Proceedings of the National Academy of Sciences of the United States of America
Jan 16, 2026
Accurately predicting the phenotypic consequences of genetic variation is a major challenge for precision medicine. The problem is exacerbated by epistatic interactions, nonadditive effects between genetic variants that produce unexpected phenotypes....
Proceedings of the National Academy of Sciences of the United States of America
Oct 16, 2025
Deep learning has advanced our ability to assess the effects that individual mutations have on protein function; however, predicting the complex interplay between two or more mutations remains challenging. Here, we seek to address this challenge by b...
The prevalence of synthetic associations in GWAS, where non-causal variants become significant by tagging multiple undetected causal variants and not necessarily in strong linkage disequilibrium with any single one, remains unexplored. We introduce a...
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...
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...
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...
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...
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...
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...
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...
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