Unraveling bioactive potential and production in Ganoderma lucidum through omics and machine learning modeling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F25%3A10258919" target="_blank" >RIV/61989100:27360/25:10258919 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1674638425000589" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1674638425000589</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.chmed.2025.05.003" target="_blank" >10.1016/j.chmed.2025.05.003</a>

Result language

angličtina

Original language name

Unraveling bioactive potential and production in Ganoderma lucidum through omics and machine learning modeling

Original language description

Ganoderma lucidum, a medicinal mushroom renowned for its production of a diverse array of compounds, accounts for the pharmacological effects including anti-inflammatory, antioxidant, immunomodulatory, and anticancer characteristics. Thus, it is recognized as a valuable species of interest in the pharmaceutical and nutraceutical industries due to its important medicinal properties. Recent advances in omics technologies such as genomes, transcriptomics, proteomics, and metabolomics have considerably increased our understanding of the bioactives in G. lucidum. This review explores the application of molecular breeding techniques to enhance both the yield and quality of G. lucidum across the food, pharmaceutical, and industrial sectors. The article discusses the current state of research on the use of contemporary omics technologies which studies and highlights future research directions that may increase the production of bioactive compounds for their therapeutic potential. Additionally, predictive methods with computational studies have recently emerged as effective tools for investigating bioactive constituents in G. lucidum, providing an organized and cost-effective strategy for understanding their bioactivity, interactions, and possible therapeutic uses. Omics and machine learning techniques can be applied to identify the candidates for pharmaceutical applications and to enhance the production of bioactive compounds in G. lucidum. The quantification and production of the bioactive compounds can be streamlined by the integrating computational study of bioactive compounds with non-destructive predictive machine learning models of the same. Synergistically, these techniques have the potential to be a promising approach for the future prediction of the bioactive constituents, without compromising the integrity of the fungal organism. (c) 2025 Tianjin Press of Chinese Herbal Medicines. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10400 - Chemical sciences

Project

—

Continuities

O - Projekt operacniho programu

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Chinese Herbal Medicines

ISSN

1674-6384

e-ISSN

2589-3610

Volume of the periodical

17

Issue of the periodical within the volume

3

Country of publishing house

SG - SINGAPORE

Number of pages

14

Pages from-to

414-427

UT code for WoS article

001549823500002

EID of the result in the Scopus database

—