Journal articles: '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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Williams, Andrew, and Sabina Halappanavar. "Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials." Beilstein Journal of Nanotechnology 6 (December 21, 2015): 2438–48. http://dx.doi.org/10.3762/bjnano.6.252.

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Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.

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Xie, Juan, Anjun Ma, Yu Zhang, Bingqiang Liu, Sha Cao, Cankun Wang, Jennifer Xu, Chi Zhang, and Qin Ma. "QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data." Bioinformatics 36, no. 4 (September 10, 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), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq. Availability and implementation The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2. Supplementary information Supplementary data are available at Bioinformatics online.

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López-Fernández, Aurelio, Domingo S. Rodríguez-Baena, and Francisco Gómez-Vela. "gMSR: A Multi-GPU Algorithm to Accelerate a Massive Validation of Biclusters." Electronics 9, no. 11 (October 27, 2020): 1782. http://dx.doi.org/10.3390/electronics9111782.

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Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinformatics, Biclustering techniques have become extremely time-consuming, also being huge the number of results generated, due to the continuous increase in the size of the databases over the last few years. For this reason, validation techniques must be adapted to this new environment in order to help researchers focus their efforts on a specific subset of results in an efficient, fast and reliable way. The aforementioned situation may well be considered as Big Data context. In this sense, multiple machine learning techniques have been implemented by the application of Graphic Processing Units (GPU) technology and CUDA architecture to accelerate the processing of large databases. However, as far as we know, this technology has not yet been applied to any bicluster validation technique. In this work, a multi-GPU version of one of the most used bicluster validation measure, Mean Squared Residue (MSR), is presented. It takes advantage of all the hardware and memory resources offered by GPU devices. Because of to this, gMSR is able to validate a massive number of biclusters in any Biclustering-based study within a Big Data context.

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Ningsih, Wiwik Andriyani Lestari, I. Made Sumertajaya, and Asep Saefuddin. "BICLUSTERING APPLICATION IN INDONESIAN ECONOMIC AND PANDEMIC VULNERABILITY." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 4 (December 15, 2022): 1453–64. http://dx.doi.org/10.30598/barekengvol16iss4pp1453-1464.

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Biclustering is an analytical tool to group data from two dimensions simultaneously. The analysis was first introduced by Hartigan (1972) and applied by Cheng and Church (2000) to the gene expression matrix. The Cheng and Church (CC) algorithm is a popular biclustering algorithm and has been widely applied outside the field of biological data in recent years. This algorithm application in economic and Covid-19 pandemic vulnerability cases is exciting and essential to do in order to get an overview of the spatial pattern and characteristics of the bicluster of economic and COVID-19 pandemic vulnerability in Indonesia. This study uses secondary data from some ministries. Forming a bicluster using the CC algorithm requires determining the delta threshold so that several types of delta thresholds are formed to choose the best (optimum) using the evaluation of the average value of mean square residue (MSR) to volume ratios. The similarity of the optimum bi-cluster with the other is also seen based on the Liu and Wang index values. The 0.01 delta threshold is chosen as the optimum threshold because it produces the smallest average value of MSR to volume ratios (0.00032). Based on Liu and Wang Index values, the optimum threshold has a similarity level below 50% with other types of delta thresholds, so the threshold is the best unique threshold. The optimum threshold resulted in six biclusters (six spatial patterns). Most regions in Indonesia (11 provinces) tend to have low economic and COVID-19 pandemic vulnerability in the first spatial pattern characteristic variables.

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Novidianto, Raditya, and Rini Irfani. "Bicluster CC Algoritm Analysis to Identify Patterns of Food Insecurity in Indonesia." Jurnal Matematika, Statistika dan Komputasi 17, no. 2 (December 23, 2020): 325–38. http://dx.doi.org/10.20956/jmsk.v17i2.12057.

