Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance.

Current HIV research - 1 Jan 2016

Zazzi Maurizio, Cozzi-Lepri Alessandro, Prosperi Mattia C F

Abstract excerpt

BACKGROUND: Resistance to antiretroviral drugs is a complex and evolving area with relevant implications in the treatment of human immunodeficiency virus (HIV) infection. Several rules, algorithms and full-fledged computer programs have been developed to assist the HIV specialist in the choice of the best patient-tailored therapy. METHODS: Experts' rules and statistical/machine learning algorithms for...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Anti-HIV Agents
Share to Anti-Retroviral Agents

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Drug Resistance, Viral

Share to Genotype

Share to HIV Infections

Source

Identifiers

PMID: 26511342
DOI: 10.2174/1570162x13666151029102254

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance.PMID 26511342Read ↗

Computational models for prediction of response to antiretroviral therapies.Semantic proximity Read ↗

The importance of bio-computational tools for predicting HIV drug resistance.Semantic proximity Read ↗

Bioinformatics approach to predicting HIV drug resistance.Semantic proximity Read ↗

Predicting drug-resistant mutations of HIV protease.Semantic proximity Read ↗

Interpreting resistance data for HIV-1 therapy management--know the limitations.Semantic proximity Read ↗

Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug ResistanceSemantic proximity Read ↗

HIV-1 genotypic drug resistance interpretation rules - 2009 Spanish guidelines.Semantic proximity Read ↗

HIV-GRADE: A Publicly Available, Rules-Based Drug Resistance Interpretation Algorithm Integrating Bioinformatic KnowledgeSemantic proximity Read ↗

Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance.Semantic proximity Read ↗

DR_SEQAN: a PC/Windows-based software to evaluate drug resistance using human immunodeficiency virus type 1 genotypes.Semantic proximity Read ↗

Algorithm evolution for drug resistance prediction: comparison of systems for HIV-1 genotyping.Semantic proximity Read ↗

Interpretation systems for genotypic drug resistance of HIV-1.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.