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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Keck Bioimaging Lab.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''[[Bioimage informatics]]''' is a multidisciplinary sub-field of [[bioinformatics]] and computational biology that involves the development and use of computational techniques to analyze bioimages, especially cellular and molecular images, on a large scale fashion, with the goal of mining useful knowledge out of complicated and heterogeneous images and related metadata.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


The field of bioimage informatics is somewhat related to [[Imaging informatics|medical imaging informatics]], in so much as some of the advances in that field have found their way to the technology of analyzing bioimages. However, "it is very challenging to directly apply existing medical image analysis methods to ... bioimage informatics problems." Some of the challenges bioimages pose to researchers include the difficulty of analyzing at the cellular and molecular scales, the large size of the files, and the amount of time required to manually analyze the files. These challenges require automatic high-throughput analysis techniques, novel algorithms, and advanced systems to deal with the tasks of processing, storing, visualizing, and mining bioimages. ('''[[Bioimage 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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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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