English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Predicting the outcome of antiretroviral therapies for HIV-1 is a pressingclinical challenge, especially when the treatment regimen includes drugs forwhich limited effectiveness data is available. This scarcity of data can ariseeither due to the introduction of a new drug to the market or due to limiteduse in clinical settings. To tackle this issue, we introduce a novel jointfusion model, which combines features from a Fully Connected (FC) NeuralNetwork and a Graph Neural Network (GNN). The FC network employs tabular datawith a feature vector made up of viral mutations identified in the most recentgenotypic resistance test, along with the drugs used in therapy. Conversely,the GNN leverages knowledge derived from Stanford drug-resistance mutationtables, which serve as benchmark references for deducing in-vivo treatmentefficacy based on the viral genetic sequence, to build informative graphs. Weevaluated these models' robustness against Out-of-Distribution drugs in thetest set, with a specific focus on the GNN's role in handling such scenarios.Our comprehensive analysis demonstrates that the proposed model consistentlyoutperforms the FC model, especially when considering Out-of-Distributiondrugs. These results underscore the advantage of integrating Stanford scores inthe model, thereby enhancing its generalizability and robustness, but alsoextending its utility in real-world applications with limited dataavailability. This research highlights the potential of our approach to informantiretroviral therapy outcome prediction and contribute to more informedclinical decisions.

A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1