Evidence map›Paper›PMID 42196225›Full record

ReviewInternational journal of molecular sciences2026

Advances in Functional Genomics and Biotechnology for Enhancing Therapeutic Potential of Medicinal Plants.

Wajid Zaman, SeonJoo Park

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. 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

2 authors.

Wajid ZamanDepartment of Life Sciences, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0001-6864-2366
SeonJoo ParkDepartment of Life Sciences, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0001-6494-3460

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medicinal plants have long served as a primary source of bioactive compounds with essential therapeutic applications. Recent advances in functional genomics and plant biotechnology now enable precise manipulation of metabolic pathways to enhance the production of specialized metabolites with medicinal value. However, an integrative understanding of how genomic discovery can be linked with pathway engineering, scalable production systems, and healthcare applications remains insufficiently developed. This knowledge gap limits the effective translation of molecular insights into the sustainable production of medicinally important compounds. The novelty of this review lies in its integrated framework linking functional genomic discovery with pathway engineering, synthetic biology, artificial intelligence-assisted prediction, and scalable production systems for medicinal plant-derived therapeutics. This review aims to provide a comprehensive overview of cutting-edge approaches in medicinal plant research, emphasizing high-throughput RNA sequencing, CRISPR/Cas9 gene editing, synthetic biology, and metabolic engineering for optimizing the production of key bioactive compounds, including artemisinin, cannabinoids, ginsenosides, and taxol. It further examines how these tools collectively support metabolite discovery, pathway elucidation, yield improvement, and biotechnological production in major medicinal plant systems. We explore the application of genomic and biotechnological approaches in plants such as

Indexed as

BiotechnologyGenomicsPlants, MedicinalCRISPR-Cas SystemsGene EditingHumansMetabolic EngineeringSynthetic BiologycannabinoidsCRISPR/Cas9functional genomicsmetabolic engineeringpersonalized medicineRNA sequencing

Identifiers

PMID42196225
PMCPMC13207428

What OpenQuestion holds

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LicenceCC BY
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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.