Evidence map›Paper›PMID 32315351›Full record

ArticlePloS one2020

PRIORI-T: A tool for rare disease gene prioritization using MEDLINE.

Aditya Rao, Thomas Joseph, Vangala G Saipradeep, Sujatha Kotte, Naveen Sivadasan, Rajgopal Srinivasan

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Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Aditya RaoTCS Research and Innovation, Tata Consultancy Services Ltd., Hyderabad, INDIA.ORCID 0000-0002-7161-737X
Thomas JosephTCS Research and Innovation, Tata Consultancy Services Ltd., Hyderabad, INDIA.
Vangala G SaipradeepTCS Research and Innovation, Tata Consultancy Services Ltd., Hyderabad, INDIA.
Sujatha KotteTCS Research and Innovation, Tata Consultancy Services Ltd., Hyderabad, INDIA.
Naveen SivadasanTCS Research and Innovation, Tata Consultancy Services Ltd., Hyderabad, INDIA.
Rajgopal SrinivasanTCS Research and Innovation, Tata Consultancy Services Ltd., Hyderabad, INDIA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPhenotype-driven rare disease gene prioritization relies on high quality curated resources containing disease, gene and phenotype annotations. However, the effectiveness of gene prioritization tools is constrained by the incomplete coverage of rare disease, phenotype and gene annotations in such curated resources.

methodsWe extracted rare disease correlation pairs involving diseases, phenotypes and genes from MEDLINE abstracts and used the information propagation algorithm GCAS to build an association network. We built a tool called PRIORI-T for rare disease gene prioritization that uses this network for phenotype-driven rare disease gene prioritization. The quality of disease-gene associations in PRIORI-T was compared with resources such as DisGeNET and Open Targets in the context of rare diseases. The gene prioritization performance of PRIORI-T was evaluated using phenotype descriptions of 230 real-world rare disease clinical cases collated from recent publications, as well as compared to other gene prioritization tools such as HANRD and Orphamizer.

resultsPRIORI-T contains qualitatively better associations than DisGeNET and Open Targets. Furthermore, the causal genes were captured within Top-50 for more than 40% of the real-world clinical cases and within Top-300 for more than 72% of the cases when PRIORI-T was used for gene prioritization. It outperformed other gene prioritization tools such as HANRD and Orphamizer that primarily rely on curated resources.

conclusionsPRIORI-T exhibited improved gene prioritization performance without requiring high quality curated data. Thus, it holds great promise in phenotype-driven gene prioritization for rare disease studies.

Indexed as

AlgorithmsMEDLINEComputational BiologyHumansPhenotypeRare Diseases

Identifiers

PMID32315351
PMCPMC7173875

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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.