AIMC Topic: Nucleotides

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COSMO-RS-Based Descriptors for the Machine Learning-Enabled Screening of Nucleotide Analogue Drugs against SARS-CoV-2.

The journal of physical chemistry letters Oct 26, 2020
Chemical similarity-based approaches employed to repurpose or develop new treatments for emerging diseases, such as COVID-19, correlates molecular structure-based descriptors of drugs with those of a physiological counterpart or clinical phenotype. W...
RNA, Viral Humans Drug Evaluation, Preclinical Machine Learning Quantum Theory Betacoronavirus SARS-CoV-2 Pandemics Pneumonia, Viral COVID-19 Molecular Docking Simulation RNA-Dependent RNA Polymerase Principal Component Analysis Coronavirus Infections Nucleotides Binding Sites
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Frequency spectra characterization of noncoding human genomic sequences.

Genes & genomics Aug 31, 2020
BACKGROUND: Noncoding sequences have been demonstrated to possess regulatory functions. Its classification is challenging because they do not show well-defined nucleotide patterns that can correlate with their biological functions. Genomic signal pro...
Genome, Human Regulatory Sequences, Nucleic Acid Algorithms Humans Genomics Machine Learning Nucleotides
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Predicting host taxonomic information from viral genomes: A comparison of feature representations.

PLoS computational biology May 26, 2020
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...
Support Vector Machine Sequence Analysis, DNA Area Under Curve Phylogeny Bacteria Computational Biology Genome, Viral Viruses Caudovirales Nucleotides Metagenomics Linear Models Algorithms Databases, Factual
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Depression phenotype identified by using single nucleotide exact amplicon sequence variants of the human gut microbiome.

Molecular psychiatry Jan 27, 2020
Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype (DEPR) could be identified from healthy reference subjects (NODEP). Microbial DNA in stool samples o...
Gastrointestinal Microbiome RNA, Ribosomal, 16S Nucleotides High-Throughput Nucleotide Sequencing Machine Learning Phenotype Depression Humans
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Effective machine-learning assembly for next-generation amplicon sequencing with very low coverage.

BMC bioinformatics Dec 11, 2019
BACKGROUND: In short-read DNA sequencing experiments, the read coverage is a key parameter to successfully assemble the reads and reconstruct the sequence of the input DNA. When coverage is very low, the original sequence reconstruction from the read...
Macropodidae Algorithms Animals Nucleotides Machine Learning High-Throughput Nucleotide Sequencing Base Sequence Genome, Mitochondrial
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FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Methods (San Diego, Calif.) Mar 26, 2019
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, call...
Neural Networks, Computer Software Deep Learning Binding Sites Protein Binding Chromatin Transcription Factors Nucleotides
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Recognition Tunneling of Canonical and Modified RNA Nucleotides for Their Identification with the Aid of Machine Learning.

ACS nano Jun 28, 2018
In the present study, we demonstrate a tunneling nanogap technique to identify individual RNA nucleotides, which can be used as a mechanism to read the nucleobases for direct sequencing of RNA in a solid-state nanopore. The tunneling nanogap is compo...
Surface Properties Nanopores Microscopy, Scanning Tunneling Nanotechnology Machine Learning Nucleotides RNA Particle Size Molecular Structure
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The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies.

PloS one Oct 5, 2017
Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism's development, disease, and evolution. The building blocks of this complex molecular machinery are an organism's genomic...
Support Vector Machine Machine Learning Nucleotides Transcription Factors Binding Sites
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Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition.

Computer methods and programs in biomedicine May 26, 2017
BACKGROUND AND OBJECTIVES: Enhancers are pivotal DNA elements, which are widely used in eukaryotes for activation of transcription genes. On the basis of enhancer strength, they are further classified into two groups; strong enhancers and weak enhanc...
Neural Networks, Computer Algorithms Nucleotides Computer Simulation Enhancer Elements, Genetic Computational Biology Support Vector Machine
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Identifying N-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine.

Scientific reports Apr 25, 2017
N6-methyladenosine (mA) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear transl...
Arabidopsis Humans Algorithms Datasets as Topic RNA, Messenger Computational Biology Saccharomyces cerevisiae Support Vector Machine Adenosine Nucleotides
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