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Incyte Genomics to Sell Access to Proteome BioKnowledge Databases
Submitted by Martin Kucej; posted on Thursday, April 18, 2002 (14 comments)
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One of the most useful databases, which gathers comprehensive data on proteomes of several organisms, including yeast and human, will require a paid subscription as of June 1st of this year (2002).
Incyte Genomics plans to charge $2000 US per year per laboratory to access the library. A laboratory is defined to be ``a single Principal Investigator, his/her postdoctoral fellows, graduate students, and technical support (usually 5-8 individuals).'' According to information provided, ``larger labs, depending on their size, may be required to purchase multiple subscriptions.''
Charging the fee of $2000 US will simply mean cessation of its use for thousands of laboratories, mainly from poor countries, unfortunately, also including most of the European laboratories.
Incyte Genomics seems to take seriously its slogan: ``Integrating the Science and Business of Genomics.''
For more information:
http://www.incyte.com/sequence/proteome/subscription.shtml
http://www.incyte.com/sequence/proteome/faq.shtml#start
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the metalife predictor is based on the up-to-date methods of top development and technologies for these purposes. various algorithms are used for improvement and mutual combinations in order to maximize the correctness of forecasts. discrete intrinsic molecular characteristics are used by these algorithms. the metalife predictor contains also internal "machine learning" algorithms for the formation of functional characteristics of biological objects. one of functions realized by metalife predictor is the use of sequence-based vector analyses in multidimensional frames. these self-learning algorithms are useful for the forecasts of protein-protein interactions, subcellular localization of either prokaryontic and also eukaryontic proteins as well as in ontology-based classification of proteins.
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the metalife predictor is based on the up-to-date methods of top development and technologies for these purposes. various algorithms are used for improvement and mutual combinations in order to maximize the correctness of forecasts. discrete intrinsic molecular characteristics are used by these algorithms. the metalife predictor contains also internal "machine learning" algorithms for the formation of functional characteristics of biological objects. one of functions realized by metalife predictor is the use of sequence-based vector analyses in multidimensional frames. these self-learning algorithms are useful for the forecasts of protein-protein interactions, subcellular localization of either prokaryontic and also eukaryontic proteins as well as in ontology-based classification of proteins.
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