Evidence map›Paper›PMID 38627848›Full record

ArticleGenome medicine2024

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Xinzhi Yao, Sizhuo Ouyang, Yulong Lian, Qianqian Peng, Xionghui Zhou, Feier Huang, Xuehai Hu, Feng Shi, Jingbo Xia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome medicine, 2024. 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. Review
  2. Review
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

9 authors.

Xinzhi YaoCollege of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
Sizhuo OuyangCollege of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
Yulong LianCollege of Science, Huazhong Agricultural University, Wuhan, China.
Qianqian PengCollege of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
Xionghui ZhouCollege of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
Feier HuangCollege of Life Science and Technology, Huazhong Agricultural University, Wuhan, China.
Xuehai HuCollege of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
Feng ShiCollege of Science, Huazhong Agricultural University, Wuhan, China.
Jingbo XiaCollege of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China. xiajingbo.math@gmail.com.ORCID 0000-0002-7285-588X

Funding

The Major Project of Hubei Hongshan Laboratory No.2022HSZD031
6 · The paper itself

Abstract

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

Indexed as

Alzheimer DiseaseBreast NeoplasmsDeep LearningLung NeoplasmsBayes TheoremFemaleGenetic Association StudiesHumansAssociated genesData fusionEmbedding datap-value

Identifiers

PMID38627848
PMCPMC11020195

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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.