Evidence map›Paper›PMID 37738675›Full record

ArticleBriefings in functional genomics2024

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Yousang Jo, Maree J Webster, Sanghyeon Kim, Doheon Lee

Open access · hybridAbstract read
In one paragraph

Article in Briefings in functional genomics, 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
0.9field-weighted citation impact, top 20% of its field
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, 3 citations in OpenAlex.

  1. Review
  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

4 authors at 2 institutions in 2 countries.

Yousang JoDepartment of Bio and Brain Engineering, KAIST, Daejeon, South Korea.ORCID 0000-0001-8103-4998
Maree J WebsterBrain Research Laboratory, Stanley Medical Research Institute, Rockville, MD, USA.
Sanghyeon KimBrain Research Laboratory, Stanley Medical Research Institute, Rockville, MD, USA.
Doheon LeeDepartment of Bio and Brain Engineering, KAIST, Daejeon, South Korea.ORCID 0000-0001-9070-4316
Korea Advanced Institute of Science and Technology · KRStanley Medical Research Institute · US

Funding

Bio and Medical Technology Development Program of the Ministry of Science, and ICTNational Research Foundation 2022M3A9B6017511
6 · The paper itself

Abstract

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-precision data (SLEM) to explore the SNP combination effects on schizophrenia through intermediate molecular and cellular functions. The SLEM technique utilizes two levels of precision data for learning. It constructs initial backbone networks with more precise but small amount of multilevel assay data. Then, it learns strengths of intermediate interactions with the less precise but massive amount of GWAS data. The learned networks facilitate identifying effective SNP interactions from the intractably large space of all possible SNP combinations. We have shown that the extracted SNP combinations show higher accuracy than any single SNPs and preserve the accuracy in an independent dataset. The learned networks also provide interpretations of molecular and cellular interactions of SNP combinations toward schizophrenia etiology.

Indexed as

Deep LearningGenetic Predisposition to DiseaseGenome-Wide Association StudyPolymorphism, Single NucleotideSchizophreniaHumansGWASinterpretable machine learningschizophreniaSNP combination

Identifiers

PMID37738675
PMCPMC11428150
OpenAlexW4386946705

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.