Ganoderma lucidum,a medicinal mushroom renowned for its production of a diverse array of compounds,accounts for the pharmacological effects including anti-infammatory,antioxidant,immunomodulatory,and anticancer charac...Ganoderma lucidum,a medicinal mushroom renowned for its production of a diverse array of compounds,accounts for the pharmacological effects including anti-infammatory,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 quantifcation 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.展开更多
Machine learning is the use of computers to learn the intrinsic laws and information contained in data through algorithms to gain new experience and knowledge,in order to improve the intelligence of computers,so that ...Machine learning is the use of computers to learn the intrinsic laws and information contained in data through algorithms to gain new experience and knowledge,in order to improve the intelligence of computers,so that they can make decisions similar to those made by humans when faced with problems.With the development of various industries,the amount of data has increased and the efficiency of data processing and analysis has become more demanding,a series of machine learning algorithms have emerged.Machine learning algorithms are essentially steps and processes that apply a large number of statistical principles to solve optimisation problems.Appropriate machine learning algorithms can be used to solve practical problems more efficiently for a wide range of model requirements.This paper presents the interim state of a dynamic disruption management software solution for logistics,using machine learning methods to study the extent to which stress is predicted based on physiological and subjective parameters,to prevent physical and mental stress on workers in the logistics industry,to maintain their health,to make them more optimistic and better able to adapt to their work,and to facilitate more accurate deployment of human resources by companies according to the real-time requirements of the logistics industry.展开更多
基金funded by DST-SERB,Govt.of India under the CRG project vide sanction order number CRG/2021/001815the financial support of the European Union under the REFRESH–Research Excellence for region Sustainability and High-tech Industries(No.CZ.10.03.01/00/22_003/0000048)via the operational Programme Just Transitionpartially carried out at the Jet Propulsion Laboratory,California Institute of Technology,under a contract with the National Aeronautics and Space Administration(No.80NM0018D0004)。
文摘Ganoderma lucidum,a medicinal mushroom renowned for its production of a diverse array of compounds,accounts for the pharmacological effects including anti-infammatory,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 quantifcation 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.
文摘Machine learning is the use of computers to learn the intrinsic laws and information contained in data through algorithms to gain new experience and knowledge,in order to improve the intelligence of computers,so that they can make decisions similar to those made by humans when faced with problems.With the development of various industries,the amount of data has increased and the efficiency of data processing and analysis has become more demanding,a series of machine learning algorithms have emerged.Machine learning algorithms are essentially steps and processes that apply a large number of statistical principles to solve optimisation problems.Appropriate machine learning algorithms can be used to solve practical problems more efficiently for a wide range of model requirements.This paper presents the interim state of a dynamic disruption management software solution for logistics,using machine learning methods to study the extent to which stress is predicted based on physiological and subjective parameters,to prevent physical and mental stress on workers in the logistics industry,to maintain their health,to make them more optimistic and better able to adapt to their work,and to facilitate more accurate deployment of human resources by companies according to the real-time requirements of the logistics industry.