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Indonesia is known as an agricultural country. This means that most of the population work in the agricultural sector related to food. However, food insecurity still occurs in Indonesia. With the COVID-19 pandemic, the Food and Agriculture Organization (FAO) stated that there was a threat of food scarcity which had an impact on food insecurity conditions. This would undermine the second goal of the SDGs, which is to end hunger and create sustainable agriculture. The purpose of this study was to determine the spatial pattern of food insecurity in each province in Indonesia using the bicluster method. The data used are data from Susenas and Sakernas by BPS in 2019. Several studies show that the bicluster method with the CC algorithm shows that each province group has a different characteristic pattern. In the bicluster approach, the researcher runs parameter tuning to select the best parameter based on the Mean Square Residual in Volume (MSR / V). The CC algorithm tries to get a bicluster with a low MSR value, therefore the best parameter is the one that produces the smallest MSR / V value, in this study the smallest MSR / V is 0,01737 with δ = 0,01. The application of the CC biclustering algorithm to the food insecurity structure in Indonesia results in 5 bicluster. Bicluster 1 consists of 15 provinces with 8 variables, Bicluster 2 consists of 10 provinces with 5 variables, Bicluster 3 consists of 3 provinces with 7 variables, Bicluster 4 consists of 4 provinces with 4 variables and Bicluster 5 consists of 2 provinces with 5 variables. Biculster 4 represents a cluster of food insecurity areas with the characteristics of the bicluster P0, P1, P2 and calorie consumption of less than 1400 KKAL.

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Siswantining, Titin, Achmad Eriza Aminanto, Devvi Sarwinda, and Olivia Swasti. "Biclustering Analysis Using Plaid Model on Gene Expression Data of Colon Cancer." Austrian Journal of Statistics 50, no. 5 (August 25, 2021): 101–14. http://dx.doi.org/10.17713/ajs.v50i5.1195.

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Unlike other typical clustering analysis, which considers column only, biclustering analysis processes a matrix into sub-matrices based on rows and columns simultaneously. One method of bicluster analysis uses the probabilistic model, like the plaid model, that provides overlapping bicluster. The plaid model calculates the value of an element given from a particular sub-matrix for each cell; thus, the value can be seen as the number of contributions of a particular bicluster. The algorithm begins with preparing the input data as a matrix, then an initial model is assessed and makes a residual matrix from the model. After that, we determine bicluster candidates, which are evaluated for its effect parameters and bicluster membership parameters. Finally, the bicluster candidate is pruned to give the optimal bicluster. We implemented the algorithm on gene expression dataset of colon cancer, where the rows and columns contain observations and types of genes, respectively. We carried out in six distinct scenarios in which each scenario uses different model parameters and threshold values. We measured the results using Jaccard index and coherence variance. Our experiments show that biclustering analysis on a model with mean, row, and column effects of colon cancer data output low coherence variance.

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Komalasari, Desy, Mustika Hadijati, Nurul Fitriyani, and Agus Kurnia. "Factor Extraction and Bicluster Analysis on Halal Destinations in Lombok Island." Jurnal Varian 4, no. 1 (September 29, 2020): 1–10. http://dx.doi.org/10.30812/varian.v4i1.743.

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Indonesia is one of the countries currently developing the concept of halal tourism. Halal tourism includes many variables that are related to each other, which need to be grouped into several main factors that affect tourist visits. This study was conducted to group the variables associated with halal tourism visits to Lombok Island using factor analysis and to classify sub-districts and halal tourism destinations on Lombok Island using the Plaid Bicluster algorithm. Based on the analysis using the main component extraction technique in factor analysis with varimax rotation, it can be concluded that the 9 halal tourism characteristic variables can be grouped into 2 main factors. Furthermore, by using the Plaid Bicluster algorithm, 2 Bicluster were produced. There were 7 sub-districts and 9 destinations formed in Bicluster I, and 8 sub-districts and 3 destinations formed in Bicluster II.

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-, Nurmawiya, and Robert Kurniawan. "PENGELOMPOKAN WILAYAH INDONESIA DALAM MENGHADAPI REVOLUSI INDUSTRI 4.0 DENGAN METODE BICLUSTERING." Seminar Nasional Official Statistics 2020, no. 1 (January 5, 2021): 790–97. http://dx.doi.org/10.34123/semnasoffstat.v2020i1.511.

