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Academic literature on the topic 'Bicluter'

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Published: 14 March 2023

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Journal articles on the topic "Bicluter"

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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 (October 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 datasets. FDCluster uses Apriori property and several novel techniques for pruning to mine biclusters efficiently. To increase the space usage, FDCluster also utilizes several techniques to generate frequent closed bicluster without candidate maintenance in memory. The experimental results show that FDCluster is more effective than traditional methods in either single micorarray dataset or multiple microarray datasets. This paper tests the biological significance using GO to show the proposed method is able to produce biologically relevant 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 (October 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, node addition, and the inclusion of inverted data. These heuristics inevitably suffer from some serious drawback. The masking of null values and discovered biclusters with random numbers may result in the phenomenon of random interference which in turn impacts the discovery of high quality biclusters. To address this issue and to further accelerate the biclustering process, we generalize the model of bicluster to incorporate null values and propose a probabilistic algorithm (FLOC) that can discover a set of k possibly overlapping biclusters simultaneously. Furthermore, this algorithm can easily be extended to support additional features that suit different requirements at virtually little cost. Experimental study on the yeast gene expression data2 shows that the FLOC algorithm can offer substantial improvements over the previously proposed algorithm.
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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 (February 20, 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 operation (POLS), and (LCM-MBC) to clustering data of protein-protein interaction between HIV-1 and human. We change the interaction protein-protein interaction data into binary then divided into two datasets called HV positive and HV negative. Then compare the biclustering results of each dataset using heatmap and analyze them with GO terms. From dataset HV positive, BiMax found 30 biclusters, LCM-MBC 31 biclusters, and POLS 13 biclusters. From dataset HV negative, BiMax found eight biclusters, LCM-MBC 14 bicluster, and POLS 10 biclusters. Based on the results of the heatmap, all bicluster entry from BiMax is a protein that interacts, whereas biclusters entry of LCM-MBC and POLS still have proteins that do not interact. It can be concluded that BiMax algorithm is good for clustering protein-protein interaction, especially for binary data.
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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 (July 24, 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 search ability, but its global search ability is poor. In order to not only improve the global search ability of a bicluster and its coverage, but also improve the local search ability of the bicluster and its quality, this paper proposed a meta-heuristic algorithm based on GA and CS algorithm (GA-CS Biclustering, Georgia Association of Community Service Boards (GACSB)) to solve the problem of gene expression data clustering. The algorithm uses the CS algorithm as the main framework, and uses the tournament strategy and the elite retention strategy based on the GA to generate the next generation of the population. Compared with the experimental results of common biclustering analysis algorithms such as correlated correspondence (CC), fast, local clustering (FLOC), interior search algorithm (ISA), Securities Exchange Board of India (SEBI), sum of squares between (SSB) and coordinated scheduling/beamforming (CSB), the GACSB algorithm can not only obtain biclusters of high quality, but also obtain biclusters of high-biologic significance. In addition, we also use different bicluster evaluation indicators, such as Average Correlation Value (ACV), Mean-Squared Residue (MSR) and Virtual Error (VE), and verify that the GACSB algorithm has a strong scalability.
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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 much more efficient than RAP in mining biclusters with constant rows from real-valued gene expression data.
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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 transcription factors (TF) such that the cell-values in biclusters are distributed around a mean value with very low variance. Such biclusters would indicate TF sets regulating gene sets with very similar probability values. However, most existing biclustering algorithms neither enforce low variance as the desired property of a bicluster, nor use variance as a guiding metric while searching for the desirable biclusters. In this paper we present an algorithm that searches a space of all overlapping biclusters organized in a lattice, and uses an upper bound on variance values of biclusters as the guiding metric. We show the algorithm to be an efficient and effective method for discovering the possibly overlapping biclusters under pre-defined variance bounds. We present in this paper our algorithm, its results with synthetic, CHIP-Seq and motif datasets, and compare them with the results obtained by other algorithms to demonstrate the power and effectiveness of our algorithm.
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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 (July 11, 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 number of conditions/samples. Given a gene expression matrix, RecBic starts with a column seed, and grows it into a full-sized bicluster by simply repetitively comparing real numbers. When tested on simulated datasets in which the elements of implanted trend-preserving biclusters and those of the background matrix have the same distribution, RecBic was able to identify the implanted biclusters in a nearly perfect manner, outperforming all the compared salient tools in terms of accuracy and robustness to noise and overlaps between the clusters. Moreover, RecBic also showed superiority in identifying functionally related genes in real gene expression datasets. Availability and implementation Code, sample input data and usage instructions are available at the following websites. Code: https://github.com/holyzews/RecBic/tree/master/RecBic/. Data: http://doi.org/10.5281/zenodo.3842717. Supplementary information Supplementary data are available at Bioinformatics online.
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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 (September 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 starts by using any biclustering algorithm in order to identify appropriate biclusters of the coverage matrix and then combines selected biclusters to define an aggregate solution to the original problem. The algorithm can be easily adapted to address a whole family of covering problems including set covering, maximal covering and backup covering problems. The proposed algorithm is tested in a series of widely known test datasets for various such problems. The main objective of this paper is to introduce the concept of biclustering as an efficient and effective approach to tackle covering problems and to stimulate further research in this area.
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Li, Yidong, Wenhua Liu, Yankun Jia, and Hairong Dong. "A weighted Mutual Information Biclustering algorithm for gene expression data." Computer Science and Information Systems 14, no. 3 (2017): 643–60. http://dx.doi.org/10.2298/csis170301021y.

