ArticlePLoS computational biology2023
Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Interpreting artificial neural networks to detect genome-wide association signals for complex traits.NAR genomics and bioinformatics · 2026Article
- Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models.Computer methods and programs in biomedicine · 2025Article
- Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography.IEEE access : practical innovations, open solutions · 2025Article
- Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer's disease.Frontiers in genetics · 2025Article
- Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning.Communications engineering · 2024Article
- A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.Scientific reports · 2024Article
- Enhancing epidural needle guidance using a polarization-sensitive optical coherence tomography probe with convolutional neural networks.Journal of biophotonics · 2024Article
- Automatic renal carcinoma biopsy guidance using forward-viewing endoscopic optical coherence tomography and deep learning.Research square · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygenic risk scores (PRS). This hypothesis was tested using a multi-task learning (MTL) approach based on an explainable neural network architecture. We found that parallel estimations of the PRS for 17 prevalent cancers in a pan-cancer MTL model were generally more accurate than independent estimations for individual cancers in comparable single-task learning (STL) models. Such performance improvement conferred by positive transfer learning was also observed consistently for 60 prevalent non-cancer diseases in a pan-disease MTL model. Interpretation of the MTL models revealed significant genetic correlations between the important sets of single nucleotide polymorphisms used by the neural network for PRS estimation. This suggested a well-connected network of diseases with shared genetic basis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.