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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Jadhav IntJofMolSci23 24-9.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:A metabolomics and big data approach to cannabis authenticity (authentomics)|A metabolomics and big data approach to cannabis authenticity (authentomics)]]"'''  
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''
 
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


With the increasing accessibility of [[cannabis]] ([[Cannabis sativa|''Cannabis sativa'' L.]], also known as marijuana and [[hemp]]), its products are being developed as [[Cannabis concentrate|extracts]] for both recreational and [[Cannabis (drug)|therapeutic]] use. This has led to increased scrutiny by [[Regulatory compliance|regulatory bodies]], who aim to understand and regulate the complex chemistry of these products to ensure their safety and efficacy. Regulators use targeted analyses to track the concentration of key bioactive [[Metabolomics|metabolites]] and potentially harmful [[Contamination|contaminants]], such as [[heavy metals]] and other impurities. However, the complexity of cannabis' metabolic pathways requires a more comprehensive approach. A non-targeted metabolomic analysis of cannabis products is necessary to generate data that can be used to determine their authenticity and efficacy ... ('''[[Journal:A metabolomics and big data approach to cannabis authenticity (authentomics)|Full article...]]''')<br />
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Latest revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

Recently featured: