Mining bioprocess data: opportunities and challenges

被引:46
作者
Charaniya, Salim [1 ]
Hu, Wei-Shou [1 ]
Karypis, George [2 ]
机构
[1] Univ Minnesota, Dept Chem Engn & Mat Sci, Minneapolis, MN 55455 USA
[2] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA
关键词
D O I
10.1016/j.tibtech.2008.09.003
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Modern biotechnology production plants are equipped with sophisticated control, data logging and archiving systems. These data hold a wealth of information that might shed light on the cause of process outcome fluctuations, whether the outcome of concern is productivity or product quality. These data might also provide clues on means to further improve process outcome. Data-driven knowledge discovery approaches can potentially unveil hidden information, predict process outcome, and provide insights on implementing robust processes. Here we describe the steps involved in process data mining with an emphasis on recent advances in data mining methods pertinent to the unique characteristics of biological process data.
引用
收藏
页码:690 / 699
页数:10
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