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Indonesia sedang berada dalam era revolusi industri 4.0 yang mana untuk menghadapi era tersebut diperlukan kesiapan dari berbagai sisi terutama masyarakat. Era ini dapat memberikan keuntungan pertumbuhan ekonomi bagi Indonesia, akan tetapi dapat berakibat buruk berupa hilangnya sejumlah lapangan pekerjaan akibat adanya automasi. Oleh karena itu, kesiapan masyarakat memegang peranan penting dalam menghadapi era ini. Berkaitan dengan hal tersebut, penelitian ini dilakukan untuk mengelompokkan wilayah kabupaten/kota di Indonesia dengan menggunakan variabel indikator kesiapan yang terdapat dalam networked readiness index (NRI) oleh World Economic Forum (WEF). Metode pengelompokan yang digunakan adalah biclustering dengan algoritma cheng dan church. Pengelompokan dengan metode tersebut menghasilkan 5 bicluster di mana bicluster 4 adalah kelompok yang memiliki nilai rataan terendah untuk setiap variabel. Posisi terendah ini kemudian diikuti oleh bicluster 3. Berdasarkan hasil tersebut, pemerintah perlu menjadikan kabupaten/kota yang tercakup dalam bicluster 4 dan 3 sebagai prioritas dalam melakukan pembenahan untuk mempersiapkan masyarakatnya menghadapi revolusi industri 4.0.

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Yuniarto, Budi, and Robert Kurniawan. "Understanding Structure of Poverty Dimensions in East Java: Bicluster Approach." Signifikan: Jurnal Ilmu Ekonomi 6, no. 2 (July 1, 2017): 289–300. http://dx.doi.org/10.15408/sjie.v6i2.4769.

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Poverty is still become a main problem for Indonesia, where recently, the view point of poverty is not just from income or consumption, but it’s defined multidimensionally. The understanding of the structure of multidimensional poverty is essential to government to develop policies for poverty reduction. This paper aims to describe the structure of poverty in East Java by using variables forming the dimensions of poverty and to investigate any clustering patterns in the region of East Java with considering the poverty variables using biclustering method. Biclustering is an unsupervised technique in data mining where we are grouping scalars from the two-dimensional matrix. Using bicluster analysis, we found two bicluster where each bicluster has different characteristics.DOI: 10.15408/sjie.v6i2.4769

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Bhavnani, Suresh K., Weibin Zhang, Sandra Hatch, Randall Urban, and Christopher Tignanelli. "364 Identification of Symptom-Based Phenotypes in PASC Patients through Bipartite Network Analysis: Implications for Patient Triage and Precision Treatment Strategies." Journal of Clinical and Translational Science 6, s1 (April 2022): 68. http://dx.doi.org/10.1017/cts.2022.207.

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OBJECTIVES/GOALS: Approximately 10% of COVID-19 patients experience multiple symptoms weeks and months after the acute phase of infection. Our goal was to use advanced machine learning methods to identify PASC phenotypes based on their symptom profiles, and their association with critical adverse outcomes, with the goal of designing future targeted interventions. METHODS/STUDY POPULATION: Data. All COVID-19 outpatients from 12 University of Minnesota hospitals and 60 clinics. Independent variables consisted of 20 CDC-defined PASC symptoms extracted from clinical notes using NLP. Covariates included demographics, and outcomes included New Psychological Diagnostic Evaluation, and Number of PASC Hospital Visits (>=5). Cases (n=3235) consisted of patients with at least one symptom, and controls (n=3034) consisted of patients with no symptoms. Method. (1) Used bipartite network analysis and modularity maximization to identify patient-symptom biclusters. (2) Used multivariable logistic regression (adjusted for demographics and corrected through Bonferroni) to measure the odds ratio of each patient bicluster to adverse outcomes, compared to controls, and to each of the other biclusters. RESULTS/ANTICIPATED RESULTS: The analysis identified 6 PASC phenotypes (http://www.skbhavnani.com/DIVA/Images/Fig-1-PASC-Network.jpg), which was statistically significant compared to 1000 random permutations of the data (PASC=.31, Random Median=.27, z=11, P<.01). Three of the clusters (Cluster-1, Cluster-4, and Cluster-5 encircled with ovals in Fig. 1) contained CNS-related symptoms, which had statistically significant risk for one or both of the adverse outcomes. For example, Cluster-1 with critical CNS symptoms (depression, insomnia, anxiety, brain-fog/difficulty-thinking), had a significantly higher OR compared to the controls for New Psychological Diagnostic Evaluation (OR=6.6, CI=4.9-9.1, P-corr<.001), in addition to having a significantly higher ORs for the same outcome compared to all the other clusters. DISCUSSION/SIGNIFICANCE: The results identified distinct PASC phenotypes based on symptom profiles, with three of them related to CNS symptoms, each of which had significantly higher risk for specific adverse outcomes compared to controls. We will test whether these phenotypes replicate in the N3C data, and explore their translation into triage and treatment strategies.

