ReviewBiomolecules2020
Machine Learning Approaches for Quality Assessment of Protein Structures.
Review in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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
9 citing papers in PubMed, 31 citations in OpenAlex.
- Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses.Journal of chemical information and modeling · 2025Article
- Structural studies of Parvoviridae capsid assembly and evolution: implications for novel AAV vector design.Frontiers in artificial intelligence · 2025Review
- A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification.Computational and mathematical methods in medicine · 2022Article
- An Overview of Alphafold's Breakthrough.Frontiers in artificial intelligence · 2022Review
- Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network.Journal of cheminformatics · 2021Article
- Deep Learning-Based Advances in Protein Structure Prediction.International journal of molecular sciences · 2021Review
- QUARTERplus: Accurate disorder predictions integrated with interpretable residue-level quality assessment scores.Computational and structural biotechnology journal · 2021Article
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 at 1 institution in 1 country.
Funding
Abstract
Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach-support vector machine, artificial neural networks, ensemble learning, or Bayesian learning-and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.
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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.