AIMC Topic: Models, Genetic

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Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Nature communications Nov 16, 2024
Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existi...
Epigenome Quantitative Trait Loci Chromatin Genome-Wide Association Study Polymorphism, Single Nucleotide Epigenesis, Genetic Epigenomics Models, Genetic DNA Humans Deep Learning
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From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.

Journal of computational biology : a journal of computational molecular cell biology Oct 10, 2024
Understanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Original methods for GRN inference from bulk expression data often struggled with the high dimensionality and inhere...
Humans Neural Networks, Computer Software Computational Biology Models, Genetic Models, Statistical Algorithms Single-Cell Analysis Gene Regulatory Networks
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Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.

Genetic epidemiology Sep 30, 2024
Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the pred...
Models, Genetic Gene Expression Quantitative Trait Loci Polymorphism, Single Nucleotide Genotype Humans Phenotype Transcriptome Deep Learning Genome-Wide Association Study
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Interpreting cis-regulatory interactions from large-scale deep neural networks.

Nature genetics Sep 16, 2024
The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with orthogonal experimental data, providin...
Deep Learning Gene Regulatory Networks Genomics Humans Neural Networks, Computer Animals Models, Genetic Regulatory Sequences, Nucleic Acid Gene Expression Regulation
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Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments.

Molecular phylogenetics and evolution Aug 30, 2024
Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the phylogenetic tree reconstruction based on the Maximum Likelihood method. In this study, we present ...
Sequence Alignment Machine Learning Phylogeny Models, Genetic Evolution, Molecular Neural Networks, Computer Likelihood Functions
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Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.

PloS one Aug 28, 2024
We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidops...
Linear Models Phenotype Quantitative Trait, Heritable Genome, Plant Genomics Neural Networks, Computer Arabidopsis Quantitative Trait Loci Models, Genetic Machine Learning
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A Secure High-Order Gene Interaction Detection Algorithm Based on Deep Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics Aug 9, 2024
Identifying high-order Single Nucleotide Polymorphism (SNP) interactions of additive genetic model is crucial for detecting complex disease gene-type and predicting pathogenic genes of various disorders. We present a novel framework for high-order ge...
Deep Learning Polymorphism, Single Nucleotide Humans Computational Biology Models, Genetic Epistasis, Genetic Neural Networks, Computer Algorithms
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Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models.

Genomics Aug 5, 2024
This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neu...
Phenotype Deep Learning Genotype Models, Genetic Schizophrenia Humans Neural Networks, Computer
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Reliable estimation of tree branch lengths using deep neural networks.

PLoS computational biology Aug 5, 2024
A phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the t...
Neural Networks, Computer Models, Genetic Evolution, Molecular Likelihood Functions Sequence Alignment Phylogeny Machine Learning Deep Learning Algorithms Computational Biology
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Current genomic deep learning models display decreased performance in cell type-specific accessible regions.

Genome biology Aug 1, 2024
BACKGROUND: A number of deep learning models have been developed to predict epigenetic features such as chromatin accessibility from DNA sequence. Model evaluations commonly report performance genome-wide; however, cis regulatory elements (CREs), whi...
Chromatin Humans Models, Genetic Deep Learning Organ Specificity Genomics Regulatory Sequences, Nucleic Acid Epigenesis, Genetic
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