title: Mining bioprocess data: opportunities and challenges.
abstract: 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.
authors.affiliation: Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455-0132, USA.
authors.forename: Salim
authors.initials: S
authors.lastname: Charaniya
authors.forename: Wei-Shou
authors.initials: WS
authors.lastname: Hu
authors.forename: George
authors.initials: G
authors.lastname: Karypis
mesh_terms: D002468:Cell Physiological Phenomena; D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D008991:Monitoring, Physiologic / Q000379:methods*