AIMC Topic: Genetic Diseases, Inborn

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Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.

Scientific reports Jun 8, 2015
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are s...
Biological Ontologies Infections Genetic Diseases, Inborn Databases, Factual Humans Mice Animals Data Mining
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Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data.

Nucleic acids research Oct 27, 2014
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens ...
Rare Diseases Genetic Diseases, Inborn Internet Biological Ontologies Humans Disease Databases, Factual
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The Human Disease Ontology 2022 update.

Nucleic acids research Jan 7, 2022
The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO's 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on availa...
Databases, Genetic Biological Ontologies Genetic Diseases, Inborn Databases, Factual Genomics Humans
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Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Nucleic acids research Jan 11, 2021
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise ...
Humans Deep Learning Genetic Diseases, Inborn Chromatin Immunoprecipitation Histone Code Binding Sites Linkage Disequilibrium Causality Epigenomics Epigenome Datasets as Topic Models, Genetic Organ Specificity Genome-Wide Association Study Transcription Factors Molecular Sequence Annotation Polymorphism, Single Nucleotide
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Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics.

Current protocols in human genetics Sep 1, 2019
The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing human disease phenotypes for computational analysis to support differential diagnost...
Databases, Genetic Diagnosis, Differential Whole Genome Sequencing Phenotype Computational Biology Exome Biological Ontologies Genetic Diseases, Inborn Humans Software
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GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease.

Mathematical biosciences and engineering : MBE Feb 20, 2019
For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agonist-LOF" has laid a solid foundation for supporting drug repurposing. In this research, an active ge...
Molecular Sequence Annotation Genetic Diseases, Inborn Algorithms Recombinant Proteins Software Interleukin-2 Receptor alpha Subunit Genetic Variation Interleukin-2 Cost-Benefit Analysis Lymphoma Myelodysplastic Syndromes Models, Genetic Humans Drug Repositioning Machine Learning Mutation
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DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders.

American journal of human genetics Oct 5, 2017
In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background nois...
Software Genomics Mutation High-Throughput Nucleotide Sequencing Databases, Genetic Humans Machine Learning Genes, Dominant Genome, Human Genetic Diseases, Inborn
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Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

Bioinformatics (Oxford, England) Mar 1, 2017
MOTIVATION: Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accurac...
Genetic Diseases, Inborn Caspases Protein Conformation Algorithms Neural Networks, Computer Computational Biology Machine Learning Proteins
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