AIMC Topic: Epistasis, Genetic

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Data-driven protease engineering by DNA-recording and epistasis-aware machine learning.

Nature communications Jul 1, 2025
Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. Predicting catalytic activity, however, remains challenging, restricting our capabilit...
Machine Learning Protein Engineering Peptide Hydrolases Epistasis, Genetic Escherichia coli DNA Substrate Specificity
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Detecting genetic interactions with visible neural networks.

Communications biology Jun 5, 2025
Non-linear interactions among single nucleotide polymorphisms (SNPs), genes, and pathways play an important role in human diseases, but identifying these interactions is a challenging task. Neural networks are state-of-the-art predictors in many doma...
Neural Networks, Computer Inflammatory Bowel Diseases Case-Control Studies Humans Genome-Wide Association Study Polymorphism, Single Nucleotide Epistasis, Genetic
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Epistasis regulates genetic control of cardiac hypertrophy.

Nature cardiovascular research Jun 5, 2025
Although genetic variant effects often interact nonadditively, strategies to uncover epistasis remain in their infancy. Here we develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy, u...
Humans Phenotype Female Gene Regulatory Networks Induced Pluripotent Stem Cells Genetic Predisposition to Disease Connectin Magnetic Resonance Imaging Myocytes, Cardiac Receptor, IGF Type 1 Hypertrophy, Left Ventricular Heart Failure Epistasis, Genetic Ventricular Remodeling Aged Middle Aged Male Deep Learning Genome-Wide Association Study
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Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.

BMC genomics May 1, 2025
BACKGROUND: Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle gen...
Epistasis, Genetic Deep Learning Genomics Genetic Predisposition to Disease Crohn Disease Schizophrenia Alzheimer Disease Humans
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Modeling gene interactions in polygenic prediction via geometric deep learning.

Genome research Jan 22, 2025
Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall...
Epistasis, Genetic Multifactorial Inheritance Humans Genetic Predisposition to Disease Neural Networks, Computer Models, Genetic Deep Learning Genome-Wide Association Study
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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 Neural Networks, Computer Allosteric Regulation Machine Learning Software Models, Genetic Mutation Thermodynamics
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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...
Polymorphism, Single Nucleotide Neural Networks, Computer Computational Biology Epistasis, Genetic Humans Models, Genetic Algorithms Deep Learning
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Gene-gene interaction detection with deep learning.

Communications biology Nov 12, 2022
The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasin...
Epistasis, Genetic Phenotype Deep Learning Polymorphism, Single Nucleotide Neural Networks, Computer
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Nested epistasis enhancer networks for robust genome regulation.

Science (New York, N.Y.) Aug 11, 2022
Mammalian genomes have multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, but it is unclear how these enhancers coordinate to achieve this task. We combine multiplexed CRISPRi screening with machine learning t...
Disease Humans K562 Cells Nuclear Proteins Epistasis, Genetic Cell Cycle Proteins Machine Learning Enhancer Elements, Genetic Transcription Factors Genome-Wide Association Study
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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...
Models, Genetic Computer Simulation Algorithms Epistasis, Genetic Genome-Wide Association Study Phenotype Multifactor Dimensionality Reduction Polymorphism, Single Nucleotide
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