AIMC Topic: 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...
Protein Binding Area Under Curve Base Sequence Nucleotide Motifs Neural Networks, Computer Deep Learning
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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...
Cluster Analysis Binding Sites Nucleotide Motifs Transcription Factors Sequence Analysis, DNA Fuzzy Logic Chromatin Immunoprecipitation Humans Algorithms Protein Binding Computational Biology
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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 ...
Neural Networks, Computer Algorithms Binding Sites Nucleotide Motifs RNA-Binding Proteins RNA User-Computer Interface Nucleic Acid Conformation Protein Binding Computational Biology
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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...
RNA Computational Biology Nucleotide Motifs Algorithms Base Sequence RNA-Binding Proteins RNA, Untranslated Reproducibility of Results Cluster Analysis Nucleic Acid Conformation Genomics
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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...
Base Sequence Genome, Human Introns Humans Machine Learning Computational Biology Position-Specific Scoring Matrices Nucleotide Motifs RNA Splicing Exons
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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...
Nucleotide Motifs Enhancer Elements, Genetic Genes, Reporter Gene Expression Regulation, Developmental Histone Code Deoxyribonuclease I Embryonic Development Promoter Regions, Genetic Drosophila melanogaster Animals Transcription Factors Sequence Analysis, DNA Machine Learning
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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...
Humans MicroRNAs Machine Learning Phylogeny Exosomes Gene Ontology Algorithms Base Sequence Nucleotide Motifs Reproducibility of Results Animals Mice Cluster Analysis
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RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

BMC bioinformatics Feb 28, 2017
BACKGROUND: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recog...
Data Mining RNA Binding Sites Nucleotide Motifs Protein Binding RNA-Binding Proteins Sequence Analysis, RNA Neural Networks, Computer Machine Learning Algorithms
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V-ELMpiRNAPred: Identification of human piRNAs by the voting-based extreme learning machine (V-ELM) with a new hybrid feature.

Journal of bioinformatics and computational biology Jan 9, 2017
Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this p...
Databases, Genetic RNA, Small Interfering Humans Nucleotide Motifs Machine Learning Algorithms
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Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

Journal of integrative bioinformatics Dec 22, 2016
The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algor...
Plants Base Sequence Nucleotide Motifs Databases, Genetic Cluster Analysis MicroRNAs Support Vector Machine
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