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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bohn JofLabMed2021 45-6.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Electronic tools in clinical laboratory diagnostics: Key examples, limitations, and value in laboratory medicine|Electronic tools in clinical laboratory diagnostics: Key examples, limitations, and value in laboratory medicine]]"'''
'''"[[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 />


Electronic tools in [[clinical laboratory]] diagnostics can assist [[laboratory]] professionals, clinicians, and patients in medical diagnostic management and laboratory test interpretation. With increasing implementation of [[electronic health record]]s (EHRs) and [[laboratory information system]]s (LIS) worldwide, there is increasing demand for well-designed and evidence-based electronic resources. Both complex data-driven and simple interpretative electronic healthcare tools are currently available to improve the integration of clinical and laboratory [[information]] towards a more patient-centered approach to medicine. Several studies have reported positive clinical impact of electronic healthcare tool implementation in clinical laboratory diagnostics, including in the management of neonatal bilirubinemia, cardiac disease, and nutritional status ... ('''[[Journal:Electronic tools in clinical laboratory diagnostics: Key examples, limitations, and value in laboratory medicine|Full article...]]''')<br />
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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...)

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