Evidence map›Paper›PMID 34202750›Full record

ArticleJournal of personalized medicine2021

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach.

Eugene Lin, Po-Hsiu Kuo, Wan-Yu Lin, Yu-Li Liu, Albert C Yang, Shih-Jen Tsai

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

3 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.International journal of molecular sciences · 2021
    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

6 authors at 5 institutions in 2 countries.

Eugene LinDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Po-Hsiu KuoDepartment of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan.ORCID 0000-0003-0365-3587
Wan-Yu LinDepartment of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan.
Yu-Li LiuCenter for Neuropsychiatric Research, National Health Research Institutes, Miaoli County 35053, Taiwan.ORCID 0000-0001-7519-2965
Albert C YangDivision of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA 02215, USA.
Shih-Jen TsaiDepartment of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan.ORCID 0000-0002-9987-022X
National Taiwan University · TWBeth Israel Deaconess Medical Center · USChina Medical University · TWNational Health Research Institutes · TWNational Yang Ming Chiao Tung University · TW

Funding

Ministry of Science and Technology, Taiwan MOST 109-2634-F-075-001Taipei Veterans General Hospital V108D44-001-MY3-1
6 · The paper itself

Abstract

In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified:

Indexed as

genome-wide association studymachine learningmajor depressive disorderpersonalized medicinesingle nucleotide polymorphisms

Identifiers

PMID34202750
PMCPMC8308113
OpenAlexW3174309346

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.