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Microarrays are one of the latest breakthroughs in experimental molecular biology, which have already provided huge amount of high dimensional genetic data. Traditional clustering methods are difficult to deal with this high dimensional data, whose a subset of genes are co-regulated under a subset of conditions. Biclustering algorithms are introduced to discover local characteristics of gene expression data. In this paper, we present a novel biclustering algorithm, which calledWeighted Mutual Information Biclustering algorithm (WMIB) to discover this local characteristics of gene expression data. In our algorithm, we use the weighted mutual information as new similarity measure which can be simultaneously detect complex linear and nonlinear relationships between genes, and our algorithm proposes a new objective function to update weights of each bicluster, which can simultaneously select the conditions set of each bicluster using some rules.We have evaluated our algorithm on yeast gene expression data, the experimental results show that our algorithm can generate larger biclusters with lower mean square residues simultaneously.
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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 biclusters were found with the highest silhouette coefficient value. The expression patterns of one bicluster are highly similar to those found by the gene expression profile of the known androgen-regulated genes. Further gene set enrichment revealed that mitochondrial function-related genes were negatively correlated with AR regulation-related genes. Then, we performed differential analysis, AR binding site analysis, and survival analysis on the core genes with high phenotypic contribution. Among the core genes, NDUFA10 showed a low expression value in cancer patients across different expression profiles, while NDUFV2 showed a high expression value in cancer patients. Survival analysis of NDUFA10 and NDUFV2 demonstrated that both genes were unfavorable prognostic markers.
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Dissertations / Theses on the topic "Bicluter"

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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 allows for rapid exploration and navigation across connected documents. Through a user study we conclude that our system has the potential to help analysts filter data by allowing them to i) form hypotheses before reading documents and subsequently ii) validating them by reading a reduced and focused set of documents.
Master of Science
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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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Golchin, Maryam. "Bicluster Analysis of Biomedical Data based on Multi-objective Evolutionary Optimization." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/376812.

