ArticleNPJ systems biology and applications2024
Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.
Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning.Nucleic acids research · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Multidimensional Modeling to Maximize Adaptations to eXercise: the MJournal of applied physiology (Bethesda, Md. : 1985) · 2025Article
- Multi-omics integration at cell type resolution uncovers gene-metabolite mechanisms underlying osteoarthritis heterogeneity.bioRxiv : the preprint server for biology · 2025Article
- Multi-task genomic prediction using gated residual variable selection neural networks.BMC bioinformatics · 2025Article
- Beyond the black box with biologically informed neural networks.Nature reviews. Genetics · 2025Article
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
- Integrated Proteomic and Metabolomic Profiling in Acute Promyelocytic Leukemia: Current Status and Perspectives.International journal of general medicine · 2025Review
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Reliable interpretability of biology-inspired deep neural networks.NPJ systems biology and applications · 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
7 authors.
Funding
Abstract
Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks informed by prior biological knowledge, referred to as visible networks. These neural networks offer insights into the decision-making process and can unveil novel perspectives on the underlying biological mechanisms associated with traits and complex diseases. We tested the performance, interpretability and generalizability for inferring smoking status, subject age and LDL levels using genome-wide RNA expression and CpG methylation data from the blood of the BIOS consortium (four population cohorts, N
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.