Evidence map›Paper›PMID 32782383›Full record

ReviewJournal of human genetics2021

Artificial intelligence powered statistical genetics in biobanks.

Akira Narita, Masao Ueki, Gen Tamiya

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  14. Standard operating procedures for biobank in oncology.Frontiers in molecular biosciences · 2022
    Article
  15. Article
  16. An Improved Stacked Autoencoder for Metabolomic Data Classification.Computational intelligence and neuroscience · 2021
    Article
  17. 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

3 authors.

Akira NaritaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
Masao UekiRIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
Gen TamiyaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan. gtamiya@megabank.tohoku.ac.jp.ORCID http://orcid.org/0000-0002-0597-415X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large-scale, sometimes nationwide, prospective genomic cohorts biobanking rich biological specimens such as blood, urine and tissues, have been established and released their vast amount of data in several countries. These genetic and epidemiological resources are expected to allow investigators to disentangle genetic and environmental components conferring common complex diseases. There are, however, two major challenges to statistical genetics for this goal: small sample size-high dimensionality and multilayered-heterogenous endophenotypes. Rather counterintuitively, biobank data generally have small sample size relative to their data dimensionality consisting of genomic variation, lifestyle questionnaire, and sometimes their interaction. This is a widely acknowledged difficulty in data analysis, so-called "p»n problem" in statistics or "curse of dimensionality" in machine-learning field. On the other hand, we have too many measurements of individual health status, which are endophenotypes, such as health check-up data, images, psychological test scores in addition to metabolomics and proteomics data. These endophenotypes are rich but not so tractable because of their worsen dimensionality, and substantial correlation, sometimes confusing causation among them. We have tried to overcome the problems inherent to biobank data, using statistical machine-learning and deep-learning technologies.

Indexed as

Artificial IntelligencePolymorphism, Single NucleotideBiological Specimen BanksComputational BiologyGenetic Association StudiesGenome, HumanGenome-Wide Association StudyGenomicsHumansReproducibility of Results

Identifiers

PMID32782383

What OpenQuestion holds

Textmetadata
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.