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Knowledge discovery is the process of finding hidden knowledge from a large volume of data that involves data mining. Data mining unveils interesting relationships among data and the results can help to make valuable predictions or recommendation in various applications. Recently, biclustering has become a common method in data mining and pattern recognition. Biclustering is an unsupervised machine learning method that can uncover and extract accurate and useful information from high-dimensional sparse data. Biclustering has found many useful applications for visualization and exploratory analysis in various fields such as knowledge discovery, data mining, pattern classification, information retrieval, collaborative filtering, and especially in gene expression data analysis such as functional annotation, tissue classification, and motif identification. It has been shown in previous studies that finding biclusters of data is inherently intractable and computationally complex. Generally, the challenges of biclustering include the high dimensionality of data, noisy data, different types of bicluster patterns, and the fact that biclusters can overlap. Although there are several studies in biclustering, after a review of the methods proposed in the literature, we found that these challenges are not addressed properly. Most of the proposed methods in literature can only detect a limited set of bicluster patterns under restrictive assumptions about the data. Moreover, in many methods biclusters are detected sequentially, i.e., the method replaces the detected bicluster with the background and detects the next bicluster, thus preventing the detection of overlapping biclusters. Given the above statements, there is a need for innovative methods to extract valuable information from the data and to reach a deeper understanding of the outcomes. Therefore, in this study, we first proposed a method (PBD-SPEA) that uses a new dynamic encoding scheme to detect multiple overlapped biclusters concurrently. However, the implementation is complex as there are several heuristic search procedures in different steps of the proposed method, and it is not able to detect all types of patterns in biclusters. Thus, a second method (LBDP) is proposed based on geometrical biclustering. In this method, we search for hyperplanes from the data using an evolutionary algorithm. Applying this idea, we are able to detect all types of bicluster patterns concurrently. We defined several scenarios in both synthetic and real data to test the performance of the proposed methods. Although our work is initially targeted for biomedical data (gene expression data), we also tested the generality of the algorithms on other non-medical data, such as image data and social networking data. In all scenarios, our methods achieved reliable results compared to several state-of-the-arts.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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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
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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
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 aplicações, desde suporte a sistemas de recomendação, até análise de dados de expressão gênica. Inicialmente, diversas heurísticas foram propostas para encontrar biclusters numa base de dados numérica. No entanto, tais heurísticas apresen- tam alguns inconvenientes, como não encontrar biclusters relevantes na base de dados e não maximizar o volume dos biclusters encontrados. Algoritmos enumerativos são uma proposta recente, especialmente no caso de bases numéricas, cuja solução é um conjunto de biclusters maximais e não redundantes. Contudo, a habilidade de enumerar biclusters trouxe mais um cenário desafiador: em bases de dados ruidosas, cada bicluster original se fragmenta em vá- rios outros biclusters com alto nível de sobreposição, o que impede uma análise direta dos resultados obtidos. Essa fragmentação irá ocorrer independente da definição escolhida de co- erência interna no bicluster, sendo mais relacionada com o próprio nível de ruído. Buscando reverter essa fragmentação, nesse trabalho propomos duas formas de agregação de biclusters a partir de resultados que apresentem alto grau de sobreposição: uma baseada na clusteriza- ção hierárquica com single linkage, e outra explorando diretamente a taxa de sobreposição dos biclusters. Em seguida, um passo de poda é executado para remover objetos ou atributos indesejados que podem ter sido incluídos como resultado da agregação. As duas propostas foram comparadas entre si e com o estado da arte, em diversos experimentos, incluindo bases de dados artificiais e reais. Essas duas novas formas de agregação não só reduziram significa- tivamente a quantidade de biclusters, essencialmente defragmentando os biclusters originais, mas também aumentaram consistentemente a qualidade da solução, medida em termos de precisão e recuperação, quando os biclusters são conhecidos previamente
