Article

Knowledge-Driven Interpretable Neural Networks for Mechanistic Insight

2025-09-08

Abstract excerpt

Analyzing omics data based on pathway knowledge is critical for understanding the molecular mechanism behind pathological changes, but current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds featur...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: fad8ac1c-1176-5bd3-9bfc-037e56270a55
DOI: 10.1101/2025.09.08.674894

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Knowledge-Driven Interpretable Neural Networks for Mechanistic InsightDOI 10.1101/2025.09.08.674894Read

PIMO: Pathway-based Interpretable Multi-Omics interactions for multi-omics integrationSemantic proximity Read

Predicting the Pathway Involvement of Compounds Annotated in the Reactome Knowledge-baseSemantic proximity Read

Interpreting biologically informed neural networks for enhanced biomarker discovery and pathway analysisSemantic proximity Read

PINT: Pathway-pathway interactions for predicting interpretable clinical outcomes from gene expressionSemantic proximity Read

Modeling causal signal propagation in multi-omic factor space with COSMOSSemantic proximity Read

Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s DiseaseSemantic proximity Read

Lemonite: identification of regulatory metabolites through data-driven, interpretable integration of transcriptomics and metabolomics dataSemantic proximity Read

COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.Semantic proximity Read

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.Semantic proximity Read

An explainable graph neural network approach for effectively integrating multi-omics with prior knowledge to identify biomarkers from interacting biological domainsSemantic proximity Read

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivitySemantic proximity Read

Expanding large-scale mechanistic models with machine learned associations and big datasetsSemantic proximity Read