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Noise-Resistant Bicluster Recognition

Huan Sun, Gengxin Miao, Xifeng Yan
2013 2013 IEEE 13th International Conference on Data Mining  
Intuition: Bicluster pattern Based on Sparse Autoencoder, we enhance: • Robustness against noiseNoise outside bicluster pattern: • Noise inside bicluster pattern: Allow more false negative  ...  Input Output Feature learning: Sparse Autoencoder (SAE) Based on Sparse Autoencoder, we enhance: • Robustness against noiseNoise outside bicluster pattern: • Noise inside bicluster pattern:  ... 
doi:10.1109/icdm.2013.34 dblp:conf/icdm/SunMY13 fatcat:cng7yyb5ybelvbuxgjbuifvsuu

Evaluation of Plaid Models in Biclustering of Gene Expression Data

Hamid Alavi Majd, Soodeh Shahsavari, Ahmad Reza Baghestani, Seyyed Mohammad Tabatabaei, Naghme Khadem Bashi, Mostafa Rezaei Tavirani, Mohsen Hamidpour
2016 Scientifica  
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  ...  because when there is a moderate or big noise in the data, it cannot find good biclusters.  ...  Clustering has been one of the most important techniques used for detecting pattern recognition and could find groups with similar expression patterns [3] .  ... 
doi:10.1155/2016/3059767 pmid:27051553 pmcid:PMC4804094 fatcat:r5g3lfqovfdtzk55lfqwtsjnx4

Discovering biclusters in gene expression data based on high-dimensional linear geometries

Xiangchao Gan, Alan Liew, Hong Yan
2008 BMC Bioinformatics  
Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. Results: In this paper, we present a novel geometric perspective for the biclustering problem.  ...  This geometric viewpoint also inspires us to propose a generic bicluster pattern, i.e. the linear coherent model that unifies the seemingly incompatible additive and multiplicative bicluster models.  ...  To test noise resistance of our method, we embedded the biclusters into a noisy background generated by a uniform distribution U (-5, 5) . Gaussian noise with variance of 0.  ... 
doi:10.1186/1471-2105-9-209 pmid:18433477 pmcid:PMC2386490 fatcat:g5ykshw5z5d2hk22bg4yjrqb2m

Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting [chapter]

Yi Shi, Zhipeng Cai, Guohui Lin, Dale Schuurmans
2009 Lecture Notes in Computer Science  
In this paper, we propose a novel bi-clustering algorithm that discovers linear coherent biclusters, based on first detecting linear correlations between pairs of gene expression profiles, then identifying  ...  However, the sparse data problem can be addressed by applying more advanced image analysis techniques such as the sparse resistant Hough transform or other feature recognition techniques.  ...  As one can see, the LCBD algorithm is robust to noise even at noise level 25%.  ... 
doi:10.1007/978-3-642-02026-1_7 fatcat:gkeqcefjejh23kp6lc4wmtji3q

Enrichment analysis on regulatory subspaces: a novel direction for the superior description of cellular responses to SARS-CoV-2 [article]

Pedro P. Rodrigues, Rafael S. Costa, Rui Henriques
2021 bioRxiv   pre-print
Statement: The enrichment analysis of discriminative cell transcriptional responses to SARS-CoV-2 infection using biclustering produces a broader set of superiorly enriched GO terms and KEGG pathways against  ...  Results: Gathered results show that, although clustering and predictive algorithms aid classic functional enrichment analysis, recent pattern-based biclustering algorithms significantly improve the number  ...  ij is the noise factor, generally a bounded deviation from expectations, η ij ∈ [−δ/2, δ/2].  ... 
doi:10.1101/2021.12.15.472466 fatcat:nfanpkjwbnhb3ju2dg5ms24uxq

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields [chapter]

Dmitry I. Ignatov
2015 Communications in Computer and Information Science  
Apart from being noise-resistance, a stable group does not collapse (e.g., merge with a different group, split into several independent subgroups) when a few members of the group stop attending the target  ...  [123] proposed Navigala, a navigation-based approach for supervised classification, and applied it to noisy symbol recognition.  ... 
doi:10.1007/978-3-319-25485-2_3 fatcat:m2zad3btkjhkja2mdwmjfbkahi

Identification of isolated or mixed strains from long reads: a challenge met on Streptococcus thermophilus using a MinION sequencer

Grégoire Siekaniec, Emeline Roux, Téo Lemane, Eric Guédon, Jacques Nicolas
2021 Microbial Genomics  
This study aimed to provide efficient recognition of bacterial strains on personal computers from MinION (Nanopore) long read data.  ...  ghost recognitions.  ...  Another recent application of clustering close strains is in the inference of antibiotic resistance and susceptibility [50] .  ... 
doi:10.1099/mgen.0.000654 pmid:34812718 pmcid:PMC8743539 fatcat:66o3cea2n5ez5jy4v4ovkfrslm

Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data [chapter]

