TY - JOUR
T1 - Mining bioprocess data
T2 - opportunities and challenges
AU - Charaniya, Salim
AU - Hu, Wei Shou
AU - Karypis, George
PY - 2008/12
Y1 - 2008/12
N2 - 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.
AB - 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.
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U2 - 10.1016/j.tibtech.2008.09.003
DO - 10.1016/j.tibtech.2008.09.003
M3 - Review article
C2 - 18977046
AN - SCOPUS:55549123480
SN - 0167-7799
VL - 26
SP - 690
EP - 699
JO - Trends in biotechnology
JF - Trends in biotechnology
IS - 12
ER -