A heuristic approach to handling missing data in biologics manufacturing databases
- Submitting institution
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University College London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 12796
- Type
- D - Journal article
- DOI
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10.1007/s00449-018-02059-5
- Title of journal
- BIOPROCESS AND BIOSYSTEMS ENGINEERING
- Article number
- -
- First page
- 657
- Volume
- 42
- Issue
- 4
- ISSN
- 1615-7591
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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7
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Industrial collaboration. Established algebraic framework for bioprocess dataset gap-filling, yielded 15% performance improvement over existing algorithms in commercial best-in-class software (Simca). Selected by Bioprocess and Biosystems Engineering Editors as Best Paper-2019. Led to (i) new data science collaboration with Bioprocessing Team, Johnson Matthey-Cambridge (lead: Andrew Kaja) BBSRC-funded BioProNet Business Interaction Vouchers (£10k-BB/L013770/1), (ii) AstraZeneca-sponsored PhD studentship (£149k), (iii) additional Johnson Matthey funding (£40k) on digital bioprocessing, (iv) Royal Academy of Engineering (£80k) funding in fermentation data science. Invited talks at ESACT-UK 2020, and the Animal Cell Technology Industrial Platform meeting Neuchatel, Switzerland 2020. Led to Lectureship interview and securing post.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -