AIMC Topic: Models, Genetic

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Genomic language models: opportunities and challenges.

Trends in genetics : TIG Jan 2, 2025
Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of natural language processing is to understand sequences of words, a major objective i...
Humans Models, Genetic Natural Language Processing Genome Language Animals Genomics
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KGRACDA: A Model Based on Knowledge Graph from Recursion and Attention Aggregation for CircRNA-Disease Association Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
CircRNA is closely related to human disease, so it is important to predict circRNA-disease association (CDA). However, the traditional biological detection methods have high difficulty and low accuracy, and computational methods represented by deep l...
Humans Genetic Predisposition to Disease RNA, Circular Computational Biology Models, Genetic Databases, Genetic Knowledge Bases Datasets as Topic
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MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.

Genome biology Dec 2, 2024
We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysical models, including from multimodal phenotypic data...
Humans Epistasis, Genetic Machine Learning Mutation Allosteric Regulation Neural Networks, Computer Software Thermodynamics Models, Genetic
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Toward trustable use of machine learning models of variant effects in the clinic.

American journal of human genetics Nov 18, 2024
There has been considerable progress in building models to predict the effect of missense substitutions in protein-coding genes, fueled in large part by progress in applying deep learning methods to sequence data. These models have the potential to e...
Machine Learning Computer Simulation Deep Learning Models, Genetic Genetic Variation Humans Mutation, Missense
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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...
Epigenomics Models, Genetic DNA Epigenome Quantitative Trait Loci Chromatin Genome-Wide Association Study Polymorphism, Single Nucleotide Epigenesis, Genetic 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...
Software Algorithms Single-Cell Analysis Humans Neural Networks, Computer Models, Statistical Gene Regulatory Networks Computational Biology Models, Genetic
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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...
Quantitative Trait Loci Genotype Models, Genetic Gene Expression Phenotype Genome-Wide Association Study Transcriptome Humans Polymorphism, Single Nucleotide Deep Learning
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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...
Humans Neural Networks, Computer Animals Deep Learning Gene Regulatory Networks Gene Expression Regulation Regulatory Sequences, Nucleic Acid Genomics Models, Genetic
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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 ...
Phylogeny Models, Genetic Evolution, Molecular Neural Networks, Computer Machine Learning Likelihood Functions Sequence Alignment
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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 Neural Networks, Computer Quantitative Trait Loci Genome, Plant Genomics Machine Learning Quantitative Trait, Heritable Models, Genetic Arabidopsis
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