ClinIAN: Clinically Informed Attention Network

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ID: 328317
2026
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Ranked #162 of 172 articles by views in Bioinformatics advances

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Abstract
Abstract Motivation Artificial Neural Networks (ANNs) hold promise in predicting disease severity from viral protein sequences. To gain clinical insights, the models must adhere to two key factors: first, they need to be interpretable, as using black-box models within a healthcare setting poses risks, and second, they should integrate viral and clinical features to correct for biases within the data. We propose ClinIAN (Clinically Informed Attention Network), an inherently interpretable end-to-end learning model that effectively combines clinical and sequence parameters. We evaluate ClinIAN within the context of the challenging task of predicting the severity of SARS-CoV-2 infection, but it could be applied to other medical and biological contexts, with sequence and tabular features, such as bacterial infections or cancer research. Results We evaluate the performance capabilities of ClinIAN in predicting SARS-CoV-2 severity using a subset of the EuCARE hospitalized cohort. We show ClinIAN’s ability to capture biologically relevant features across multiple layers of resolution while retaining stable state-of-the-art performance. Availability and implementation The code is available on Zenodo and on GitHub. Supplementary Information Supplementary materials are published at Bioinformatics Advances online.
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Authors Florian König, Jonas C. Ditz, Elham Shamsara, Pontus Hedberg, Iuri Fanti, Pontus Nauclér, Luca Carioti, Andreas Walker, Miłosz Parczewski, Francis Drobniewski, Francesca Ceccherini‐Silberstein, Maurizio Zazzi, Björn-Erik Ole Jensen, Anders Sönnerborg, Nico Pfeifer
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag254
URL
https://doi.org/10.1093/bioadv/vbag254
Keywords Keywords not found

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