AIMC Topic: Nucleotide Motifs

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Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach.

Genomics Dec 6, 2019
Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miR...
Genomics Liver MicroRNAs Muscle, Skeletal Animals Transcriptome RNA-Seq Machine Learning RNA Precursors Algorithms Nucleotide Motifs Genome Sequence Homology, Nucleic Acid Genetic Loci Molecular Sequence Annotation Sus scrofa
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Deciphering epigenomic code for cell differentiation using deep learning.

BMC genomics Sep 12, 2019
BACKGROUND: Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiatio...
CD4-Positive T-Lymphocytes Nucleotide Motifs Cell Lineage Deep Learning Conserved Sequence Epigenomics Histone Code Cell Differentiation Humans
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MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.

BMC bioinformatics May 1, 2019
BACKGROUND: Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these ...
Algorithms Protein Binding Transcription Factors Support Vector Machine Humans Binding Sites Nucleotide Motifs
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Prediction of binding property of RNA-binding proteins using multi-sized filters and multi-modal deep convolutional neural network.

PloS one Apr 26, 2019
RNA-binding proteins (RBPs) are important in gene expression regulations by post-transcriptional control of RNAs and immune system development and its function. Due to the help of sequencing technology, numerous RNA sequences are newly discovered wit...
Deep Learning Protein Binding Area Under Curve Neural Networks, Computer Base Sequence Nucleotide Motifs
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Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification.

Journal of computational biology : a journal of computational molecular cell biology Aug 22, 2018
The identification of transcription factor binding sites (TFBSs) is a problem for which computational methods offer great hope. Thus far, the expectation maximization (EM) technique has been successfully utilized in finding TFBSs in DNA sequences, bu...
Nucleotide Motifs Chromatin Immunoprecipitation Protein Binding Computational Biology Transcription Factors Cluster Analysis Binding Sites Sequence Analysis, DNA Fuzzy Logic Humans Algorithms
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Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.

BMC genomics Jul 3, 2018
BACKGROUND: RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence ...
Nucleotide Motifs RNA Nucleic Acid Conformation Binding Sites Neural Networks, Computer Computational Biology Algorithms User-Computer Interface RNA-Binding Proteins Protein Binding
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DotAligner: identification and clustering of RNA structure motifs.

Genome biology Dec 28, 2017
The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further e...
Computational Biology Algorithms Reproducibility of Results RNA-Binding Proteins Genomics Cluster Analysis RNA Base Sequence Nucleic Acid Conformation Nucleotide Motifs RNA, Untranslated
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Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

BMC bioinformatics Dec 1, 2017
BACKGROUND: Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human sp...
Humans Machine Learning Position-Specific Scoring Matrices Computational Biology Introns RNA Splicing Base Sequence Nucleotide Motifs Exons Genome, Human
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McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.

Genome biology Oct 26, 2017
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target g...
Enhancer Elements, Genetic Genes, Reporter Gene Expression Regulation, Developmental Histone Code Deoxyribonuclease I Promoter Regions, Genetic Drosophila melanogaster Machine Learning Nucleotide Motifs Animals Transcription Factors Sequence Analysis, DNA Embryonic Development
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Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms.

Interdisciplinary sciences, computational life sciences Jul 22, 2017
BACKGROUND: The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Re...
Phylogeny Mice Humans Cluster Analysis Gene Ontology Machine Learning Algorithms MicroRNAs Reproducibility of Results Nucleotide Motifs Exosomes Animals Base Sequence
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