Fitting a Mixture Model By Expectation Maximization To Discover Motifs In Biopolymer

By Timothy L. Bailey, Charles Peter Elkan. 1994

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Citations in the corpus

Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure (2019, background): “Learning the latent structure of data has a long standing history in machine learning, including Expectation-Maximization (Bailey, Elkan, and others 1994), topic modelling (Blei 2012), latent-SVM (Felzenszwalb, McAllester, and Ramanan 2008), and more recently, variational autoen-coders (VAE) (Kingma”
Predicting effective microRNA target sites in mammalian mRNAs (2015, unknown): “…miRNA-transfection or CLIP-seq models) ( Wen et al., 2011 ), DIANA-microT-CDS (September 2013) ( Reczko et al., 2012 ), ElMMo v5 (January 2011) ( Gaidatzis et al., 2007 ), MBSTAR (all predictions) ( Bandyopadhyay et al., 2015 ), miRanda-MicroCosm v5 ( Griffiths-Jones et al., 2008 ), miRmap v1.”
Master Transcription Factors Determine Cell-Type-Specific Responses to TGF-β Signaling (2011, unknown): “De novo motif discovery was performed (Bailey and Elkan, 1994) on sites bound by SMAD3 and sites bound by OCT4.”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, methodology): “For each group of operons regulated by the same transcription factor, we analyzed approximately 150 base pairs upstream of the transcription start site with the MEME multiple alignment system [34].”
Genome-Wide Comparative Analysis and Expression Pattern of TCP Gene Families in Arabidopsis thaliana and Oryza sativa (2007, unknown): “In addition, to search for other motifs shared by some TCP members, we used MEME and MAST (version 3.5. 4; http://meme.sdsc.edu/meme/meme.html; Bailey and Elkan 1994).”
Diverse RNA-Binding Proteins Interact with Functionally Related Sets of RNAs, Suggesting an Extensive Regulatory System (2008, methodology): “In brief, REFINE identifies all hexamers that are significantly enriched in putative 59- and 39-UTR regions of targets over nontargets, filters out regions of target sequences that are relatively devoid of such hexamers, and then applies the ‘‘multiple expectation maximization for motif elicitation’”
From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis (2020, methodology): “Another widely used tool is MEME suite [115, 116], which includes FIMO [117] to search for individual motifs, MAST [118] for aggregating search results from multiple motifs, and MCAST [119] to infer regulatory modules formed by multiple motifs.”
iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition (2014, methodology): “To further investigate the sequence mode in 54 promoters, the MEME (Multiple Em for Motif Elicitation) (88) was used to discover the consensus motifs in 54 promoters.”
De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing (2017, methodology): “The meme algorithm 33 is then applied to these sequences in the ZOOPS model.”
Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding (2013, unknown): “Analysis of Enriched Motifs on mRNA Targets Identifies the Major Interaction Site for Many miRNAs To identify additional features of miRNA-binding sites, we sought statistically overrepresented sequence elements in the CLASH targets of each miRNA using MEME (Bailey and Elkan, 1994) (Figure 4A).”
MitoFates: Improved Prediction of Mitochondrial Targeting Sequences and Their Cleavage Sites (2015, methodology): “MEME (51) derived PWM motifs to model peptidase cleavage”
Genetic reconstruction of a functional transcriptional regulatory network (2007, methodology): “Next, we used AlignACE 16 , MEME 17 and MDscan 18 for de novo promoter motif discovery ( Supplementary Methods ).”