ArticleGenome biology2023
Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 11 citations in OpenAlex.
- End-to-end deep learning methods for genetic risk prediction of schizophrenia.Nature communications · 2026Article
- Article
- Using machine learning to predict and analyze complex trait diseases: Lessons from a simple abstract model.PloS one · 2026Article
- Explainable deep learning for stratified medicine in inflammatory bowel disease.Genome biology · 2025Article
- Detecting genetic interactions with visible neural networks.Communications biology · 2025Article
- Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits.Genome biology · 2025Article
- A quantitative benchmark of neural network feature selection methods for detecting nonlinear signals.Scientific reports · 2024Article
- A python library for the fast and scalable computation of biologically meaningful individual specific networks.Scientific reports · 2024Article
- Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana.Scientific reports · 2024Article
- Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.Frontiers in plant science · 2024Article
- Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease.Genome biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDespite clear evidence of nonlinear interactions in the molecular architecture of polygenic diseases, linear models have so far appeared optimal in genotype-to-phenotype modeling. A key bottleneck for such modeling is that genetic data intrinsically suffers from underdetermination ([Formula: see text]). Millions of variants are present in each individual while the collection of large, homogeneous cohorts is hindered by phenotype incidence, sequencing cost, and batch effects.
resultsWe demonstrate that when we provide enough training data and control the complexity of nonlinear models, a neural network outperforms additive approaches in whole exome sequencing-based inflammatory bowel disease case-control prediction. To do so, we propose a biologically meaningful sparsified neural network architecture, providing empirical evidence for positive and negative epistatic effects present in the inflammatory bowel disease pathogenesis.
conclusionsIn this paper, we show that underdetermination is likely a major driver for the apparent optimality of additive modeling in clinical genetics today.
Indexed as
Identifiers
What Socratic holds
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.