Abstract: Biclustering involves the simultaneous clustering of objects and their attributes, thus defin- ing local models for the two-way relationship of objects and attributes. Just like clustering, biclustering has a broad set of applications, ranging from an advanced support for recom- mender systems of practical relevance to a decisive role in data mining techniques devoted to gene expression data analysis. Initially, heuristics have been proposed to find biclusters, and their main drawbacks are the possibility of losing some existing biclusters and the inca- pability of maximizing the volume of the obtained biclusters. Recently efficient algorithms were conceived to enumerate all the biclusters, particularly in numerical datasets, so that they compose a complete set of maximal and non-redundant biclusters. However, the ability to enumerate biclusters revealed a challenging scenario: in noisy datasets, each true bicluster becomes highly fragmented and with a high degree of overlapping, thus preventing a direct analysis of the obtained results. Fragmentation will happen no matter the boundary condi- tion adopted to specify the internal coherence of the valid biclusters, though the degree of fragmentation will be associated with the noise level. Aiming at reverting the fragmentation, we propose here two approaches for properly aggregating a set of biclusters exhibiting a high degree of overlapping: one based on single linkage and the other directly exploring the rate of overlapping. A pruning step is then employed to filter intruder objects and/or attributes that were added as a side effect of aggregation. Both proposals were compared with each other and also with the actual state-of-the-art in several experiments, including real and artificial datasets. The two newly-conceived aggregation mechanisms not only significantly reduced the number of biclusters, essentially defragmenting true biclusters, but also consistently in- creased the quality of the whole solution, measured in terms of Precision and Recall when the composition of the dataset is known a priori
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
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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 relationships into coordinated sets. Thus, coordinated relationships can be formalized as biclusters. However, how to incorporate biclusters into a visual analytics tool to support sensemaking tasks is challenging. To address this, this work features three key contributions: 1) a five-level design framework for bicluster visualizations, 2) BiSet, highlighting bicluster-based edge bundling, seriation-based multiple lists ordering, and interactions for dynamic information foraging and management, and 3) an evaluation of BiSet.
Ph. D.
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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 independence) in occurrence patterns over the underlying objects. Unlike other measures of independence, truth tables possess anti-monotone features that can be exploited in order to mine them effectively. We present a level-wise algorithm that takes advantage of these features, showing results on real and synthetic data. These results demonstrate the scalability of our algorithm. We also introduce new methods of mining straddling biclusters. Biclusters relate subsets of objects to subsets of properties they share within a single dataset. Straddling biclusters extend biclusters by relating a subset of objects to subsets of properties they share in two datasets. We present two levelwise algorithms, named UnionMiner and TwoMiner, which discover straddling biclusters efficiently by treating multiple datasets as a single dataset. We show results on real and synthetic data, and explore the advantages and limitations of each algorithm. We develop guidelines which suggest which of these algorithms is likely to perform better based on features of the datasets.
Master of Science
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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 algorithm that models genome-wide perturbation screens by deriving relations between phenotypes and subsequently using these relations in a local manner to derive genephenotype relationships. We show how this algorithm outperforms all previously described algorithms for gene-phenotype modeling. We also present theoretical insight into the convergence and accuracy properties of this approach. Second, we define a new data mining problemâ constrained minimal separator miningâ and propose algorithms as well as applications to modeling gene perturbation screens by viewing the perturbed genes as a graph separator. Both of these data mining applications are evaluated on network datasets from S. cerevisiae and C. elegans. Finally, we present an approach to model the relationship between metabolic pathways and operon structure in prokaryotic genomes. In this approach, we present a new pattern classâ biclusters over domains with supplied partial ordersâ and present algorithms for systematically detecting such biclusters. Together, our data mining algorithms provide a comprehensive arsenal of techniques for modeling gene perturbation screens and metabolic pathways.
Ph. D.
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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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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
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 study the effectiveness of biclustering, an advanced clustering technique that groups rows and columns simultaneously, in Collaborative Filtering. When applied to the classic U-I interaction matrices, biclustering considers the duality relations between users and items, creating clusters of users who are similar under a particular group of items. We propose USBCF, a novel biclustering-based Collaborative Filtering approach that creates user specific models to improve the scalability of traditional CF approaches. Using a realworld dataset, we conduct a set of experiments to objectively evaluate the performance of the proposed approach, comparing it against baseline and state-of-the-art Collaborative Filtering methods. Our results show that the proposed approach can successfully suppress the main limitation of the previously proposed state-of-the-art biclustering-based Collaborative Filtering (BBCF) since BBCF can only output predictions for a small subset of the system users and item (lack of coverage). Moreover, USBCF produces rating predictions with quality comparable to the state-of-the-art approaches.
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Books on the topic "Bicluter"

