Evidence map›Paper›PMID 35563034›Full record

ReviewInternational journal of molecular sciences2022

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.

Mubashir Hassan, Faryal Mehwish Awan, Anam Naz, Enrique J deAndrés-Galiana, Oscar Alvarez, Ana Cernea, Lucas Fernández-Brillet, Juan Luis Fernández-Martínez, Andrzej Kloczkowski

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 3 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

62 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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2 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

9 authors.

Mubashir HassanInstitute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan.
Faryal Mehwish AwanDepartment of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan.ORCID 0000-0002-8146-8929
Anam NazInstitute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan.ORCID 0000-0002-6514-5417
Enrique J deAndrés-GalianaGroup of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain.
Oscar AlvarezDeepBioInsights, 38311 La Florida, Spain.
Ana CerneaDeepBioInsights, 38311 La Florida, Spain.ORCID 0000-0001-8890-6429
Lucas Fernández-BrilletDeepBioInsights, 38311 La Florida, Spain.
Juan Luis Fernández-MartínezGroup of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain.ORCID 0000-0002-4758-2832
Andrzej KloczkowskiThe Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH 43205, USA.ORCID 0000-0003-1002-5095

Funding

Novel Use of Genome Information to Understand MutationsR01HG012117 · NHGRI · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L, KLOCZKOWSKI, ANDRZEJ · 2021 to 2025
$2.3M
Protein Sequence MatchingR01GM127701 · NIGMS · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L, KLOCZKOWSKI, ANDRZEJ · 2018 to 2021
$1.4M
NHGRI NIH HHS R01 HG012117NIGMS NIH HHS R01 GM127701NIH HHS R01GM127701NIH HHS R01HG012117
6 · The paper itself

Abstract

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Indexed as

Data SciencePrecision MedicineBig DataDelivery of Health CareGenomicsbig data analyticscomputational approachesgenomicshealthpersonalized medicine

Identifiers

PMID35563034
PMCPMC9104788

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.