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Gao, Hanjia, Zhengjian Bai, Weiguo Gao, and Shuqin Zhang. "Penalized -regression-based bicluster localization." Pattern Recognition 117 (September 2021): 107984. http://dx.doi.org/10.1016/j.patcog.2021.107984.

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Tsur, Dekel. "Faster parameterized algorithm for Bicluster Editing." Information Processing Letters 168 (June 2021): 106095. http://dx.doi.org/10.1016/j.ipl.2021.106095.

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Lee, Youngrok, Jeonghwa Lee, and Chi-Hyuck Jun. "Stability-based validation of bicluster solutions." Pattern Recognition 44, no. 2 (February 2011): 252–64. http://dx.doi.org/10.1016/j.patcog.2010.08.029.

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Santamaria, R., R. Theron, and L. Quintales. "BicOverlapper: A tool for bicluster visualization." Bioinformatics 24, no. 9 (March 5, 2008): 1212–13. http://dx.doi.org/10.1093/bioinformatics/btn076.

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Hochreiter, Sepp, Ulrich Bodenhofer, Martin Heusel, Andreas Mayr, Andreas Mitterecker, Adetayo Kasim, Tatsiana Khamiakova, et al. "FABIA: factor analysis for bicluster acquisition." Bioinformatics 26, no. 12 (April 23, 2010): 1520–27. http://dx.doi.org/10.1093/bioinformatics/btq227.

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de Sousa, Gilberto F., Lucidio dos Anjos F. Cabral, Luiz Satoru Ochi, and Fábio Protti. "Hybrid Metaheuristic for Bicluster Editing Problem." Electronic Notes in Discrete Mathematics 39 (December 2012): 35–42. http://dx.doi.org/10.1016/j.endm.2012.10.006.

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Alavi Majd, Hamid, Soodeh Shahsavari, Ahmad Reza Baghestani, Seyyed Mohammad Tabatabaei, Naghme Khadem Bashi, Mostafa Rezaei Tavirani, and Mohsen Hamidpour. "Evaluation of Plaid Models in Biclustering of Gene Expression Data." Scientifica 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3059767.

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Background.Biclustering algorithms for the analysis of high-dimensional gene expression data were proposed. Among them, the plaid model is arguably one of the most flexible biclustering models up to now.Objective.The main goal of this study is to provide an evaluation of plaid models. To that end, we will investigate this model on both simulation data and real gene expression datasets.Methods.Two simulated matrices with different degrees of overlap and noise are generated and then the intrinsic structure of these data is compared with biclusters result. Also, we have searched biologically significant discovered biclusters by GO analysis.Results.When there is no noise the algorithm almost discovered all of the biclusters but when there is moderate noise in the dataset, this algorithm cannot perform very well in finding overlapping biclusters and if noise is big, the result of biclustering is not reliable.Conclusion.The plaid model needs to be modified because when there is a moderate or big noise in the data, it cannot find good biclusters. This is a statistical model and is a quite flexible one. In summary, in order to reduce the errors, model can be manipulated and distribution of error can be changed.

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R., Gowri, and Rathipriya R. "Protein Motif Comparator using PSO K-Means." International Journal of Applied Metaheuristic Computing 7, no. 3 (July 2016): 56–68. http://dx.doi.org/10.4018/ijamc.2016070104.

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The main goal of this paper is to compare the motif information extracted from clusters and biclusters of the protein using Motif Comparator. The clusters and biclusters are obtained using the PSO k-means algorithm. The functions of the proteins are preferably found from their motif information. The Motif Comparator is used to detect the clusters and biclusters, to locate the Significant Amino Acids present, to find the highly homologous cluster. The motif information acquired is based on the structure homogeneity of the protein sequence. The homogeneity is evaluated based on their secondary structure similarity of the protein.

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de Sousa Filho, Gilberto F., Teobaldo L. Bulhões Júnior, Lucídio dos Anjos F. Cabral, Luiz Satoru Ochi, and Fábio Protti. "A parallel hybrid metaheuristic for bicluster editing." International Transactions in Operational Research 23, no. 3 (November 10, 2015): 409–31. http://dx.doi.org/10.1111/itor.12215.

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de Sousa Filho, Gilberto F., Teobaldo L. Bulhões Júnior, Lucidio A. F. Cabral, Luiz Satoru Ochi, and Fábio Protti. "New heuristics for the Bicluster Editing Problem." Annals of Operations Research 258, no. 2 (June 28, 2016): 781–814. http://dx.doi.org/10.1007/s10479-016-2261-x.

