Positive and unlabeled learning for anomaly detection

Zhang, Jiaqi

doi:10.32657/10356/75883

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…We chose to use XGBoost for our architecture due to their robustness to outliers and noise, minimal need to adjust model hyperparameters ( 25 ), and enabling combination of multiple “weak learners” in our PU (positive and unlabelled) learning ensemble ( 26 , 27 ), to produce a robust final prediction.…”

Section: Resultsmentioning

confidence: 99%

DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations

Høie,

Gade,

Johansen

et al. 2024

Front. Immunol.

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Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.

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Section: Resultsmentioning

confidence: 99%