2016
DOI: 10.1038/srep24883
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Genotypic Prediction of Co-receptor Tropism of HIV-1 Subtypes A and C

Abstract: Antiretroviral treatment of Human Immunodeficiency Virus type-1 (HIV-1) infections with CCR5-antagonists requires the co-receptor usage prediction of viral strains. Currently available tools are mostly designed based on subtype B strains and thus are in general not applicable to non-B subtypes. However, HIV-1 infections caused by subtype B only account for approximately 11% of infections worldwide. We evaluated the performance of several sequence-based algorithms for co-receptor usage prediction employed on su… Show more

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Cited by 30 publications
(24 citation statements)
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References 47 publications
(60 reference statements)
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“…We used cutoffs of 10 ng/mL and 20 ng/mL for AFP, 10 % for AFP-L3, 7.5 ng/mL for DCP, and −0.63 for the GALAD score. For direct comparison of the resulting models, we calculated the diagnostic odds ratio (DOR) as described by Riemenschneider et al [21]. The DOR is defined as follows:…”
Section: Discussionmentioning
confidence: 99%
“…We used cutoffs of 10 ng/mL and 20 ng/mL for AFP, 10 % for AFP-L3, 7.5 ng/mL for DCP, and −0.63 for the GALAD score. For direct comparison of the resulting models, we calculated the diagnostic odds ratio (DOR) as described by Riemenschneider et al [21]. The DOR is defined as follows:…”
Section: Discussionmentioning
confidence: 99%
“…Sub-classifications included early chronic infection (186 days to 1 year post infection) and late chronic infections (> 5 years post infection). Sequences were separately analysed for predicted co-receptor preferences Phenoseq-C and PSSMsinsi are particularly HIV-1-C based co-receptor prediction tools [36][37][38][39]. An inferred concordance of all three tools was used to assign the coreceptor biotype.…”
Section: Co-receptor Prediction and Sequence Analysesmentioning
confidence: 99%
“…Machine learning, statistical learning, and soft-computing approaches, such as deep neural networks or genetic algorithms, have also become terms used in the bio world, with an incomplete comprehension however, of their potential (Pavel et al, 2016;Lin and Lane, 2017;Zeng and Lumley, 2018). In recent years, omics, multi-omics, and inter-omics experiments have presented a further step toward the investigation in biology, opening the window on personalized medicine, for example for diagnostics (Riemenschneider et al, 2016). The era of big data in medicine is imminent and represents yet a further step forward.…”
Section: Editorial On the Research Topic Artificial Intelligence Bioimentioning
confidence: 99%