ArticlePeerJ2020
DISNET: a framework for extracting phenotypic disease information from public sources.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- GenTIGS: a database empowering research and clinical insights on rare genetic disorders with an Indian perspective.NAR genomics and bioinformatics · 2025Article
- Article
- Identifying symptom etiologies using syntactic patterns and large language models.Scientific reports · 2024Article
- Protein sequence analysis in the context of drug repurposing.BMC medical informatics and decision making · 2024Article
- Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios.BMC genomics · 2024Article
- Article
- Repositioning Drugs for Rare Diseases Based on Biological Features and Computational Approaches.Healthcare (Basel, Switzerland) · 2022Article
- Integrating heterogeneous data to facilitate COVID-19 drug repurposing.Drug discovery today · 2022Review
- Classifying diseases by using biological features to identify potential nosological models.Scientific reports · 2021Article
- Leveraging network analysis to evaluate biomedical named entity recognition tools.Scientific reports · 2021Article
- A data-driven methodology towards evaluating the potential of drug repurposing hypotheses.Computational and structural biotechnology journal · 2021Article
- Developing an automated mechanism to identify medical articles from wikipedia for knowledge extraction.International journal of medical informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWithin 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.
methodsWe 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.
resultsWe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.