A comparison of accuracy and computational feasibility of two record linkage algorithms in retrieving vital status information from HIV/AIDS patients registered in Brazilian public databases.

BACKGROUND AND OBJECTIVE: While cross-referencing information from people living with HIV/AIDS (PLWHA) to the official mortality database is a critical step in monitoring the HIV/AIDS epidemic in Brazil, the accuracy of the linkage routine may compromise the validity of the final database, yielding to biased epidemiological estimates. We compared the accuracy and the total runtime of two linkage algorithms applied to retrieve vital status information from PLWHA in Brazilian public databases. METHODS: Nominally identified records from PLWHA were obtained from three distinct government databases. Linkage routines included an algorithm in Python language (PLA) and Reclink software (RlS), a probabilistic software largely utilized in Brazil. Records from PLWHA1 known to be alive were added to those from patients reported as deceased. Data were then searched into the mortality system. Scenarios where 5% and 50% of patients actually dead were simulated, considering both complete cases and 20% missing maternal names. RESULTS: When complete information was available both algorithms had comparable accuracies. In the scenario of 20% missing maternal names, PLA2 and RlS3 had sensitivities of 94.5% and 94.6% (p > 0.5), respectively; after manual reviewing, PLA sensitivity increased to 98.4% (96.6-100.0) exceeding that for RlS (p < 0.01). PLA had higher positive predictive value in 5% death proportion. Manual reviewing was intrinsically required by RlS in up to 14% register for people actually dead, whereas the corresponding proportion ranged from 1.5% to 2% for PLA. The lack of manual inspection did not alter PLA sensitivity when complete information was available. When incomplete data was available PLA sensitivity increased from 94.5% to 98.4%, thus exceeding that presented by RlS (94.6%, p < 0.05). RlS spanned considerably less processing time compared to PLA. CONCLUSION: Both linkage algorithms presented interchangeable accuracies in retrieving vital status data from PLWHA. RlS had a considerably lesser runtime but intrinsically required manually reviewing a fastidious proportion of the matched registries. On the other hand, PLA spent quite more runtime but spared manual reviewing at no expense of accuracy.

Reposicionamento de fármacos para malária: um método que identifica alvos no domínio das doenças negligenciadas / Drug repositioning to malaria disease: a method to a method to identifying targets in domain neglected diseases

O atual cenário da Biologia Computacional conta com o know-how de diversas bases tecnológicas voltadas para informação, computação e, especialmente, para construção e uso de bancos de dados na Internet como MEDLINE, PubMed, PDB. Na medida em que essas bases de dados possibilitam o aumento no registro de produção, estoque e circulação de dados genéticos, também viabilizam, em anos recentes, ambientes para acessar, integrar e produzir o novo conhecimento. O reuso desses dados, isto é, a transformação e o emprego desses dados em um processo diferente do qual os dados foram originalmente concebidos, torna-se um desafio em pesquisas na área biológica. Os problemas surgem pela falta de estrutura textual ou marcação para processamento por computadores. Neste trabalho, foi desenvolvido um método que identifica nomes de proteínas que servirão de substrato às práticas laboratoriais de reposicionamento de medicamentos. Dentre as fontes citadas, foram empregados, inicialmente, documentos textuais digitais do PubMed, a principal fonte de informação das ciências da saúde da Biblioteca Nacional de Medicina (National Library of Medicine). Esta base foi explorada com métodos e recursos da mineração de textos que são amplamente empregados para extrair termos relacionados aos nomes de proteínas no domínio das doenças negligenciadas. Os resultados obtidos após o processamento de 8444 artigos relacionados ao termo malaria do PubMed revelaram 254 fármacos candidatos ao reposicionamento. Dentre estes, três fármacos (Amitriptyline, Trimethoprim e Methrotrexate) foram comprovados, por meio de uma busca não exaustiva na literatura biomédica, que são utilizados em experimentos para o tratamento de malária. Por outro lado foi possível sugerir um conjunto de proteínas ou moléculas que poderão servir de insumos na fase inicial da cadeia de produção de medicamentos que é o screening de moléculas...

The current scenario of computational biology relies on the know-how of manytechnological areas, with focus on information, computing, and, particularly on theconstruction and use of existing Internet databases such as MEDLINE, PubMed andPDB. In recent years, these databases provide an environment to access, integrateand produce new knowledge by storing ever increasing volumes of genetic or proteindata The transformation and management of these data in a different way from theone that were originally thought can be a challenge for research in biology. Theproblems appear by the lack of textual structure or appropriate markup tags. In thisstudy, It aimed to develop a method that identifies proteins names that will serve asa substrate to laboratory practices for drug repositioning. Among the sources cited, Itwas initially explored digital text documents from PubMed, the main source ofinformation about Health Sciences of the National Library Medicine. This base wasexplored with text mining methods and resources that are widely used to extractterms related to proteins names in domain of neglected diseases. The resultsobtained after the processing of 8444 articles related to term malaria in PubMedrevealed 254 drugs candidates for repositioning. Among these, three drugs(Amitriptyline, Trimethoprim and Methrotrexate) were confirmed by a non-exhaustivesearch in the biomedical literature, that are used in experiments to treat malaria. Onthe other hand we can suggest a set of proteins or molecules that can serve asinputs in screening of molecules, the early stage of the drug production chain...