AIMC Topic: Models, Genetic
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Exploring the application of deep learning methods for polygenic risk score estimation.
Biomedical physics & engineering express
Mar 13, 2025
. Polygenic risk scores (PRS) summarise genetic information into a single number with clinical and research uses. Deep learning (DL) has revolutionised multiple fields, however, the impact of DL on PRSs has been less significant. We explore how DL ca...
Genomic selection in pig breeding: comparative analysis of machine learning algorithms.
Genetics, selection, evolution : GSE
Mar 10, 2025
BACKGROUND: The effectiveness of genomic prediction (GP) significantly influences breeding progress, and employing SNP markers to predict phenotypic values is a pivotal aspect of pig breeding. Machine learning (ML) methods are usually used to predict...
A compendium of human gene functions derived from evolutionary modelling.
Nature
Feb 26, 2025
A comprehensive, computable representation of the functional repertoire of all macromolecules encoded within the human genome is a foundational resource for biology and biomedical research. The Gene Ontology Consortium has been working towards this g...
Comparing statistical learning methods for complex trait prediction from gene expression.
PloS one
Feb 11, 2025
Accurate prediction of complex traits is an important task in quantitative genetics. Genotypes have been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regression methods, dimension reduction me...
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...
The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.
PLoS computational biology
Jan 10, 2025
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they...
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...
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...
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...
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...
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