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

TL;DR

This review examines machine learning techniques tailored for small datasets in upstream bioprocessing, providing a taxonomy, analyzing methods, and offering practical insights for data-constrained biopharmaceutical applications.

Contribution

It introduces a taxonomy of ML methods for small data, thoroughly analyzes each method's core concepts, and evaluates their effectiveness in bioprocessing contexts.

Findings

Taxonomy of ML methods for small data in bioprocessing

Analysis of method effectiveness in real-world applications

Identification of research gaps and practical guidance

Abstract

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.