mlsanityData to 14 Oct 2026

Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review

arXiv 2506.12322 ↗First posted 14 Jun 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

Data is crucial for machine learning (ML) applications, yet acquiring large datasets can be costly and time-consuming, especially in complex, resource-intensive fields like biopharmaceuticals. A key process in this industry is upstream bioprocessing, where living cells are cultivated and optimised to produce therapeutic proteins and biologics. The intricate nature of these processes, combined with high resource demands, often limits data collection, resulting in smaller datasets. This comprehensive review explores ML methods designed to address the challenges posed by small data and classifies them into a taxonomy to guide practical applications. Furthermore, each method in the taxonomy was thoroughly analysed, with a detailed discussion of its core concepts and an evaluation of its effectiveness in tackling small data challenges, as demonstrated by application results in the upstream bioprocessing and other related domains. By analysing how these methods tackle small data challenges from different perspectives, this review provides actionable insights, identifies current research gaps, and offers guidance for leveraging ML in data-constrained environments.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 25 of its 224 references that are in the map. First posted on arXiv 14 Jun 2025.

Builds on

25 of 224 references.

References · 25

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13 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Lessons Learned from Deploying Adaptive Machine Learning Agents with Limited Data for Real-time Cell Culture Process Monitoringcited by 1
  2. 2025Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Studycited by 1
  3. 2026Reinforcing Dual-Path Reasoning in Spatial Vision Language Modelscited by 1
  4. 2026Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecastingcited by 0