AIMC Topic: Genome, Human

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Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.

Nature communications Nov 15, 2023
The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant protein...
Humans Genome, Human Transcription, Genetic Polyadenylation Deep Learning Nucleotides
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Improving variant calling using population data and deep learning.

BMC bioinformatics May 12, 2023
Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to ...
Gene Frequency High-Throughput Nucleotide Sequencing Genome, Human Deep Learning Genome-Wide Association Study Polymorphism, Single Nucleotide Whole Genome Sequencing Humans
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Cell type-specific interpretation of noncoding variants using deep learning-based methods.

GigaScience Mar 27, 2023
Interpretation of noncoding genomic variants is one of the most important challenges in human genetics. Machine learning methods have emerged recently as a powerful tool to solve this problem. State-of-the-art approaches allow prediction of transcrip...
Deep Learning Genome, Human Neural Networks, Computer Machine Learning Humans
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Cue: a deep-learning framework for structural variant discovery and genotyping.

Nature methods Mar 23, 2023
Structural variants (SVs) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine. Existing SV callers rely on hand-engineered features and heuristics to model SVs, w...
Deep Learning Genotype Humans Software Genomic Structural Variation Cues Genome, Human
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Detecting genomic deletions from high-throughput sequence data with unsupervised learning.

BMC bioinformatics Jan 27, 2023
BACKGROUND: Structural variation (SV), which ranges from 50 bp to [Formula: see text] 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replicati...
High-Throughput Nucleotide Sequencing Genome, Human Genomic Structural Variation Sequence Analysis, DNA Unsupervised Machine Learning Software Genomics Humans
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Perspectives on the future of dysmorphology.

American journal of medical genetics. Part A Dec 9, 2022
The field of clinical genetics and genomics continues to evolve. In the past few decades, milestones like the initial sequencing of the human genome, dramatic changes in sequencing technologies, and the introduction of artificial intelligence, have u...
Humans Artificial Intelligence Genome, Human Genomics
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Context dependent prediction in DNA sequence using neural networks.

PeerJ Sep 20, 2022
One way to better understand the structure in DNA is by learning to predict the sequence. Here, we trained a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model was a neural n...
DNA Base Sequence Genome, Human Nucleosomes Animals Humans Mice Neural Networks, Computer
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Automated filtering of genome-wide large deletions through an ensemble deep learning framework.

Methods (San Diego, Calif.) Aug 28, 2022
Computational methods based on whole genome linked-reads and short-reads have been successful in genome assembly and detection of structural variants (SVs). Numerous variant callers that rely on linked-reads and short reads can detect genetic variati...
Sequence Analysis Humans Base Sequence Deep Learning Genome, Human Sequence Analysis, DNA High-Throughput Nucleotide Sequencing
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Haplotype and population structure inference using neural networks in whole-genome sequencing data.

Genome research Aug 25, 2022
Accurate inference of population structure is important in many studies of population genetics. Here we present HaploNet, a method for performing dimensionality reduction and clustering of genetic data. The method is based on local clustering of phas...
Humans Neural Networks, Computer Principal Component Analysis Genome, Human Whole Genome Sequencing Genetics, Population Haplotypes
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A review of deep learning applications in human genomics using next-generation sequencing data.

Human genomics Jul 25, 2022
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, arti...
Deep Learning Genomics Humans Artificial Intelligence High-Throughput Nucleotide Sequencing Genome, Human
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