AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Nucleotide Motifs

Showing 41 to 50 of 50 articles

Clear Filters

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 Nov 1, 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...
Humans Algorithms Protein Binding Computational Biology Cluster Analysis Binding Sites Nucleotide Motifs Chromatin Immunoprecipitation Transcription Factors Sequence Analysis, DNA Fuzzy Logic
View on PubMed DOI

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 ...
Nucleic Acid Conformation Binding Sites Nucleotide Motifs Neural Networks, Computer RNA-Binding Proteins Algorithms User-Computer Interface RNA Protein Binding Computational Biology
View on PubMed DOI

DiscMLA: An Efficient Discriminative Motif Learning Algorithm over High-Throughput Datasets.

IEEE/ACM transactions on computational biology and bioinformatics Jan 1, 2018
The transcription factors (TFs) can activate or suppress gene expression by binding to specific sites, hence are crucial regulatory elements for transcription. Recently, series of discriminative motif finders have been tailored to offering promising ...
Databases, Genetic Cell Line, Tumor Chromatin Immunoprecipitation Humans Computational Biology Sequence Analysis, DNA Machine Learning High-Throughput Nucleotide Sequencing Algorithms Nucleotide Motifs Transcription Factors Area Under Curve
View on PubMed DOI

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 Computational Biology Base Sequence RNA Splicing Exons Position-Specific Scoring Matrices Genome, Human Introns Nucleotide Motifs
View on PubMed DOI

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 Machine Learning Promoter Regions, Genetic Animals Drosophila melanogaster Transcription Factors Nucleotide Motifs Sequence Analysis, DNA Embryonic Development
View on PubMed DOI

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.

Bioinformatics (Oxford, England) Oct 1, 2017
MOTIVATION: Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of ...
Sequence Analysis, DNA DNA DNA-Binding Proteins Nucleic Acid Conformation Genomics Transcription Factors Support Vector Machine Protein Array Analysis Binding Sites Nucleotide Motifs Software Protein Binding
View on PubMed DOI

A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.

Nucleic acids research Aug 21, 2017
Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-s...
Algorithms Reproducibility of Results Humans Machine Learning Base Sequence Mutation Binding Sites Protein Binding Internet Nucleotide Motifs High-Throughput Nucleotide Sequencing Computational Biology Binding, Competitive Gene Expression Regulation RNA-Binding Proteins 3' Untranslated Regions
View on PubMed DOI

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...
RNA Binding Sites Nucleotide Motifs RNA-Binding Proteins Sequence Analysis, RNA Neural Networks, Computer Data Mining Machine Learning Algorithms Protein Binding
View on PubMed DOI

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 Feb 1, 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 Nucleotide Motifs Humans Machine Learning Algorithms
View on PubMed DOI

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 Support Vector Machine Databases, Genetic Cluster Analysis MicroRNAs
View on PubMed DOI
Popular Topics
Recent Journals