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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). ru: 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 of the area. The prospects of the use of biotechnology in integrated environmental protection. In the framework of the above to address complex social, environmental and production problems in Azerbaijan developed scientific basis of integrated system of industrial farms — biclusters with a closed production cycle through effective utilization of regional biological resources, whose interactions and relationships take on the character of vzaimodeistvie components for obtaining focused final result with high practical importance. Microbiological, biochemical and technological processes are the basis of all development of biotechnology. Presents the development will help strengthen the ties between science and production, establishing mechanisms to conduct applied research in the field of innovation and creation of knowledge-based technologies in solving current and future environmental problems in Azerbaijan. We offer innovative ideas distinguishes the potential need for their materialization into new products, technologies and services, including the widespread use of digital technologies to design dynamic digital environmental map in space and in time. For students, scientific and engineering-technical workers, students and specializing in environmental technology, environmental protection.
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Book chapters on the topic "Bicluter"

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Shojima, Kojiro. "Bicluster Network Model." In Test Data Engineering, 527–69. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9986-3_11.

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Sun, Peng, Jiong Guo, and Jan Baumbach. "Complexity of Dense Bicluster Editing Problems." In Lecture Notes in Computer Science, 154–65. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08783-2_14.

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Gheno, Gloria. "BIBLIOBICLUSTER: A Bicluster Algorithm for Bibliometrics." In IFIP Advances in Information and Communication Technology, 271–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79150-6_22.

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Protti, Fábio, Maise Dantas da Silva, and Jayme Luiz Szwarcfiter. "Applying Modular Decomposition to Parameterized Bicluster Editing." In Parameterized and Exact Computation, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11847250_1.

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Lonardi, Stefano, Wojciech Szpankowski, and Qiaofeng Yang. "Finding Biclusters by Random Projections." In Combinatorial Pattern Matching, 102–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27801-6_8.

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Lafond, Manuel. "Even Better Fixed-Parameter Algorithms for Bicluster Editing." In Lecture Notes in Computer Science, 578–90. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58150-3_47.

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Oliveira, Saullo, Rosana Veroneze, and Fernando J. Von Zuben. "On Bicluster Aggregation and its Benefits for Enumerative Solutions." In Machine Learning and Data Mining in Pattern Recognition, 266–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21024-7_18.

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Saito, Tatsuya, and Yoshifumi Okada. "Bicluster-Network Method and Its Application to Movie Recommendation." In Advances in Intelligent Systems and Computing, 147–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02821-7_14.

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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, 433–39. Berlin, Heidelberg: 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, 573–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_70.

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Conference papers on the topic "Bicluter"

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P. Pinto-Roa, Diego, Hernán Medina, Federico Román, Miguel García-Torres, Federico Divina, Francisco Gómez-Vela, Félix Morales, 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 scheme to find biclusters in electric energy. Numerical simulations show the benefits of the proposed approach, discovering significantly more electricity consumption patterns compared to a state-of-the-art non-parallel competitive algorithm.
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Sun, Huan, Gengxin Miao, and Xifeng Yan. "Noise-Resistant Bicluster Recognition." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.34.

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Sun, Maoyuan, David Koop, Jian Zhao, Chris North, and Naren Ramakrishnan. "Interactive Bicluster Aggregation in Bipartite Graphs." In 2019 IEEE Visualization Conference (VIS). IEEE, 2019. http://dx.doi.org/10.1109/visual.2019.8933546.

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Aggarwal, Geeta, and Neelima Gupta. "BEMI Bicluster Ensemble Using Mutual Information." In 2013 12th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2013. http://dx.doi.org/10.1109/icmla.2013.65.

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Ohama, Iku, Takuya Kida, and Hiroki Arimura. "Discovering Relevance-Dependent Bicluster Structure from Relational Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/359.

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In this paper, we propose a statistical model for relevance-dependent biclustering to analyze relational data. The proposed model factorizes relational data into bicluster structure with two features: (1) each object in a cluster has a relevance value, which indicates how strongly the object relates to the cluster and (2) all clusters are related to at least one dense block. These features simplify the task of understanding the meaning of each cluster because only a few highly relevant objects need to be inspected. We introduced the Relevance-Dependent Bernoulli Distribution (R-BD) as a prior for relevance-dependent binary matrices and proposed the novel Relevance-Dependent Infinite Biclustering (R-IB) model, which automatically estimates the number of clusters. Posterior inference can be performed efficiently using a collapsed Gibbs sampler because the parameters of the R-IB model can be fully marginalized out. Experimental results show that the R-IB extracts more essential bicluster structure with better computational efficiency than conventional models. We further observed that the biclustering results obtained by R-IB facilitate interpretation of the meaning of each cluster.
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Chen, Kuanchung, and Yuh-Jyh Hu. "Bicluster Analysis of Genome-Wide Gene Expression." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330994.

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Liu, Shuyong, Yan Chen, Ming Yang, and Rui Ding. "Bicluster Algorithm and Used in Market Analysis." In 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). IEEE, 2009. http://dx.doi.org/10.1109/wkdd.2009.224.

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Golchin, Maryam, and Alan Wee-Chung Liew. "Bicluster Detection by Hyperplane Projection and Evolutionary Optimization." In 9th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006710000610068.

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Golchin, Maryam, and Alan Wee-Chung Liew. "Bicluster detection using strength pareto front evolutionary algorithm." In ACSW '16: Australasian Computer Science Week. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2843043.2843050.

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Liu, Feng, Huaibei Zhou, and Juan Liu. "A Projection and Search Algorithm for the Bicluster Problem." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.1487.

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