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Robust biclustering by sparse singular value decomposition incorporating stability selection

Martin Sill, Sebastian Kaiser, Axel Benner, Annette Kopp-Schneider
2011 Computer applications in the biosciences : CABIOS  
Despite of huge diversity regarding the mathematical concepts of the different biclustering methods, many of them can be related to the singular value decomposition (SVD).  ...  Recently, a sparse SVD approach (SSVD) has been proposed to reveal biclusters in gene expression data. In this article, we propose to incorporate stability selection to improve this method.  ...  By incorporating the stability selection, a stopping criterion can be defined.  ... 
doi:10.1093/bioinformatics/btr322 pmid:21636597 fatcat:6yo7f4hbubbg5mwju2ikaoniti

Robust Integrative Biclustering for Multi-view Data [article]

W. Zhang, C. Wendt, R. Bowler, C. P. Hersh, S. E. Safo
2021 arXiv   pre-print
Our method, integrative sparse singular value decomposition (iSSVD), incorporates stability selection to control Type I error rates, estimates the probability of samples and variables to belong to a bicluster  ...  We extend an existing biclustering method based on sparse singular value decomposition for single-view data to data from multiple views.  ...  Finally, we incorporate the concept of stability selection to identify robust biclusters and control Type 1 error rates of falsely selecting samples and variables in a bicluster.  ... 
arXiv:2111.06209v1 fatcat:xezdt5n5yzchvooji6qoztiezq

bioNMF: a versatile tool for non-negative matrix factorization in biology

Alberto Pascual-Montano, Pedro Carmona-Saez, Monica Chagoyen, Francisco Tirado, Jose M Carazo, Roberto D Pascual-Marqui
2006 BMC Bioinformatics  
This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression.  ...  Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types  ...  Acknowledgements This work has been partially funded by the Spanish grants CICYT BFU2004-00217/BMC, GEN2003-20235-c05-05, CYTED-505PI0058, TIN2005-5619, PR27/05-13964-BSCH and a collaborative grant between  ... 
doi:10.1186/1471-2105-7-366 pmid:16875499 pmcid:PMC1550731 fatcat:5tyxoqbuezbfjbgaq6dg6xjvgy

Scalable Interpretable Multi-Response Regression via SEED [article]

Mohammad Taha Bahadori, Zemin Zheng, Yan Liu, Jinchi Lv
2016 arXiv   pre-print
In this paper, we suggest a scalable procedure called sequential estimation with eigen-decomposition (SEED) which needs only a single top-r singular value decomposition to find the optimal low-rank and  ...  sparse matrix by solving a sparse generalized eigenvalue problem.  ...  In fact, the above decomposition (2) for C * coincides with the singular value decomposition of XC * through different scalings on the singular vectors.  ... 
arXiv:1608.03686v1 fatcat:q5wibugdsrh2dp4l4pxva77gbe

A New Algorithm for Convex Biclustering and Its Extension to the Compositional Data [article]

Binhuan Wang, Lanqiu Yao, Jiyuan Hu, Huilin Li
2021 arXiv   pre-print
biclusters required by existing convex biclustering algorithms.  ...  Multiple biclustering algorithms have been developed in the past two decades, among which the convex biclustering can guarantee a global optimum by formulating in as a convex optimization problem.  ...  Acknowledgement Hu and Li's research is partially supported by a National Institute of Health (NIH) grant (5R01DK110014).  ... 
arXiv:2011.12182v2 fatcat:onylus2refexhmvanvlebuv5va

Biclustering on expression data: A review

Beatriz Pontes, Raúl Giráldez, Jesús S. Aguilar-Ruiz
2015 Journal of Biomedical Informatics  
Most of biclustering approaches use a measure or cost function that determines the quality of biclusters.  ...  Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters.  ...  Acknowledgement This research has been supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2011-28956.  ... 
doi:10.1016/j.jbi.2015.06.028 pmid:26160444 fatcat:3w5p45w4zzebxmnphdue67ueny

Non-Negative Factorization for Clustering of Microarray Data

Lucian Morgos
2014 International Journal of Computers Communications & Control  
Typically, gene expression data are formed by thousands of genes associated to tens or hundreds of samples.  ...  One solution for improving the class discovery efficiency is provided by data dimensionality reduction, where data is decomposed into lower dimensional factors, so that those factors approximate original  ...  Approach One of the frequently used dimensionality reduction approaches is given by singular values decomposition (SVD).  ... 
doi:10.15837/ijccc.2014.1.866 fatcat:dlbifvalnjfulhn5a6ece3wh6m

Matrix Reordering Methods for Table and Network Visualization

Michael Behrisch, Benjamin Bach, Nathalie Henry Riche, Tobias Schreck, Jean-Daniel Fekete
2016 Computer graphics forum (Print)  
Value Decomposition (SVD) step to derive a permutation.  ...  (d) Single Value Decomposition: Liu et al. rowMean = mean(M). Figure 11 : 11 Heuristic Approaches for Matrix Reordering. Figure 12 : 12 Example for Heuristic Matrix Reorderings.  ...  They found that for feature retrieval tasks, the Manhattan Distance is a robust choice and outperforms Jensen-Shannon Divergence and even the Euclidean Distance.  ... 
doi:10.1111/cgf.12935 fatcat:oaqptigpdja4fbemtowma3zpcm

