ArticleBioinformatics advances2023
BERTwalk for integrating gene networks to predict gene- to pathway-level properties.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations.PloS one · 2026Article
- AI digital-twin ecosystem translating gut-microbiome-neuroimmune signals into precision sleep-mood interventions.Frontiers in psychiatry · 2026Review
- Unifying proteomic technologies with ProteinProjector.Bioinformatics advances · 2025Article
- ICoN: integration using co-attention across biological networks.Bioinformatics advances · 2025Article
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Multi-modal contrastive learning of subcellular organization using DICE.Bioinformatics (Oxford, England) · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Graph representation learning is a fundamental problem in the field of data science with applications to integrative analysis of biological networks. Previous work in this domain was mostly limited to shallow representation techniques. A recent deep representation technique, BIONIC, has achieved state-of-the-art results in a variety of tasks but used arbitrarily defined components. Results: Here, we present BERTwalk, an unsupervised learning scheme that combines the BERT masked language model with a network propagation regularization for graph representation learning. The transformation from networks to texts allows our method to naturally integrate different networks and provide features that inform not only nodes or edges but also pathway-level properties. We show that our BERTwalk model outperforms BIONIC, as well as four other recent methods, on two comprehensive benchmarks in yeast and human. We further show that our model can be utilized to infer functional pathways and their effects. Availability and implementation: Code and data are available at https://github.com/raminass/BERTwalk. Contact: roded@tauex.tau.ac.il.
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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 OpenQuestion graph.