ArticleScientific reports2016
Observation selection bias in contact prediction and its implications for structural bioinformatics.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- In silico identification of archaeal DNA-binding proteins.Bioinformatics (Oxford, England) · 2025Article
- Creating a bias-free dataset with food delivery app reviews under data poisoning attack.Data in brief · 2024Article
- Petabase-Scale Homology Search for Structure Prediction.Cold Spring Harbor perspectives in biology · 2024Review
- Integrating physics in deep learning algorithms: a force field as a PyTorch module.Bioinformatics (Oxford, England) · 2024Article
- Cross-linking mass spectrometry discovers, evaluates, and corroborates structures and protein-protein interactions in the human cell.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Insight into the protein solubility driving forces with neural attention.PLoS computational biology · 2020Article
- Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.Scientific reports · 2019Article
- Numerical Encodings of Amino Acids in Multivariate Gaussian Modeling of Protein Multiple Sequence Alignments.Molecules (Basel, Switzerland) · 2018Article
- Enhancing coevolution-based contact prediction by imposing structural self-consistency of the contacts.Scientific reports · 2018Article
- Applications of sequence coevolution in membrane protein biochemistry.Biochimica et biophysica acta. Biomembranes · 2018Review
- EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction.BMC bioinformatics · 2017Article
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3 authors.
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Abstract
Next Generation Sequencing is dramatically increasing the number of known protein sequences, with related experimentally determined protein structures lagging behind. Structural bioinformatics is attempting to close this gap by developing approaches that predict structure-level characteristics for uncharacterized protein sequences, with most of the developed methods relying heavily on evolutionary information collected from homologous sequences. Here we show that there is a substantial observational selection bias in this approach: the predictions are validated on proteins with known structures from the PDB, but exactly for those proteins significantly more homologs are available compared to less studied sequences randomly extracted from Uniprot. Structural bioinformatics methods that were developed this way are thus likely to have over-estimated performances; we demonstrate this for two contact prediction methods, where performances drop up to 60% when taking into account a more realistic amount of evolutionary information. We provide a bias-free dataset for the validation for contact prediction methods called NOUMENON.
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