AIMC Topic: Models, Genetic

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Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.

PloS one Jan 26, 2022
Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions...
Genomics Polymorphism, Single Nucleotide Genotype Models, Genetic Genetic Markers Coffea Genome, Plant Neural Networks, Computer Phenotype
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Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.

Journal of cellular and molecular medicine Jan 22, 2022
There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death,...
Female Hospitalization Male Middle Aged Neural Networks, Computer Humans COVID-19 Intensive Care Units Polymorphism, Single Nucleotide Models, Genetic Morbidity Greece Thrombomodulin Complement Factor H Complement Activation Complement System Proteins
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Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework.

PLoS computational biology Dec 29, 2021
Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by sp...
Population Dynamics Supervised Machine Learning Biodiversity Clustered Regularly Interspaced Short Palindromic Repeats Male Rats Models, Genetic Introduced Species Pest Control, Biological Gene Drive Technology Population Control Female Animals
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GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

PLoS computational biology Dec 10, 2021
microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related m...
Humans Neural Networks, Computer Algorithms Neoplasms MicroRNAs Area Under Curve Genetic Predisposition to Disease Computational Biology Models, Genetic
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Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers.

PLoS computational biology Oct 11, 2021
Read-depths (RDs) are frequently used in identifying structural variants (SVs) from sequencing data. For existing RD-based SV callers, it is difficult for them to determine breakpoints in single-nucleotide resolution due to the noisiness of RD data a...
Genome, Human Genomic Structural Variation Genomics Models, Genetic Whole Genome Sequencing Humans Deep Learning
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Spatial rank-based multifactor dimensionality reduction to detect gene-gene interactions for multivariate phenotypes.

BMC bioinformatics Oct 4, 2021
BACKGROUND: Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popul...
Computer Simulation Genome-Wide Association Study Polymorphism, Single Nucleotide Models, Genetic Epistasis, Genetic Multifactor Dimensionality Reduction Algorithms Phenotype
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VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics.

Nature communications Sep 28, 2021
Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological ins...
Models, Genetic Single-Cell Analysis Humans Datasets as Topic Animals RNA-Seq Deep Learning Gene Regulatory Networks Mice
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Nature communications Sep 24, 2021
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...
Genetic Variation Species Specificity Genome, Plant Machine Learning Gene Expression Regulation, Plant Phenotype Evolution, Molecular Transcriptome Zea mays Genomics Arabidopsis Nitrogen Genotype Models, Genetic
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Genetic dissection of complex traits using hierarchical biological knowledge.

PLoS computational biology Sep 17, 2021
Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical bio...
Saccharomyces cerevisiae Gene Ontology Hydroxyurea Chromosome Mapping Multifactorial Inheritance Benomyl Nucleotidyltransferases Mutation Glucose Genome-Wide Association Study Glycine Models, Genetic Knowledge Bases Polymorphism, Single Nucleotide Metabolic Networks and Pathways Computational Biology Copper Systems Biology Neural Networks, Computer Machine Learning Phenotype
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ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.

PLoS computational biology Sep 7, 2021
Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understan...
Humans Promoter Regions, Genetic Regulatory Sequences, Nucleic Acid Molecular Sequence Annotation Models, Genetic Deep Learning Enhancer Elements, Genetic Transcription Initiation, Genetic Mutation Genome-Wide Association Study Genes, Reporter Gene Expression Regulation Computational Biology Genome, Human
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