein-protein interaction assays, RNA interference experiments, and methods to probe metabolic pathways. Efficient and comprehensive computational approaches are required to model these screens and gain insight into the nature of biological networks. This thesis presents three new algorithms to model and mine network datasets. First, we present an a

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Kumar, Lalit. "Scalable Map-Reduce Algorithms for Mining Formal Concepts and Graph Substructures." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543996580926452.

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Lourenço, Artur Pedro Duarte Reis Bastos. "A new methodology for the analysis and validation of clusters and biclusters of genes." Master's thesis, 2006. http://hdl.handle.net/10451/14049.

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Tese de mestrado em Bioinformática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2006<br>A era da pós-genómica e das tecnologias de larga escala traz consigo a necessidade de desenvolver novos métodos para lidar com grandes quantidades de dados. Para tal, têm sido aplicados algoritmos de clustering e biclustering em bioinformática para descobrir padrões em dados biológicos. A validação dos resultados de clustering e de biclustering é essencial para a sua análise. Esta dissertação propõe uma nova metodologia para validar resultados de clustering e biclustering. A meto

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Silva, Miguel Miranda Garção da. "User-Specific Bicluster-based Collaborative Filtering." Master's thesis, 2020. http://hdl.handle.net/10451/48316.

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Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020<br>Collaborative Filtering is one of the most popular and successful approaches for Recommender Systems. However, some challenges limit the effectiveness of Collaborative Filtering approaches when dealing with recommendation data, mainly due to the vast amounts of data and their sparse nature. In order to improve the scalability and performance of Collaborative Filtering approaches, several authors proposed successful approaches combining Collaborative Filtering with clustering techniques. In this work, we

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Books on the topic "Biclusters"

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Ismailov, Nariman, Samira Nadzhafova, and Aygyun Gasymova. Bioecosystem complexes for the solution of environmental, industrial and social problems (on the example of Azerbaijan). INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1043239.

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A key objective of the modern development of society is the observance of ecological and socio-economic unity in human life  and comprehensive improvement of environment and quality of life should be considered in close connection with the quality of the natural landscape. The formation of scientific understanding of the unity of society and nature is driven by the need for practical implementation of such unity. This defines the focus of this monograph. Given the overall assessment of the current state of the environment in Azerbaijan, considers the scenarios for the future development

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Book chapters on the topic "Biclusters"

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Lonardi, Stefano, Wojciech Szpankowski, and Qiaofeng Yang. "Finding Biclusters by Random Projections." In Combinatorial Pattern Matching. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27801-6_8.

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Xu, Xiaohua, Ping He, Lin Lu, Yanqiu Xi, and Zhoujin Pan. "Finding k-Biclusters from Gene Expression Data." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31576-3_55.

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Menezes, Lara, and André L. V. Coelho. "Mining Coherent Biclusters with Fish School Search." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_70.

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Palacios, Pablo, David Pelta, and Armando Blanco. "Obtaining Biclusters in Microarrays with Population-Based Heuristics." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11732242_11.

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Bagyamani, J., K. Thangavel, and R. Rathipriya. "Extraction of Optimal Biclusters from Gene Expression Data." In Information and Communication Technologies. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15766-0_59.

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Michalak, Marcin, and Magdalena Stawarz. "Generating and Postprocessing of Biclusters from Discrete Value Matrices." In Computational Collective Intelligence. Technologies and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23935-9_10.

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Gil-Cumbreras, Francisco Javier, Raúl Giráldez, and Jesús S. Aguilar-Ruiz. "Extending Probabilistic Encoding for Discovering Biclusters in Gene Expression Data." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32034-2_59.

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Kingcade, J. E., I. Shim, and K. A. Gingerich. "Stability of Small Biclusters of Transition Metals with Semi-Conductors." In Metal-Metal Bonds and Clusters in Chemistry and Catalysis. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-2492-6_41.

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Michalak, Marcin. "Induction of Centre-Based Biclusters in Terms of Boolean Reasoning." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31964-9_23.

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Maji, Pradipta, and Sushmita Paul. "Possibilistic Biclustering for Discovering Value-Coherent Overlapping $$\delta $$ δ -Biclusters." In Scalable Pattern Recognition Algorithms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05630-2_10.

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Conference papers on the topic "Biclusters"

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P. Pinto-Roa, Diego, Hernán Medina, Federico Román, et al. "Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption." In 2nd International Conference on Machine Learning &Trends (MLT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111110.

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The discovery and description of patterns in electric energy consumption time series is fundamental for timely management of the system. A bicluster describes a subset of observation points in a time period in which a consumption pattern occurs as abrupt changes or instabilities homogeneously. Nevertheless, the pattern detection complexity increases with the number of observation points and samples of the study period. In this context, current bi-clustering techniques may not detect significant patterns given the increased search space. This study develops a parallel evolutionary computation s

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Tchagang, Alain, Ahmed Tewfik, and Amy Skubitz. "Analysis of order preserving genes biclusters." In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353176.

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Lakshmi, K. Iswarya, and C. P. Chandran. "Mining coregulated biclusters from gene expression data." In 2012 International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME). IEEE, 2012. http://dx.doi.org/10.1109/icprime.2012.6208292.

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Giraldez, Raul, Federico Divina, Beatriz Pontes, and Jesus S. Aguilar-Ruiz. "Evolutionary Search of Biclusters by Minimal Intrafluctuation." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295631.

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Menezes, Lara, and Andre L. V. Coelho. "On ensembles of biclusters generated by NichePSO." In 2011 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2011. http://dx.doi.org/10.1109/cec.2011.5949674.

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Hanna, Eileen Marie, and Nazar M. Zaki. "Detecting protein complexes using gene expression biclusters." In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2015. http://dx.doi.org/10.1109/cibcb.2015.7300271.

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Aguilar-Ruiz, Jesús S., and Federico Divina. "GA-based approach to discover meaningful biclusters." In the 2005 conference. ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068086.

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Sun, Maoyuan, Lauren Bradel, Chris L. North, and Naren Ramakrishnan. "The role of interactive biclusters in sensemaking." In CHI '14: CHI Conference on Human Factors in Computing Systems. ACM, 2014. http://dx.doi.org/10.1145/2556288.2557337.

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Mukhopadhyay, A., U. Maulik, and S. Bandyopadhyay. "Discovering Coherent Biclusters from Microarray Gene Expression Data." In Artificial Intelligence and Applications. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.674-112.

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To, Cuong, and Alan Wee-Chung Liew. "Genetic algorithm based detection of general linear biclusters." In 2014 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2014. http://dx.doi.org/10.1109/icmlc.2014.7009667.

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