ArticleNPJ systems biology and applications2023
Reliable interpretability of biology-inspired deep neural networks.
Article in NPJ systems biology and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.Cell systems · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- TF-GateNet: An Interpretable and Biologically Guided Framework for Primary-Metastatic State Prediction from Somatic Genomic Alterations.Biomolecules · 2026Article
- Is the molecular microenvironment of the latent HIV reservoir predictable using deep learning approaches?Journal of virology · 2026Review
- scMarkerGene: an interpretable neural network framework for cell-type-specific marker gene discovery.Briefings in bioinformatics · 2026Article
- Algorithm guided personalized T cell therapy: machine learning unlocks next generation TCR engineered immunotherapy.Pharmacological reports : PR · 2026Review
- AUTOENCODIX: a generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond.Nature computational science · 2026Article
- Research on the prediction of slow blood flow in pPCI of STEMI patients based on CatBoost.European journal of medical research · 2025Article
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.bioRxiv : the preprint server for biology · 2025Article
- From network biology to immunity: potential longitudinal biomarkers for targeting the network topology of the HIV reservoir.Journal of translational medicine · 2025Review
- Recent advances in plant stress analysis using chlorophyllPhotosynthetica · 2025Review
- A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale.Communications biology · 2024Article
- Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.NPJ systems biology and applications · 2024Article
- Molecular causality in the advent of foundation models.Molecular systems biology · 2024Review
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Inference of drug off-target effects on cellular signaling using interactome-based deep learning.iScience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where real-world concepts are encoded in hidden nodes, which can be ranked by importance and thereby interpreted. In such models trained on single-cell transcriptomes, we previously demonstrated that node-level interpretations lack robustness upon repeated training and are influenced by biases in biological knowledge. Similar studies are missing for related models. Here, we test and extend our methodology for reliable interpretability in P-NET, a biology-inspired model trained on patient mutation data. We observe variability of interpretations and susceptibility to knowledge biases, and identify the network properties that drive interpretation biases. We further present an approach to control the robustness and biases of interpretations, which leads to more specific interpretations. In summary, our study reveals the broad importance of methods to ensure robust and bias-aware interpretability in biology-inspired deep learning.
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.