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MUKHOPADHYAY, ANIRBAN, UJJWAL MAULIK, and SANGHAMITRA BANDYOPADHYAY. "A NOVEL COHERENCE MEASURE FOR DISCOVERING SCALING BICLUSTERS FROM GENE EXPRESSION DATA." Journal of Bioinformatics and Computational Biology 07, no. 05 (October 2009): 853–68. http://dx.doi.org/10.1142/s0219720009004370.

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Biclustering methods are used to identify a subset of genes that are co-regulated in a subset of experimental conditions in microarray gene expression data. Many biclustering algorithms rely on optimizing mean squared residue to discover biclusters from a gene expression dataset. Recently it has been proved that mean squared residue is only good in capturing constant and shifting biclusters. However, scaling biclusters cannot be detected using this metric. In this article, a new coherence measure called scaling mean squared residue (SMSR) is proposed. Theoretically it has been proved that the proposed new measure is able to detect the scaling patterns effectively and it is invariant to local or global scaling of the input dataset. The effectiveness of the proposed coherence measure in detecting scaling patterns has been demonstrated experimentally on artificial and real-life benchmark gene expression datasets. Moreover, biological significance tests have been conducted to show that the biclusters identified using the proposed measure are composed of functionally enriched sets of genes.

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Yanjie, Zhang, and Sun Hongbo. "Application of biclustering algorithm to extract rules from labeled data." International Journal of Crowd Science 2, no. 2 (June 11, 2018): 86–98. http://dx.doi.org/10.1108/ijcs-01-2018-0002.

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Purpose For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times. Design/methodology/approach In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed. Findings To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters. Originality/value The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.

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LI, HAIFENG, XIN CHEN, KESHU ZHANG, and TAO JIANG. "A GENERAL FRAMEWORK FOR BICLUSTERING GENE EXPRESSION DATA." Journal of Bioinformatics and Computational Biology 04, no. 04 (August 2006): 911–93. http://dx.doi.org/10.1142/s021972000600217x.

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A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for biclustering that, in general, can be used to discover all computable patterns in gene expression data. The method is based on the theory of Kolmogorov complexity. More precisely, we use Kolmogorov complexity to measure the randomness of submatrices as the merit of biclusters because randomness naturally consists in a lack of regularity, which is a common property of all types of patterns. On the basis of algorithmic probability measure, we develop a Markov Chain Monte Carlo algorithm to search for biclusters. Our method can also be easily extended to solve the problems of conventional clustering and checkerboard type biclustering. The preliminary experiments on simulated as well as real data show that our approach is very versatile and promising.

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Ramkumar, M., N. Basker, D. Pradeep, Ramesh Prajapati, N. Yuvaraj, R. Arshath Raja, C. Suresh, et al. "Healthcare Biclustering-Based Prediction on Gene Expression Dataset." BioMed Research International 2022 (February 22, 2022): 1–7. http://dx.doi.org/10.1155/2022/2263194.

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In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

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Sun, Maoyuan, Chris North, and Naren Ramakrishnan. "A Five-Level Design Framework for Bicluster Visualizations." IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (December 31, 2014): 1713–22. http://dx.doi.org/10.1109/tvcg.2014.2346665.

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Cheng, K. O., N. F. Law, W. C. Siu, and T. H. Lau. "BiVisu: software tool for bicluster detection and visualization." Bioinformatics 23, no. 17 (June 22, 2007): 2342–44. http://dx.doi.org/10.1093/bioinformatics/btm338.

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Pinheiro, Rian G. S., Ivan C. Martins, Fábio Protti, Luiz S. Ochi, Luidi G. Simonetti, and Anand Subramanian. "On solving manufacturing cell formation via Bicluster Editing." European Journal of Operational Research 254, no. 3 (November 2016): 769–79. http://dx.doi.org/10.1016/j.ejor.2016.05.010.

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Coelho, A. L. V., and F. O. França. "Enhancing perceptrons with contrastive biclusters." Electronics Letters 52, no. 24 (November 2016): 1974–76. http://dx.doi.org/10.1049/el.2016.3067.

