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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Genome sequencing costs 2011.jpg|250px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''[[Genome informatics]]''' is a field of computational molecular biology and branch of [[Informatics (academic field)|informatics]] that uses computers, software, and computational solution techniques to make observations, resolve problems, and manage data related to the genomic function of DNA sequences, comparison of gene structures, determination of the tertiary structure of all proteins, and other molecular biological activities. The informatics side of genomics has largely focused on analytical tools and methodologies. DNA-microarray and sequencing technology helped researchers for the Human Genome Project, for example, analyze and understand thousands of genes and their expressions. By 2000, artificial neural networks were being theorized as a possible informatics tools to aid with data analysis and the problem of "high dimensionality" of the outputted data; by 2014 artificial neural networks were being proposed for cancer genomic research.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Genome informatics can help tackle problems and tasks such as analyzing DNA sequences, recognizing genes and proteins and predicting their structures, and predicting the biochemical function of new genes or fragments, as well as molecular profiling. ('''[[Genome 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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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...)

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