Identification of HIV-1 Vif Protein Attributes Associated With CD4 T Cell Numbers and Viral Loads Using Artificial Intelligence Algorithms
Bibliographic record
Abstract
The Human Immunodeficiency Virus (HIV) Viral Infectivity Factor (Vif) is a 192-amino acid accessory protein essential to viral replication which counteracts host APOBEC3 proteins. APOBEC3 proteins interfere with the replication of HIV, hepatitis C virus, hepatitis B virus and retrotransposons. Vif is a recent candidate target for therapeutic and preventative interventions in HIV/AIDS yet little is known about its clinical relevance. We describe the results of applying different machine learning algorithms (Apriori, Multifactor Dimensionality Reductor, C4.5, Artificial Neural Networks and ID3) to the search of associations between HIV-1 Vif protein attributes and clinical endpoints. Final iterations showed that the presence of mutations in BC Boxes, APOBEC motifs and Cullin5 binding motifs were together associated with higher initial CD4 T cells while mutations of specific APOBEC motifs coupled with the conservation of other APOBEC motifs were associated with lower historic CD4 T cells. Conservation of specific APOBEC motifs and BC boxes were linked to lower initial viral loads while different combinations of mutations in the Nuclear Localisation Inhibition Signal and BC Boxes were associated with higher historic viral loads. Further scrutiny of these combinations through traditional statistical methods revealed striking differences in both CD4 T cells and viral loads in patients stratified into those having the previous combinations. While artificial intelligence algorithms do not phase out traditional statistical methods, our Artificial Intelligence (AI)-based approach highlights their use at reducing the dimensionality of large and complex datasets and at proposing novel, unimaginable, associations of biological patterns with functional relevance or clinical roles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".