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Human illness from avian influenza H7N3, British Columbia

Emerging infectious diseases, 2004-12, Vol.10 (12), p.2196-2199

2004

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The effect of Ca 2+ on the pharmacological specificity of [ 3H]glutamate binding sites in rat hippocampus

Neuroscience letters, 1982, Vol.32 (1), p.59-63

1982

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The effect of Ca2+ on the pharmacological specificity of [3H]glutamate binding sites in rat hippocampus

Neuroscience letters, 1982-09, Vol.32 (1), p.59-63

1982

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2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings

Journal of antimicrobial chemotherapy, 2021-06, Vol.76 (7), p.1898-1906

2021

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Depletion of stromal cells expressing fibroblast activation protein-α from skeletal muscle and bone marrow results in cachexia and anemia

The Journal of experimental medicine, 2013-06, Vol.210 (6), p.1137-1151

2013

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Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response

Journal of acquired immune deficiency syndromes (1999), 2019-06, Vol.81 (2), p.207

2019

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2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings

Journal of antimicrobial chemotherapy, 2018-08, Vol.73 (8), p.2186-2196

2018

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The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool

AIDS (London), 2011-09, Vol.25 (15), p.1855-1863

2011

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An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype

Journal of antimicrobial chemotherapy, 2016-10, Vol.71 (10), p.2928-2937

2016

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An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes

Journal of antimicrobial chemotherapy, 2014-04, Vol.69 (4), p.1104-1110

2014

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High Degree of Interlaboratory Reproducibility of Human Immunodeficiency Virus Type 1 Protease and Reverse Transcriptase Sequencing of Plasma Samples from Heavily Treated Patients

Journal of Clinical Microbiology, 2001-04, Vol.39 (4), p.1522-1529

2001

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Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings

Journal of antimicrobial chemotherapy, 2013-06, Vol.68 (6), p.1406-1414

2013

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Virological and immunological effects of treatment interruptions in HIV-1 infected patients with treatment failure

AIDS (London), 2000-12, Vol.14 (18), p.2857-2867

2000

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A comparison of computational models with and without genotyping for prediction of response to second‐line HIV therapy

HIV medicine, 2014-08, Vol.15 (7), p.442-448

2014

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Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

BioMed research international, 2013-01, Vol.2013, p.579741-6

2013

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Clinical evaluation of the potential utility of computational modeling as an HIV treatment selection tool by physicians with considerable HIV experience

AIDS patient care and STDs, 2011-01, Vol.25 (1), p.29

2011

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A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy

Artificial intelligence in medicine, 2009-09, Vol.47 (1), p.63-74

2009

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Dual Resistance to Zidovudine and Lamivudine in Patients Treated with Zidovudine-Lamivudine Combination Therapy: Association with Therapy Failure

The Journal of infectious diseases, 1998-06, Vol.177 (6), p.1521-1532

1998

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Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

Southern African journal of HIV medicine, 2016, Vol.17 (1), p.450-7

2016

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3′-Azido-3′-Deoxythymidine Drug Resistance Mutations in HIV-1 Reverse Transcriptase Can Induce Long Range Conformational Changes

Proceedings of the National Academy of Sciences - PNAS, 1998-08, Vol.95 (16), p.9518-9523

1998

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The use of computational models to predict response to HIV therapy for clinical cases in Romania

Germs (Bucureşti), 2012-03, Vol.2 (1), p.6-11

2012

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O131: The HIV treatment response prediction system: using the experience of treating tens of thousands of patients to guide optimal drug selection

Journal of the International AIDS Society, 2014-05, Vol.17 (2(Suppl 1))

2014

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Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa : original research

Southern African journal of HIV medicine, 2016-01, Vol.17 (1), p.1-7

2016

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HIV drug susceptibility and treatment response to mega-HAART regimen in patients from the Frankfurt HIV cohort

Antiviral therapy, 2000-03, Vol.5 (1), p.49-55

2000

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Human illness from avian influenza H7N3, British Columbia

The effect of Ca 2+ on the pharmacological specificity of [ 3H]glutamate binding sites in rat hippocampus

The effect of Ca2+ on the pharmacological specificity of [3H]glutamate binding sites in rat hippocampus

Trim28 Haploinsufficiency Triggers Bi-stable Epigenetic Obesity

2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings

Depletion of stromal cells expressing fibroblast activation protein-α from skeletal muscle and bone marrow results in cachexia and anemia

Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response

2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings

The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool

An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype

An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes

High Degree of Interlaboratory Reproducibility of Human Immunodeficiency Virus Type 1 Protease and Reverse Transcriptase Sequencing of Plasma Samples from Heavily Treated Patients

Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings

Virological and immunological effects of treatment interruptions in HIV-1 infected patients with treatment failure

A comparison of computational models with and without genotyping for prediction of response to second‐line HIV therapy

Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

Clinical evaluation of the potential utility of computational modeling as an HIV treatment selection tool by physicians with considerable HIV experience

A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy

Dual Resistance to Zidovudine and Lamivudine in Patients Treated with Zidovudine-Lamivudine Combination Therapy: Association with Therapy Failure

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

3′-Azido-3′-Deoxythymidine Drug Resistance Mutations in HIV-1 Reverse Transcriptase Can Induce Long Range Conformational Changes

The use of computational models to predict response to HIV therapy for clinical cases in Romania

O131: The HIV treatment response prediction system: using the experience of treating tens of thousands of patients to guide optimal drug selection

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa : original research

HIV drug susceptibility and treatment response to mega-HAART regimen in patients from the Frankfurt HIV cohort

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