Mining bioprocess data: opportunities and challenges

Salim Charaniya, Wei Shou Hu, George Karypis

Research output: Contribution to journalReview articlepeer-review

40 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)690-699
Number of pages10
JournalTrends in biotechnology
Issue number12
StatePublished - Dec 1 2008


Dive into the research topics of 'Mining bioprocess data: opportunities and challenges'. Together they form a unique fingerprint.

Cite this