AIMC Topic: Genetic Variation

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Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Briefings in bioinformatics Jul 25, 2024
Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key fact...
Computational Biology Epigenesis, Genetic High-Throughput Nucleotide Sequencing Genetic Variation Humans Deep Learning
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A comprehensive review of deep learning-based variant calling methods.

Briefings in functional genomics Jul 19, 2024
Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspect...
Humans Algorithms Deep Learning Genomics Genetic Variation
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CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions.

Nucleic acids research Jan 5, 2024
Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of th...
Machine Learning Software Genetic Variation Humans Nucleotides Genome, Human
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A Comprehensive System for Searching and Evaluating Genomic Variant Evidence Using AI and Knowledge Bases to Support Personalized Medicine.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 1, 2024
We introduce an innovative automated system for the search and assessment of genetic variant evidence, meticulously aligned with ACMG guidelines. Leveraging the synergistic power of artificial intelligence (AI), elastic search, and an extensive knowl...
Humans Genomics Knowledge Bases Genetic Variation Artificial Intelligence Precision Medicine
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Decoding the effects of synonymous variants.

Nucleic acids research Dec 16, 2021
Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs su...
Genome, Human Disease Proteins Genetic Variation RNA Splicing Humans Machine Learning
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WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.

Briefings in bioinformatics Nov 5, 2021
Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are...
Gene Expression Regulation RNA, Untranslated Models, Statistical Regulatory Sequences, Ribonucleic Acid Genetic Variation Computational Biology Databases, Genetic Humans Algorithms Deep Learning Software
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Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches.

Briefings in bioinformatics Jul 20, 2021
Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting the disease type and disease-gene associations, scholars face two essential challenges, namely the vas...
Humans Genetic Variation Genomics Models, Genetic Neural Networks, Computer Software
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Opportunities and challenges for the computational interpretation of rare variation in clinically important genes.

American journal of human genetics Apr 1, 2021
Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic vari...
Genetic Variation Genetics, Medical Metabolism, Inborn Errors Genome, Human Pharmacogenetics Genomics Precision Medicine Infant, Newborn Humans Machine Learning
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A machine-learning approach to map landscape connectivity in with genetic and environmental data.

Proceedings of the National Academy of Sciences of the United States of America Mar 2, 2021
Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interact...
Aedes Mosquito Vectors Models, Biological Genetic Variation Genetics, Population Environment Gene-Environment Interaction Humans Animals Workflow Machine Learning
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CHDGKB: a knowledgebase for systematic understanding of genetic variations associated with non-syndromic congenital heart disease.

Database : the journal of biological databases and curation Jan 1, 2020
Congenital heart disease (CHD) is one of the most common birth defects, with complex genetic and environmental etiologies. The reports of genetic variation associated with CHD have increased dramatically in recent years due to the revolutionary devel...
Knowledge Bases Heart Defects, Congenital Genetic Variation Humans Databases, Factual
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