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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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/).

Název v anglickém jazyce

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

Popis výsledku anglicky

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/).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10400 - Chemical sciences

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Chinese Herbal Medicines

ISSN

1674-6384

e-ISSN

2589-3610

Svazek periodika

17

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

14

Strana od-do

414-427

Kód UT WoS článku

001549823500002

EID výsledku v databázi Scopus

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