Author

Sophia N. Yaliraki

Imperial College LondonORCID 0000-0002-8968-0713Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Molecular Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:protein structure· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1 Research Focus:protein structure prediction· 1 Organism:virus· 1

Frequent Co-Authors

Mauricio BarahonaiD· 3 shared Léonie StrömichiD· 2 shared Nan WuiD· 2 shared Alexis ArnaudoniD· 1 shared Benjamin R. C. Amor· 1 shared Henry Antonio PalascianoiD· 1 shared Julia A. SchmidtiD· 1 shared Kim E. JelfsiD· 1 shared

Activity by Year

2016-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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3 shared papersBioinformatics

Mauricio BarahonaORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Allosteric hotspots in the main protease of SARS-CoV-2

Léonie Strömich, Nan Wu, Mauricio Barahona, Sophia N. Yaliraki

The paper identifies four statistically significant putative allosteric sites in the SARS-CoV-2 main protease using graph theoretical methods.

Journal of Molecular Biology · Jul 16, 2022Peer-reviewedBioinformatics

Prediction of allosteric sites and signalling: insights from benchmarking datasets

Nan Wu, Léonie Strömich, Sophia N. Yaliraki

The paper benchmarks a method for predicting allosteric sites using two datasets, achieving high accuracy with only orthosteric site information.

bioRxiv · Aug 16, 2021 · PreprintBioinformatics

hcga: Highly Comparative Graph Analysis for network phenotyping

Robert L. Peach, Alexis Arnaudon, Julia A. Schmidt, Henry Antonio Palasciano, Nathan Bernier, Kim E. Jelfs, Sophia N. Yaliraki, Mauricio Barahona

The paper introduces HCGA, a framework for analyzing graph data by computing thousands of graph features and using statistical learning tools to improve classification and discovery.

Patterns · Apr 1, 2021Peer-reviewedSystems Biology

Prediction of allosteric sites and mediating interactions through bond-to-bond propensities

Benjamin R. C. Amor, Michael T. Schaub, Sophia N. Yaliraki, Mauricio Barahona

The paper introduces a graph-theoretical method to predict allosteric sites and their mediating interactions by analyzing bond-to-bond propensities in proteins.

bioRxiv · May 31, 2016Molecular Biology