Evidence map›Paper›PMID 39066293›Full record

ArticleViruses2024

Quantitative Prediction of Human Immunodeficiency Virus Drug Resistance.

Ekaterina A Stolbova, Leonid A Stolbov, Dmitry A Filimonov, Vladimir V Poroikov, Olga A Tarasova

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Viruses, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ekaterina A StolbovaDepartment of Bioinformatics, Institute of Biomedical Chemistry, 10-7, Pogodinskaya Street, Moscow 119121, Russia.ORCID 0009-0001-0569-4967
Leonid A StolbovDepartment of Bioinformatics, Institute of Biomedical Chemistry, 10-7, Pogodinskaya Street, Moscow 119121, Russia.
Dmitry A FilimonovDepartment of Bioinformatics, Institute of Biomedical Chemistry, 10-7, Pogodinskaya Street, Moscow 119121, Russia.
Vladimir V PoroikovDepartment of Bioinformatics, Institute of Biomedical Chemistry, 10-7, Pogodinskaya Street, Moscow 119121, Russia.ORCID 0000-0001-7937-2621
Olga A TarasovaDepartment of Bioinformatics, Institute of Biomedical Chemistry, 10-7, Pogodinskaya Street, Moscow 119121, Russia.ORCID 0000-0002-3723-7832

Funding

Program for Basic Research in the Russian Federation for a long-term period (2021-2030) № 124050800018-9
6 · The paper itself

Abstract

Drug resistance of pathogens, including viruses, is one of the reasons for decreased efficacy of therapy. Considering the impact of HIV type 1 (HIV-1) on the development of progressive immune dysfunction and the rapid development of drug resistance, the analysis of HIV-1 resistance is of high significance. Currently, a substantial amount of data has been accumulated on HIV-1 drug resistance that can be used to build both qualitative and quantitative models of HIV-1 drug resistance. Quantitative models of drug resistance can enrich the information about the efficacy of a particular drug in the scheme of antiretroviral therapy. In our study, we investigated the possibility of developing models for quantitative prediction of HIV-1 resistance to eight protease inhibitors based on the analysis of amino acid sequences of HIV-1 protease for 900 virus variants. We developed random forest regression (RFR), support vector regression (SVR), and self-consistent regression (SCR) models using binary vectors containing values from 0 or 1, depending on the presence of a specific peptide fragment in each amino acid sequence as independent variables, while fold ratio, reflecting the level of resistance, was the predicted variable. The SVR and SCR models showed the highest predictive performances. The models built demonstrate reasonable performances for eight out of nine (R

Indexed as

Drug Resistance, ViralHIV-1HIV InfectionsHIV ProteaseHIV Protease InhibitorsAmino Acid SequenceAnti-HIV AgentsHumansAnti-HIV AgentsHIV ProteaseHIV Protease Inhibitorsp16 protease, Human immunodeficiency virus 1drug resistanceHIVmachine learningself-consistent regressionviral infections

Identifiers

PMID39066293
PMCPMC11281478

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.