Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Hong CancerInnov22 1-1.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:Application of informatics in cancer research and clinical practice: Opportunities and challenges|Application of informatics in cancer research and clinical practice: Opportunities and challenges]]"'''
'''"[[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 />


[[Cancer informatics]] has significantly progressed in the big data era. We summarize the application of [[Informatics (academic field)|informatics]] approaches to the [[cancer]] domain from both the informatics perspective (e.g., [[Information management|data management]] and [[Information science|data science]]) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high-throughput [[omics]] [[data mining]], [[machine learning]] algorithms, [[artificial intelligence]] [[imaging]], and intelligent radiation ... ('''[[Journal:Application of informatics in cancer research and clinical practice: Opportunities and challenges|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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