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Fiaux, Patrick, Maoyuan Sun, Lauren Bradel, Chris North, Naren Ramakrishnan, and Alex Endert. "Bixplorer: Visual Analytics with Biclusters." Computer 46, no. 8 (August 2013): 90–94. http://dx.doi.org/10.1109/mc.2013.269.

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Lonardi, Stefano, Wojciech Szpankowski, and Qiaofeng Yang. "Finding biclusters by random projections." Theoretical Computer Science 368, no. 3 (December 2006): 217–30. http://dx.doi.org/10.1016/j.tcs.2006.09.023.

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XIE, Huabo, Xuequn SHANG, and Miao WANG. "Differential co-expression relative constant row bicluster mining algorithm." Journal of Computer Applications 33, no. 8 (November 5, 2013): 2188–93. http://dx.doi.org/10.3724/sp.j.1087.2013.02188.

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Yanjie, Zhang, Hu Zhanyi, and Sun Limin. "Bicluster Significance Evaluation with the Application of Information Entropy." Information Technology Journal 12, no. 23 (November 15, 2013): 7898–901. http://dx.doi.org/10.3923/itj.2013.7898.7901.

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Xiao, Mingyu, and Shaowei Kou. "A simple and improved parameterized algorithm for bicluster editing." Information Processing Letters 174 (March 2022): 106193. http://dx.doi.org/10.1016/j.ipl.2021.106193.

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Wei Shen, You Zhou, Ming Zheng, GuiXia Liu, Chong Xing, JiaNan Wu, and Yi Zhong. "A novel bicluster method with mixed optimal search algorithm." Journal of Convergence Information Technology 6, no. 12 (December 31, 2011): 390–97. http://dx.doi.org/10.4156/jcit.vol6.issue12.49.

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Aouabed, Haithem, Rodrigo SantamaríA, and Mourad Elloumi. "VisBicluster: A Matrix-Based Bicluster Visualization of Expression Data." Journal of Computational Biology 27, no. 9 (September 1, 2020): 1384–96. http://dx.doi.org/10.1089/cmb.2019.0385.

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Goncalves, Joana P., and Sara C. Madeira. "LateBiclustering: Efficient Heuristic Algorithm for Time-Lagged Bicluster Identification." IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, no. 5 (September 2014): 801–13. http://dx.doi.org/10.1109/tcbb.2014.2312007.

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Pauk, J., and K. Minta-Bielecka. "Gait patterns classification based on cluster and bicluster analysis." Biocybernetics and Biomedical Engineering 36, no. 2 (2016): 391–96. http://dx.doi.org/10.1016/j.bbe.2016.03.002.

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Frick, Mareike, Alla Bulashevska, Marcus Duehren-von Minden, Kristina Heining-Mikesch, Dietmar Pfeifer, and Hendrik Veelken. "High-Throughput Analysis of Antigen Recognition by the B Cell Receptors of Malignant Lymphomas with High-Density Protein Microarrays." Blood 114, no. 22 (November 20, 2009): 2945. http://dx.doi.org/10.1182/blood.v114.22.2945.2945.

