ArticleFrontiers in bioengineering and biotechnology2024
Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.
Article in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview.Journal of pharmaceutical analysis · 2026Review
- Physics-guided machine learning framework for RCA concrete by experimental database, modelling, and statistical validation.Scientific reports · 2026Article
- Synthetic spectral libraries for Raman model calibration.Analytical and bioanalytical chemistry · 2025Article
- A Novel Raman-Chromatography Assembly for Automated Calibration and In-Line Monitoring in Bioprocessing.Engineering in life sciences · 2025Article
- Raman-based PAT for multi-attribute monitoring during VLP recovery by dual-stage CFF: attribute-specific spectral preprocessing for model transfer.Frontiers in bioengineering and biotechnology · 2025Article
- Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification.Frontiers in bioengineering and biotechnology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.