Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.

HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sepideh Mazrouee, Susan J Little, Joel O Wertheim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/19d0493ddfa44a4b91f02b81ec4c2533
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:19d0493ddfa44a4b91f02b81ec4c2533
record_format	dspace
spelling	oai:doaj.org-article:19d0493ddfa44a4b91f02b81ec4c25332021-12-02T19:57:45ZIncorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.1553-734X1553-735810.1371/journal.pcbi.1009336https://doaj.org/article/19d0493ddfa44a4b91f02b81ec4c25332021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009336https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.Sepideh MazroueeSusan J LittleJoel O WertheimPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009336 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sepideh Mazrouee Susan J Little Joel O Wertheim Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.
description	HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.
format	article
author	Sepideh Mazrouee Susan J Little Joel O Wertheim
author_facet	Sepideh Mazrouee Susan J Little Joel O Wertheim
author_sort	Sepideh Mazrouee
title	Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.
title_short	Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.
title_full	Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.
title_fullStr	Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.
title_full_unstemmed	Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.
title_sort	incorporating metadata in hiv transmission network reconstruction: a machine learning feasibility assessment.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/19d0493ddfa44a4b91f02b81ec4c2533
work_keys_str_mv	AT sepidehmazrouee incorporatingmetadatainhivtransmissionnetworkreconstructionamachinelearningfeasibilityassessment AT susanjlittle incorporatingmetadatainhivtransmissionnetworkreconstructionamachinelearningfeasibilityassessment AT joelowertheim incorporatingmetadatainhivtransmissionnetworkreconstructionamachinelearningfeasibilityassessment
_version_	1718375765561573376

Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment.

Ejemplares similares