Publications by authors named "Vlad-Rares Danaila"
Article Synopsis
- Machine learning algorithms are crucial in bioinformatics, enabling the exploration of complex biological data, especially in HIV neutralizing antibody research.
- This systematic review highlights various machine learning applications such as predicting neutralization potency, detecting antibody-virus binding sites, and designing enhanced antibodies, based on literature from the past decade.
- The review covers different methodologies—including supervised and unsupervised learning—and concludes with suggestions for future research challenges in the field.
Motivation: Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence.
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