ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2022
Semantic Annotation of NIH Funding Data for Supporting Rare Disease Research.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Synonym Augmentation for Rare Disease Identification in Unstructured Data.medRxiv : the preprint server for health sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
With the advances in science and technology, the number of research in rare diseases has dramatically increased over the past twenty years. Systematically accessing those research projects funded by NIH would allow us to assess the current status of research, and research gaps remain in this area. Consequently, new research might be inspired to bridge the gaps. We previously developed a knowledge graph to semantically represent NIH funded rare disease research projects by analyzing project titles. To expand the use of NIH funding data, in this study we extended the previous work in two folds, 1) we applied our self-developed NLP package named NormMap to identify rare disease related projects, 2) we semantically annotated project titles and abstracts with biomedical concepts in UMLS to illustrate the project aims. With such rich information extracted from NIH funding data via semantic annotation, an updated version of the knowledge graph will be developed to advance rare disease research as the next step.
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.