On Bayesian new edge prediction and anomaly detection in computer networks

Silvia Metelli, Nicholas Heard
2019 Annals of Applied Statistics  
This work was supported in part by The Alan Turing Institute and the Lloyds Register Foundation Programme on Data-Centric Engineering.  ...  When performing sparse truncated-SVD incorporating stability selection, a dimensionality of K = 6 is automatically chosen.  ...  The commonly used spectral biclustering algorithm of Dhillon (2001) and Cho et al. (2004) calculates a truncated-singular value decomposition of (5.2) D −1/2 X AD −1/2 Y .  ... 
doi:10.1214/19-aoas1286 fatcat:kshb6cj6qngcbgqwkuwz3q6naa

Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition

Miaoyan Wang, Jonathan Fischer, Yun S. Song
2019 Annals of Applied Statistics  
Through simulation and application to the GTEx RNA-seq data from 53 human tissues, we show that MultiCluster identifies three-way interactions with high accuracy and robustness.  ...  We further develop a tensor projection procedure which detects covariate-related genes with high power, demonstrating the advantage of tensor-based methods in incorporating information across similar tissues  ...  This research is supported in part by a Math+X research grant from the Simons Foundation, a Packard Fellowship for Science and Engineering, and a National Institutes of Health grant R01-GM094402.  ... 
doi:10.1214/18-aoas1228 pmid:33381253 pmcid:PMC7771883 fatcat:bpq6h35zlnehrfhnhvauiaeeui

Inference of gene regulatory subnetworks from time course gene expression data

Xi-Jun Liang, Zhonghang Xia, Li-Wei Zhang, Fang-Xiang Wu
2012 BMC Bioinformatics  
Results: We propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference  ...  Particularly, we apply sparse singular value decomposition (SSVD) [17] on a general L* to identify the communities in GRNs. The NCI method is summarized in Algorithm 1.  ...  Consider the sigular value decompostion (SVD) of the matrix X X = U V T , (18) where U and V are orthogonal matrices consisting of singular vectors, and Σ is the diagnal matrix made up of the singular  ... 
doi:10.1186/1471-2105-13-s9-s3 pmid:22901088 pmcid:PMC3372453 fatcat:l6irizresjgjrjicdsq5sqtima

Learning the Structure of Biomedical Relationships from Unstructured Text

Bethany Percha, Russ B. Altman, K. Bretonnel Cohen
2015 PLoS Computational Biology  
Here we describe a novel algorithm, Ensemble Biclustering for Classification (EBC), that learns the structure of biomedical relationships automatically from text, overcoming differences in word choice  ...  LSA uses the singular value decomposition (SVD) [37] instead of ITCC to accomplish a similar goal, and has been applied in at least one case to corpus-level relationship extraction (a technique called  ...  Comparing EBC to Latent Semantic Analysis (LSA) To investigate how similar EBC's performance was to a more established method designed to solve a similar problem, we used the singular value decomposition  ... 
doi:10.1371/journal.pcbi.1004216 pmid:26219079 pmcid:PMC4517797 fatcat:lvc57v6l4jfehb2ppl7qyaim2u

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

Irene Sui Lan Zeng, Thomas Lumley
2018 Bioinformatics and Biology Insights  
The normalized singular value represents the square root of the first eigenvalue.  ...  Multiple factor analysis starts from a PCA on each block (type) of data and followed by jointly analyzing the singular-value normalized data using the global PCA.  ...  Integration occurs at the subject level: the input data are an original expression or sequence variables from the same subject, data are merged by subject ID.  ... 
doi:10.1177/1177932218759292 pmid:29497285 pmcid:PMC5824897 fatcat:nbknjl4qq5awrldy7natmg3h6y

Bioinformatics: Organisms from Venus, Technology from Jupiter, Algorithms from Mars

Bart De Moor, Kathleen Marchal, Janick Mathys, Yves Moreau
2003 European Journal of Control  
In this paper, we discuss datasets that are being generated by microarray technology, which makes it possible to measure in parallel the activity or expression of thousands of genes simultaneously.  ...  This year 2003 marks the 50 th anniversary of the discovery of the double helix structure of DNA, the basic building structure of all living organisms, by Crick and Watson, in the 1 page landmark paper  ...  In this Section, we will elaborate on some often used algorithms in microarray data analysis, including Principal Component Analysis (or the Singular Value Decomposition) and some classical and newly developed  ... 
doi:10.3166/ejc.9.237-278 fatcat:w26h4jvugjg35abzgqctb2yubi

A Biclustering Framework for Consensus Problems

Mariano Tepper, Guillermo Sapiro
2014 SIAM Journal of Imaging Sciences  
Among many tools for bi-clustering, see [35] and references therein, the Penalized Matrix Decomposition (PMD) [49] and the Sparse Singular Value Decomposition (SSVD) [28] have shown great promise  ...  Hence, the value of ε is not critical, our model being inherently robust to poor models, as shown in Section 2.  ... 
doi:10.1137/140967325 fatcat:umu2aqg6onaoxkq5yoea6ctjw4
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