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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Rahit, K. M. Tahsin Hassan Avramovic, Vladimir Chong, Jessica X. Tarailo-Graovac, Maja |
| Abstract | Background Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest. Results GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation—whether through model organisms or cohort-based patient-matching approaches—for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150–250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017–2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed. Conclusions GPAD’s real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature. |
| Related Links | https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-024-05693-x.pdf |
| Ending Page | 21 |
| Page Count | 21 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712105 |
| DOI | 10.1186/s12859-024-05693-x |
| Journal | BMC Bioinformatics |
| Issue Number | 1 |
| Volume Number | 25 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-02-27 |
| Access Restriction | Open |
| Subject Keyword | Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Mendelian disorder NLP Gene-disease relationship Gene discovery Rare disease gene Trends in gene discovery Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology |
| Journal Impact Factor | 2.9/2023 |
| 5-Year Journal Impact Factor | 3.6/2023 |
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