AIMC Topic: Nucleic Acid Conformation

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RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks.

PLoS computational biology Nov 27, 2018
Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA st...
Neural Networks, Computer Hot Temperature Nucleic Acid Conformation Monte Carlo Method Molecular Dynamics Simulation RNA Datasets as Topic
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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 ...
Binding Sites Nucleotide Motifs RNA-Binding Proteins RNA Nucleic Acid Conformation Neural Networks, Computer User-Computer Interface Algorithms Protein Binding Computational Biology
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DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC.

Journal of theoretical biology May 16, 2018
A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting...
Support Vector Machine Databases, Protein DNA Algorithms Reproducibility of Results Computational Biology Nucleic Acid Conformation Amino Acids DNA-Binding Proteins Amino Acid Sequence Models, Molecular Protein Domains
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The first two mitochondrial genomes from Taeniopterygidae (Insecta: Plecoptera): Structural features and phylogenetic implications.

International journal of biological macromolecules Dec 29, 2017
The complete mitochondrial genomes (mitogenomes) of Taeniopteryx ugola and Doddsia occidentalis (Plecoptera: Taeniopterygidae) were firstly sequenced from the family Taeniopterygidae. The 15,353-bp long mitogenome of T. ugola and the 16,020-bp long m...
Genome, Mitochondrial Animals Lepidoptera Whole Genome Sequencing Phylogeny Nucleic Acid Conformation RNA, Ribosomal RNA, Transfer
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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...
Cluster Analysis RNA Base Sequence Computational Biology Algorithms Nucleic Acid Conformation RNA-Binding Proteins Reproducibility of Results Nucleotide Motifs Genomics RNA, Untranslated
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Sensitive detection of DNA methyltransferase using the dendritic rolling circle amplification-induced fluorescence.

Analytica chimica acta Dec 27, 2016
The analysis of DNA methylation and MTase activities is very important in the early clinical diagnosis of cancer, on purposes of providing insights into the mechanism of gene repression and developing novel drugs of treating methylation-related disea...
Fluorescence DNA, Catalytic DNA Methylation Nucleic Acid Amplification Techniques DNA Modification Methylases DNA Probes Nucleic Acid Conformation
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A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes.

Journal of integrative bioinformatics Dec 22, 2016
Identification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especi...
RNA Precursors Humans Genome, Human Artificial Intelligence Genome, Viral MicroRNAs Nucleic Acid Conformation ROC Curve Reverse Transcription
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Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

BMC bioinformatics Dec 15, 2016
BACKGROUND: MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays...
Humans Machine Learning Computer Simulation Animals Genomics Nucleic Acid Conformation Computational Biology Eukaryota MicroRNAs Genome Plants
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LBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop/bulge length.

BMC bioinformatics Nov 25, 2016
BACKGROUND: Dicer is necessary for the process of mature microRNA (miRNA) formation because the Dicer enzyme cleaves pre-miRNA correctly to generate miRNA with correct seed regions. Nonetheless, the mechanism underlying the selection of a Dicer cleav...
RNA Precursors DEAD-box RNA Helicases Ribonuclease III Base Sequence Nucleic Acid Conformation Humans Software Algorithms Support Vector Machine MicroRNAs Bayes Theorem
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A semi-supervised learning approach for RNA secondary structure prediction.

Computational biology and chemistry Feb 20, 2015
RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because o...
RNA Nucleic Acid Conformation Stochastic Processes Supervised Machine Learning
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