Evidence map›Paper›PMID 37647638›Full record

ArticleBioinformatics (Oxford, England)2023

diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype.

Daoyi Huang, Jianping Jiang, Tingting Zhao, Shengnan Wu, Pin Li, Yongfen Lyu, Jincai Feng, Mingyue Wei, Zhixing Zhu, Jianlei Gu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Autonomous Self-Evolving Research on Biomedical Data: The DREAM Paradigm.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  2. Article
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

13 authors.

Daoyi HuangState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.ORCID 0009-0004-5545-4381
Jianping JiangState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Tingting ZhaoShanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shengnan WuShanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Pin LiShanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yongfen LyuShanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jincai FengShanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Mingyue WeiShanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Zhixing ZhuShanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jianlei GuState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Yongyong RenState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.ORCID 0000-0001-9217-3483
Guangjun YuShanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Hui LuState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.ORCID 0000-0001-8347-0830

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

summaryThe next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods. AVAILABILITY AND IMPLEMENTATION: diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

Indexed as

Computational BiologyBayes TheoremChildChinaGenotypeHumansPhenotype

Identifiers

PMID37647638
PMCPMC10500091

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Registered trials

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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.