Almas Jabeen, Nadeem Ahmad, Khalid Raza
2017 Lecture Notes in Computational Vision and Biomechanics  
and music/audio signal recognition, etc [14] . not peer-reviewed) is the author/funder.  ...  Hence biclustering technique a variant of clustering emerged. Here each of the generated bicluster is formed by their respective feature subset.  ... 
doi:10.1007/978-3-319-65981-7_6 fatcat:ybc2r3cx5vdsnexel3bqq3rinm

Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data [article]

Almas Jabeen, Nadeem Ahmad, Khalid Raza
2017 bioRxiv   pre-print
and music/audio signal recognition, etc [14] .  ...  Hence biclustering technique a variant of clustering emerged. Here each of the generated bicluster is formed by their respective feature subset.  ... 
doi:10.1101/120592 fatcat:frdzqa4awvbuddkp4vxmyvyo2q

Systematic integration of protein affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile [article]

Shawn S Striker, Sierra F Wilferd, Erika M Lewis, Samantha A O'Connor, Chris L Plaisier
2022 bioRxiv   pre-print
Likewise, coherent FFL network motifs have also been associated with enhanced drug resistance 47 .  ...  Therefore TF family members from the TFClass database 28 with a known DNA recognition motif can be used as a proxy for a TF with no known DNA recognition motif.  ... 
doi:10.1101/2022.07.22.501139 fatcat:q7fmtjk2ibdx5hzvhjlitze5uu

Inferring the relation between transcriptional and posttranscriptional regulation from expression compendia

Ivan Ishchukov, Yan Wu, Sandra Van Puyvelde, Jos Vanderleyden, Kathleen Marchal
2014 BMC Microbiology  
This indicates that using modules instead of individual profiles helps reducing the noise and makes the assignments more robust.  ...  Module inference To infer modules, we relied on a previously developed global biclustering algorithm (ISA [17, 18] ).  ... 
doi:10.1186/1471-2180-14-14 pmid:24467879 pmcid:PMC3948049 fatcat:jlrshfz6cfdazn3jyazuldf4ki

On the Role of Clustering and Visualization Techniques in Gene Microarray Data

Angelo Ciaramella, Antonino Staiano
2019 Algorithms  
Moreover, gene expression data typically involve an enormous quantity of noise; nonetheless, K-means forces each gene into a cluster that can lead the algorithm to be noise prone [30, 31] .  ...  The criterion considers all possible pairs of distances between samples in the clusters, and is thus far more accurate and resistant to outliers.  ... 
doi:10.3390/a12060123 fatcat:bod6vdo4m5dfnhtzsjavqt4viu

Network-Based Analysis of OMICs Data to Understand the HIV–Host Interaction

Sergey Ivanov, Alexey Lagunin, Dmitry Filimonov, Olga Tarasova
2020 Frontiers in Microbiology  
To overcome HIV drug resistance, along with the approaches aimed at the HIV drug resistance prediction based on the HIV genotype (Riemenschneider and Heider, 2016; Zazzi et al., 2016; Tarasova and Poroikov  ...  These biclusters formed a strongly connected subnetwork containing 7 HIV-1 and 19 human proteins.  ... 
doi:10.3389/fmicb.2020.01314 pmid:32625189 pmcid:PMC7311653 fatcat:dljf5aw2lbgj7lkponfyebmwbe

Integration of Metabolomic and Proteomic Phenotypes

Stefanie Wienkoop, Katja Morgenthal, Florian Wolschin, Matthias Scholz, Joachim Selbig, Wolfram Weckwerth
2008 Molecular & Cellular Proteomics  
Eventually, sample pattern recognition and correlation network topology analysis allowed for the detection of specific metabolite-protein co-regulation and assignment of a circadian output regulated RNA-binding  ...  For this diagram the weights are normalized and visualized in a biclustering diagram.  ...  Kim et al. (71) demonstrated that over-expression of a glycine-rich RNA-binding protein resulted in enhanced cold-shock resistance in Escherichia coli.  ... 
doi:10.1074/mcp.m700273-mcp200 pmid:18445580 pmcid:PMC2556022 fatcat:ig6gp46by5eajfentfdvb7bwhm

Molecular estimation of neurodegeneration pseudotime in older brains

Sumit Mukherjee, Laura Heath, Christoph Preuss, Suman Jayadev, Gwenn A Garden, Anna K Greenwood, Solveig K Sieberts, Philip L De Jager, Nilüfer Ertekin-Taner, Gregory W Carter, Lara M Mangravite, Benjamin A Logsdon
2020 Nature Communications  
We hypothesize this group represents a disease resistant state to the disease. b Biclustering results of average expression from each disease state, with increased expression of a gene cluster (Cluster  ...  the gene sets identified in the biclustering of the Mayo RNA-seq data ( Supplementary Fig. 25 ).  ... 
doi:10.1038/s41467-020-19622-y pmid:33188183 pmcid:PMC7666177 fatcat:r5powg3pxna25nkxlcc23adokm
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