Evidence map›Paper›PMID 30809243›Full record

ReviewFrontiers in genetics2019

Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics.

Pawel Suwinski, ChuangKee Ong, Maurice H T Ling, Yang Ming Poh, Asif M Khan, Hui San Ong

Abstract readReview
In one paragraph

Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 1 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

92 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. The clinical impact of chronopharmacology on current medicine.Naunyn-Schmiedeberg's archives of pharmacology · 2025
    Review
  12. Review
  13. Whole-Exome Analysis and Osteosarcoma: A Game Still Open.International journal of molecular sciences · 2024
    Review
  14. Review
  15. Article
  16. Article
  17. Review
  18. Cellular and molecular mechanisms of skin wound healing.Nature reviews. Molecular cell biology · 2024
    Review
  19. Review
  20. Review

32 more citing papers are in PubMed but not listed here.

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.

Pawel SuwinskiMalaysian Genomics Resource Centre Berhad, Kuala Lumpur, Malaysia.
ChuangKee OngCentre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia.
Maurice H T LingCentre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia.
Yang Ming PohCentre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia.
Asif M KhanCentre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia.
Hui San OngCentre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a growing attention toward personalized medicine. This is led by a fundamental shift from the 'one size fits all' paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data "10 Vs" and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine.

Indexed as

analyticsbig dataexomepersonalized medicineprecisionsequencing

Identifiers

PMID30809243
PMCPMC6379253

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.