AIMC Topic: Base Sequence

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DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2022
Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process...
Algorithms DNA Privacy Base Sequence Neural Networks, Computer
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A brief review of machine learning methods for RNA methylation sites prediction.

Methods (San Diego, Calif.) Mar 3, 2022
Thanks to the tremendous advancement of deep sequencing and large-scale profiling, epitranscriptomics has become a rapidly growing field. As one of the most important parts of epitranscriptomics, ribonucleic acid (RNA) methylation has been focused on...
Machine Learning RNA Methylation Base Sequence
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DeLUCS: Deep learning for unsupervised clustering of DNA sequences.

PloS one Jan 21, 2022
We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of prim...
Genome, Mitochondrial Genome, Bacterial Cluster Analysis Base Sequence Genome, Viral Humans Deep Learning Sequence Analysis, DNA Neural Networks, Computer Animals
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Using k-mer embeddings learned from a Skip-gram based neural network for building a cross-species DNA N6-methyladenine site prediction model.

Plant molecular biology Nov 29, 2021
This study used k-mer embeddings as effective feature to identify DNA N6-Methyladenine sites in plant genomes and obtained improved performance without substantial effort in feature extraction, combination and selection. Identification of DNA N6-meth...
Neural Networks, Computer Algorithms Surveys and Questionnaires Adenine DNA, Plant Plants Nucleotides Base Sequence ROC Curve Databases, Genetic Models, Biological
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T Cell Epitope Prediction and Its Application to Immunotherapy.

Frontiers in immunology Sep 15, 2021
T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for ep...
Base Sequence Antigen Presentation Computational Biology T-Cell Antigen Receptor Specificity Immunotherapy Antigens, Neoplasm Models, Molecular Histocompatibility Antigens Class I Sequence Analysis, DNA Mass Spectrometry Epitopes, T-Lymphocyte Peptides Humans Receptors, Antigen, T-Cell Neural Networks, Computer Neoplasms
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NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.

Genome biology Sep 6, 2021
Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs usin...
Nanopore Sequencing INDEL Mutation Chromosome Mapping Major Histocompatibility Complex Polymorphism, Single Nucleotide Nanoparticles Humans Neural Networks, Computer Haplotypes Base Sequence Genome, Human Benchmarking Alleles High-Throughput Nucleotide Sequencing
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Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Genome biology Aug 16, 2021
Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chr...
Humans Neural Networks, Computer Genome Leukemia Chromatin Machine Learning Computational Biology Base Sequence
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Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods.

Nature communications Aug 12, 2021
Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the seq...
Gene Library Caenorhabditis elegans Cytidine Deaminase Gene Editing Binding Sites Codon HEK293 Cells Uracil-DNA Glycosidase Base Sequence Machine Learning Female Reproducibility of Results Escherichia coli CRISPR-Cas Systems Animals Mice Humans
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A deep learning model for predicting next-generation sequencing depth from DNA sequence.

Nature communications Jul 19, 2021
Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization...
Sequence Analysis, DNA DNA High-Throughput Nucleotide Sequencing DNA Probes Base Sequence Deep Learning Genomics Polymorphism, Single Nucleotide Humans
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Analysis of DNA Sequence Classification Using CNN and Hybrid Models.

Computational and mathematical methods in medicine Jul 15, 2021
In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification o...
Base Sequence Databases, Nucleic Acid Humans Neural Networks, Computer Pandemics Computational Biology DNA, Viral Sequence Analysis, DNA High-Throughput Nucleotide Sequencing COVID-19 SARS-CoV-2 Deep Learning
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