AIMC Topic: Genome

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SVision: a deep learning approach to resolve complex structural variants.

Nature methods Sep 16, 2022
Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequ...
Deep Learning Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Genome
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Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

Molecular plant Sep 7, 2022
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by inco...
Genome Big Data Plant Breeding Genomics Artificial Intelligence Genotype Selection, Genetic Phenotype
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Quartet Based Gene Tree Imputation Using Deep Learning Improves Phylogenomic Analyses Despite Missing Data.

Journal of computational biology : a journal of computational molecular cell biology Sep 1, 2022
Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene...
Computer Simulation Phylogeny Genome Genetic Speciation Deep Learning Models, Genetic
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Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America Aug 26, 2022
Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the databas...
DNA DNA Barcoding, Taxonomic Deep Learning Genomics Base Sequence Genome Software Algorithms
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Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning.

Scientific reports Jul 8, 2022
The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data....
Caenorhabditis elegans Genome Saccharomyces cerevisiae Drosophila melanogaster Animals Mice Machine Learning Phenotype
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DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes.

Computers in biology and medicine Jun 20, 2022
The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The ...
Metabolic Networks and Pathways Genome Databases, Factual Deep Learning Neural Networks, Computer
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Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization.

Journal of molecular biology Jun 2, 2022
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. Howeve...
Machine Learning Imaging, Three-Dimensional Chromosome Mapping Chromatin Genome Sequence Analysis, DNA Chromosomes Cell Nucleus
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AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning.

BMC bioinformatics Apr 25, 2022
BACKGROUND: The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statisti...
Genome Humans SARS-CoV-2 Deep Learning Pandemics COVID-19
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InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification.

Genes Mar 30, 2022
Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, ...
CCCTC-Binding Factor Genome Neural Networks, Computer Chromatin Supervised Machine Learning
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Integrating Long-Range Regulatory Interactions to Predict Gene Expression Using Graph Convolutional Networks.

Journal of computational biology : a journal of computational molecular cell biology Mar 21, 2022
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Humans Genome Neural Networks, Computer Epigenesis, Genetic Genomics Gene Expression
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