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ZENODO
Software . 2022
License: https://opensource.org/licenses/MIT
Data sources: Datacite
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ZENODO
Software . 2022
License: http://opensource.org/licenses/MIT
Data sources: ZENODO
Claim

vlad-danaila/deep_hiv_ab_pred: zendo_release_fc-att-fix

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 27 Jul 2022 English Publisher:Zenodo
Authors: Vlad-Rareș Dănăilă;
Code repository: https://github.com/vlad-danaila/deep_hiv_ab_pred/tree/zenodo_release_fc-att-fix

doi: 10.5281/zenodo.6916481

vlad-danaila/deep_hiv_ab_pred: zendo_release_fc-att-fix

Abstract

The repository contains the software implementation associated with the article "Prediction of HIV Sensitivity to Monoclonal Antibodies Using Aminoacid Sequences and Deep Learning" by Vlad-Rareș Dănăilă and Cătălin Buiu, Bioinformatics, 2022. The models are based on neural networks and predict the sensitivity of an HIV strain against a monoclonal antibody. The input is formed from the aminoacid sequences of the virus and antibody.

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Keywords

deep learning, neural networks, machine learning, bioinformatics, sequence analysis, HIV, monoclonal antibodies

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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