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'''[[Cancer informatics]]''' is a multidisciplinary field of science that "deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of [[information]] in cancer" research and treatment. Like many other fields of science, researchers in cancer biology have seen a dramatic increase in the amount of clinical and research data, in particular with genomic and molecular cancer data. While this data can benefit researchers' understanding of cancer behavior and development of better therapies, new and improved data management and analysis tools are needed. Cancer informatics attempts to provide those tools "that interconnect research, clinical activities, and data in an organized and efficient manner, with as broad a database as possible." For many, the coupling of cancer informatics and other bioinformatics tools with computational modeling and statistical analysis will accelerate the goal of making cancer a more treatable if not curable disease.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Cancer informatics can help tackle problems and tasks such as the development of computational diagnosis, prognosis, and predictive models; the development of standards for the entry, annotation, and sharing of clinical cancer data; and the management and distribution of annotated molecular data for further research. ('''[[Cancer informatics|Full article...]]''')<br />
[[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 />
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''Recently featured'': [[Evolutionary informatics]], [[Scientific data management system]], [[Centers for Disease Control and Prevention]]
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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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