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

arXiv: https://arxiv.org/abs/2506.12322 · HTML: https://mlsanity.com/paper/2506.12322/

## 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 (most cited first, 25 of 25)

- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks](https://mlsanity.com/paper/2007.15951/) (2020)
- [A Survey of Deep Meta-Learning](https://mlsanity.com/paper/2010.03522/) (2020)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [Data augmentation using synthetic data for time series classification with deep residual networks](https://mlsanity.com/paper/1808.02455/) (2018)
- [Reinforcement Learning for Batch Bioprocess Optimization](https://mlsanity.com/paper/1904.07292/) (2019)
- [Bayesian Learning for Neural Networks: an algorithmic survey](https://mlsanity.com/paper/2211.11865/) (2022)
- [Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review](https://mlsanity.com/paper/1905.02845/) (2019)
- [A Survey on Machine Learning from Few Samples](https://mlsanity.com/paper/2009.02653/) (2020)
- [Empirical comparison between autoencoders and traditional dimensionality reduction methods](https://mlsanity.com/paper/2103.04874/) (2021)
- [AutonoML: Towards an Integrated Framework for Autonomous Machine Learning](https://mlsanity.com/paper/2012.12600/) (2020)
- [Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA](https://mlsanity.com/paper/1612.08789/) (2016)
- [Interpretable Biomanufacturing Process Risk and Sensitivity Analyses for Quality-by-Design and Stability Control](https://mlsanity.com/paper/1909.04261/) (2019)
- [A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems](https://mlsanity.com/paper/2007.10818/) (2020)
- [Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors](https://mlsanity.com/paper/2011.13863/) (2020)
- [When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development](https://mlsanity.com/paper/2209.01083/) (2022)
- [Machine learning in bioprocess development: From promise to practice](https://mlsanity.com/paper/2210.02200/) (2022)
- [Neural ODEs as Feedback Policies for Nonlinear Optimal Control](https://mlsanity.com/paper/2210.11245/) (2022)
- [Semi-supervised Learning for Data-driven Soft-sensing of Biological and Chemical Processes](https://mlsanity.com/paper/2107.13822/) (2021)
- [SPSA-FSR: Simultaneous Perturbation Stochastic Approximation for Feature Selection and Ranking](https://mlsanity.com/paper/1804.05589/) (2018)
- [PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes](https://mlsanity.com/paper/2205.11673/) (2022)
- [The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry](https://mlsanity.com/paper/2211.04148/) (2022)
- [Deep Learning for Fast Inference of Mechanistic Models' Parameters](https://mlsanity.com/paper/2312.03166/) (2023)
- [Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations](https://mlsanity.com/paper/2402.00851/) (2024)

## Built on by (most cited first, 4 of 4)

- [Lessons Learned from Deploying Adaptive Machine Learning Agents with Limited Data for Real-time Cell Culture Process Monitoring](https://mlsanity.com/paper/2509.02606/) (2025)
- [Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study](https://mlsanity.com/paper/2512.03460/) (2025)
- [Reinforcing Dual-Path Reasoning in Spatial Vision Language Models](https://mlsanity.com/paper/2606.17539/) (2026)
- [Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecasting](https://mlsanity.com/paper/2606.26520/) (2026)

## 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.