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Abstract Abstract 2945 Poster Board II-921 The etiology of indolent B cell lymphomas (iNHL) is largely unknown. However, systemic autoimmune diseases are associated with certain iNHL. Organ-restricted chronic inflammation, i.e. H. pylori-associated gastritis, Sjogren's syndrome, and others, plays an etiological role in extranodal marginal zone lymphoma (MZL). Finally, shared structural features of the B cell receptor (BCR) suggest antigen recognition in iNHL etiology, particularly in CLL. Indeed, recognition of common autoantigens has been shown for CLL BCR. However, there has been no unbiased comparative assessment of antigen binding by iNHL BCR. We measured binding of lymphoma BCR simultaneously to 8000 human proteins displayed on high-density microarrays. BCR from 45 lymphomas, including 13 mantle cell lymphomas (MCL), 10 CLL, 5 nodal MZL, 5 diffuse large B-cell lymphomas (DLBCL), 4 follicular lymphomas (FL), 3 myelomas, 2 splenic MZL, and 2 LPL, were expressed as recombinant Fab fragments in E. coli. The most abundant among the 19 represented different VH segments were VH 4-34 (n=10), 1-69 (6), 3-30 (4), 3-21 (3), and 1-8 (3). Bound Fab was detected by 647AlexaFluor-goat-anti-human IgG. Z-scores and Z-factors (Zhang et al., 1999) were calculated for each Fab-protein interaction. Fab binding was defined as either Z-score >1.65 and Z-factor >0, or Z-score >1 and Z-factor >0.5. 108 robust Fab-Protein interactions were identified that involved 48 different proteins. 21 NHL BCR, derived from all lymphoma types, did not bind any protein. 12 BCR recognized one protein only. 9 BCR were highly polyreactive as defined by binding to ≥5 proteins (3 VH4-34-utilizing Fabs, 3 VH1-69 Fabs, 1 VH1-8 Fab, 1 VH3-21 Fab, and the only VH2-26 Fab). All Fab-protein interactions were analyzed by biclustering after adaptation of the Bimax algorithm used in gene expression studies to the protein microarray platform. 28 biclusters involving 11 Fabs and 21 proteins were identifed. 11 of the bicluster proteins have a nuclear localization; the cellular localization of 6 proteins is unknown. Subsequent hierarchical clustering of the biclusters distinguished three separate clusters. The DILIMOT algorithm identifed GxAxSxA as a potential consensus protein motif among 8 proteins clustering together (Scons=5.29×10-23, p=3.83×10-8). Although the arrays used here carried the most comprehensive assembly of proteins available, they represent only a small fraction of the human proteome. Therefore, homologues to recognized proteins were identified by BLAST search and included, among others, the paraneoplastic neuronal autoantigen Ma1 as being potentially recognized by 4 lymphomas, including a primary CNS DLBCL, and 2 cell wall proteins of pathogenic bacteria. There was no preponderance of any particular NHL entity within the biclusters, except a group of 3 MZL Fabs. Each of these MZL BCR utilized VH1-69 and Vk3-20, and all 3 were classified as polyreactive with very similar protein recognition patterns, including calcium binding and coiled domain 1 and the autoantigens cardiolipin and Ro-60/SS-A. This study of a broad selection of lymphoma types and BCR structures establishes protein microarrays as a novel and valuable platform to study antigen recognition by lymphoma cells in an unbiased and quantitative fashion, and thereby to deduct a comprehensive view of antigen stimulation in iNHL development. The currently available array generations permit unbiased identification of directly recognized candidate autoantigens and potentially bound homologues in appr. 60% of cases. The remaining cases may recognize carbohydrate and/or microbial antigens not represented on the array. In addition, the development of appropriate bioinformatic tools within this study permitted to define recurrent oligo- and polyreactivity patterns of antigen recognition by lymphoma BCR. With the possible exception of MZL, these patterns appear to operate across various lymphoma entities, generally suggesting two different components in lymphoma development: A requirement for BCR-mediated stimulation in the majority of cases which may occur during various scenarios and may be specific or follow definable patterns of cross-reactivity, and full malignant transformation of the stimulated cell by genetic alterations. The latter step would be expected to define the lymphoma type through the nature of the oncogenic event and the maturation stage of the cell of origin. Disclosures: No relevant conflicts of interest to declare.

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Rathipriya, R., and K. Thangavel. "A Discrete Artificial Bees Colony Inspired Biclustering Algorithm." International Journal of Swarm Intelligence Research 3, no. 1 (January 2012): 30–42. http://dx.doi.org/10.4018/jsir.2012010102.

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Biclustering methods are the potential data mining technique that has been suggested to identify local patterns in the data. Biclustering algorithms are used for mining the web usage data which can determine a group of users which are correlated under a subset of pages of a web site. Recently, many blistering methods based on meta-heuristics have been proposed. Most use the Mean Squared Residue as merit function but interesting and relevant patterns such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of pattern since commonly the web users can present a similar behavior although their interest levels vary in different ranges or magnitudes. In this paper a new correlation based fitness function is designed to extract shifting and scaling browsing patterns. The proposed work uses a discrete version of Artificial Bee Colony optimization algorithm for biclustering of web usage data to produce optimal biclusters (i.e., highly correlated biclusters). It’s demonstrated on real dataset and its results show that proposed approach can find significant biclusters of high quality and has better convergence performance than Binary Particle Swarm Optimization (BPSO).

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Zhao, Jian, Maoyuan Sun, Francine Chen, and Patrick Chiu. "BiDots: Visual Exploration of Weighted Biclusters." IEEE Transactions on Visualization and Computer Graphics 24, no. 1 (January 2018): 195–204. http://dx.doi.org/10.1109/tvcg.2017.2744458.

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Journal articles on the topic 'Bicluter'

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