ReviewEmerging topics in life sciences2021
Graph representation learning for structural proteomics.
Review in Emerging topics in life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 8 citations in OpenAlex.
- StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.Protein science : a publication of the Protein Society · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures.Antibiotics (Basel, Switzerland) · 2026Article
- Nanodesigner: resolving the complex-CDR interdependency with iterative refinement.Journal of cheminformatics · 2025Article
- STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures.Computational and structural biotechnology journal · 2025Article
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- Review
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins.International journal of molecular sciences · 2023Article
- MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases.Cell reports methods · 2023Article
- Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The field of structural proteomics, which is focused on studying the structure-function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storing increasing amounts of protein structural data, in addition to modeled structures becoming increasingly available. This, combined with the recent advances in graph-based machine-learning models, enables the use of protein structural data in predictive models, with the goal of creating tools that will advance our understanding of protein function. Similar to using graph learning tools to molecular graphs, which currently undergo rapid development, there is also an increasing trend in using graph learning approaches on protein structures. In this short review paper, we survey studies that use graph learning techniques on proteins, and examine their successes and shortcomings, while also discussing future directions.
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.