AIMC Topic: Genome

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Mouse4mC-BGRU: Deep learning for predicting DNA N4-methylcytosine sites in mouse genome.

Methods (San Diego, Calif.) Jan 31, 2022
DNA N4-methylcytosine (4mC) is an important DNA modification and plays a crucial role in a variety of biological processes. Accurate 4mC site identification is fundamental to improving the understanding of 4mC biological functions and mechanisms. How...
Machine Learning Mice DNA Genome Animals Deep Learning
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Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes.

Methods (San Diego, Calif.) Dec 13, 2021
As one of the most common post-transcriptional epigenetic modifications, N6-methyladenine (6 mA), plays an essential role in various cellular processes and disease pathogenesis. Therefore, accurately identifying 6 mA modifications is necessary for a ...
Genome Epigenesis, Genetic Neural Networks, Computer DNA Software
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BreakNet: detecting deletions using long reads and a deep learning approach.

BMC bioinformatics Dec 2, 2021
BACKGROUND: Structural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based...
Genome Humans Sequence Analysis, DNA Software High-Throughput Nucleotide Sequencing Deep Learning
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Spliceator: multi-species splice site prediction using convolutional neural networks.

BMC bioinformatics Nov 23, 2021
BACKGROUND: Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non...
Algorithms Animals Genome Humans Neural Networks, Computer
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Interpretable machine learning for genomics.

Human genetics Oct 20, 2021
High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced sta...
Precision Medicine Genomics Genome Machine Learning Algorithms Humans
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Deep learning allows genome-scale prediction of Michaelis constants from structural features.

PLoS biology Oct 19, 2021
The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estima...
Neural Networks, Computer Models, Biological Metabolomics Enzymes Deep Learning Substrate Specificity Genome Kinetics Databases, Genetic
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Effective gene expression prediction from sequence by integrating long-range interactions.

Nature methods Oct 4, 2021
How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction a...
Genome Quantitative Trait Loci Gene Expression Regulation Nerve Net Epigenesis, Genetic Databases, Genetic Cell Line DNA Mice Genomics Machine Learning Animals Humans
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Biologically relevant transfer learning improves transcription factor binding prediction.

Genome biology Sep 27, 2021
BACKGROUND: Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall...
Genome Chromatin Immunoprecipitation Sequencing Machine Learning Transcription Factors
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EditPredict: Prediction of RNA editable sites with convolutional neural network.

Genomics Sep 23, 2021
RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to pr...
RNA Editing RNA-Seq RNA Neural Networks, Computer Genome Animals
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Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens.

Nature communications Aug 25, 2021
Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great ...
Gene Editing Deep Learning Humans Base Pairing Gene Library High-Throughput Nucleotide Sequencing Algorithms Genome
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