Integration of Genetic and Medical Information Through a Web Crawler System

Dias, Gaspar; Oliveira, José Luís; Vicente, F. J.; Martín-Sánchez, Fernando

doi:10.1007/11573067_9

Cited by 4 publications

(2 citation statements)

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“…On one hand, it is possible to find free biomedical vocabularies like Unified Medical Language System (UMLS) (Bodenreider, 2004), Human Phenotype Ontology (HPO) (Robinson et al, 2008;Köhler et al, 2017), Disease Ontology (DO) (Schriml et al, 2012) or MeSH (Lipscomb, 2000), all of them offering disease classifications, disease coding standards and associated medical resources. On the other hand, one can find bioinformatic databases created by complex medical systems, like DiseaseCard (Oliveira et al, 2004;Dias et al, 2005;Lopes & Oliveira, 2013), MalaCards (Rappaport et al, 2013;Rappaport et al, 2014;Espe, 2018), GeneCard (Safran et al, 2002), Diseases Database (DD) ( H Duncan, 2019, p. 2), DISEASES (Pletscher-Frankild et al, 2015), SIGnaling Network Open Resource (SIGNOR) (Perfetto et al, 2016), Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa & Goto, 2000), MENTHA (Calderone, Castagnoli & Cesareni, 2013), PhosphositePlus (Hornbeck et al, 2015), PhosphoELM (Hornbeck et al, 2015), UniProtKB (UniProt Consortium, 2014), Human Gene Mutation Database (HGMD) (Stenson et al, 2014), Comparative Toxicogenomics Database (CTD) (Mattingly et al, 2006), and the database for Pediatric Disease Annotation and Medicine (PedAM) (Jia et al, 2018). These datasets have generally been created by processing the information from several sources, and they usually offer simple search engines; yet, not all of them have a systematic and structured form of sharing their knowledge.…”

Section: Introductionmentioning

confidence: 99%

DISNET: a framework for extracting phenotypic disease information from public sources

García

Rodríguez‐González

Santamaría

et al. 2020

PeerJ

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Background Within the global endeavour of improving population health, one major challenge is the identification and integration of medical knowledge spread through several information sources. The creation of a comprehensive dataset of diseases and their clinical manifestations based on information from public sources is an interesting approach that allows one not only to complement and merge medical knowledge but also to increase it and thereby to interconnect existing data and analyse and relate diseases to each other. In this paper, we present DISNET (http://disnet.ctb.upm.es/), a web-based system designed to periodically extract the knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. Methods We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia and PubMed websites; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. Results We further present the validation of our system on Wikipedia and PubMed texts, obtaining the relevant accuracy. The final output includes the creation of a comprehensive symptoms-disease dataset, shared (free access) through the system’s API. We finally describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses. Discussion DISNET allows retrieving knowledge about the signs, symptoms and diagnostic tests associated with a disease. It is not limited to a specific category (all the categories that the selected sources of information offer us) and clinical diagnosis terms. It further allows to track the evolution of those terms through time, being thus an opportunity to analyse and observe the progress of human knowledge on diseases. We further discussed the validation of the system, suggesting that it is good enough to be used to extract diseases and diagnostically-relevant terms. At the same time, the evaluation also revealed that improvements could be introduced to enhance the system’s reliability.

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Section: Introductionmentioning

confidence: 99%

DISNET: a framework for extracting phenotypic disease information from public sources

García

Rodríguez‐González

Santamaría

et al. 2020

PeerJ

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“…Desde su página Web se pueden hacer consultas y visualizar los resultados conectados por medio de un árbol de grafos donde se aprecian las relaciones entre los distintos tipos de conocimientos de las distintas bases de datos; también cuenta con una API 51 con la que se accede a todos los datos de enfermedades recogidos en su base de conocimiento por medio de servicios de interfaces LinkedData 52 , un endpoint SPARQL y un conjunto de servicios REST. A pesar de la potencia de la herramienta solo se enfoca a un subconjunto de enfermedades (Oliveira et al, 2004;Dias et al, 2005;Lopes & Oliveira, 2013).…”

Section: Repositorios De Conocimiento Biomédicounclassified

Caracterización de enfermedades basada en su información fenotípica recuperada mediante la extracción de conocimiento biomédico de fuentes de información públicas

García¹

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Y como no, gracias a mi familia, a mi esposa y a mis tres hijos, por sacarme de mi comodidad, por hacer siempre lo mejor, por ayudarme y apoyarme en todos mis proyectos, por quererme sin condición. Somos un buen equipo de cinco. Quiero decir, que sin mi esposa este doctorado no hubiera existido, su determinación siempre me ha empujado hacia la mejor dirección. La admiro, la respeto, pero sobre todo la amo. Es la mujer adecuada para mi. Y por último, dar gracias a Dios, el precursor de mi historia, al que escribe cada renglón de mi vida, al que sabe darme lo que necesito siempre que lo necesito. Sin duda todas las cosas están bien hechas.

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DISNET: A framework for extracting phenotypic disease information from public sources

García

Santamaría

Valle

et al. 2018

Preprint

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Within the global endeavour of improving population health, one major challenge is the increasingly high cost associated with drug development. Drug repositioning, i.e. finding new uses for existing drugs, is a promising alternative; yet, its effectiveness has hitherto been hindered by our limited knowledge about diseases and their relationships. In this paper, we present DISNET (disnet.ctb.upm.es), a web-based system designed to extract knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia, PubMed and Mayo Clinic; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. We further present a validation of the processing performed by the system; and describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses.

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Integration of Genetic and Medical Information Through a Web Crawler System

Cited by 4 publications

References 4 publications

DISNET: a framework for extracting phenotypic disease information from public sources

DISNET: a framework for extracting phenotypic disease information from public sources

Caracterización de enfermedades basada en su información fenotípica recuperada mediante la extracción de conocimiento biomédico de fuentes de información públicas

DISNET: A framework for extracting phenotypic disease information from public sources

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