Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling
with Small Data
- URL: http://arxiv.org/abs/2211.14493v1
- Date: Sat, 26 Nov 2022 06:38:34 GMT
- Title: Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling
with Small Data
- Authors: Yuan Sun, Winton Nathan-Roberts, Tien Dung Pham, Ellen Otte, Uwe
Aickelin
- Abstract summary: We propose to use a statistical machine learning approach, multi-fidelity Gaussian process, for process modelling in biomanufacturing.
We apply the multi-fidelity Gaussian process to solve two significant problems in biomanufacturing, bioreactor scale-up and knowledge transfer across cell lines, and demonstrate its efficacy on real-world datasets.
- Score: 1.4687789417816917
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In biomanufacturing, developing an accurate model to simulate the complex
dynamics of bioprocesses is an important yet challenging task. This is
partially due to the uncertainty associated with bioprocesses, high data
acquisition cost, and lack of data availability to learn complex relations in
bioprocesses. To deal with these challenges, we propose to use a statistical
machine learning approach, multi-fidelity Gaussian process, for process
modelling in biomanufacturing. Gaussian process regression is a
well-established technique based on probability theory which can naturally
consider uncertainty in a dataset via Gaussian noise, and multi-fidelity
techniques can make use of multiple sources of information with different
levels of fidelity, thus suitable for bioprocess modeling with small data. We
apply the multi-fidelity Gaussian process to solve two significant problems in
biomanufacturing, bioreactor scale-up and knowledge transfer across cell lines,
and demonstrate its efficacy on real-world datasets.
Related papers
- Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery [56.622854875204645]
We present an innovative approach utilizing data-driven computational tools, leveraging an advanced Transformer model, to unearth gene-gene interactions.
A novel weighted diversified sampling algorithm computes the diversity score of each data sample in just two passes of the dataset.
arXiv Detail & Related papers (2024-10-21T03:35:23Z) - BioDiffusion: A Versatile Diffusion Model for Biomedical Signal
Synthesis [4.765541373485142]
BioDiffusion is a diffusion-based probabilistic model optimized for the synthesis of biomedical signals.
Our research encompasses both qualitative and quantitative assessments of the synthesized data quality.
arXiv Detail & Related papers (2024-01-12T23:52:44Z) - Causal machine learning for single-cell genomics [94.28105176231739]
We discuss the application of machine learning techniques to single-cell genomics and their challenges.
We first present the model that underlies most of current causal approaches to single-cell biology.
We then identify open problems in the application of causal approaches to single-cell data.
arXiv Detail & Related papers (2023-10-23T13:35:24Z) - Machine learning in bioprocess development: From promise to practice [58.720142291102135]
Data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces.
The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development.
arXiv Detail & Related papers (2022-10-04T13:48:59Z) - Differentiable Agent-based Epidemiology [71.81552021144589]
We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation.
GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
arXiv Detail & Related papers (2022-07-20T07:32:02Z) - Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs.
We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling.
We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
arXiv Detail & Related papers (2022-06-10T04:54:13Z) - Opportunities of Hybrid Model-based Reinforcement Learning for Cell
Therapy Manufacturing Process Development and Control [6.580930850408461]
Key challenges of cell therapy manufacturing include high complexity, high uncertainty, and very limited process data.
We propose a framework named "hybridRL" to efficiently guide process development and control.
In the empirical study, cell therapy manufacturing examples are used to demonstrate that the proposed hybrid-RL framework can outperform the classical deterministic mechanistic model assisted process optimization.
arXiv Detail & Related papers (2022-01-10T00:01:19Z) - Policy Optimization in Bayesian Network Hybrid Models of
Biomanufacturing Processes [3.124775036986647]
Biomanufacturing processes require close monitoring and control.
We develop a novel model-based reinforcement learning framework that can achieve human-level control in low-data environments.
arXiv Detail & Related papers (2021-05-13T20:39:02Z) - Exploring the potential of transfer learning for metamodels of
heterogeneous material deformation [0.0]
We show that transfer learning can be used to leverage both low-fidelity simulation data and simulation data.
We extend Mechanical MNIST, our open source benchmark dataset of heterogeneous material undergoing large deformation.
We show that transferring the knowledge stored in metamodels trained on these low-fidelity simulation results can vastly improve the performance of metamodels used to predict the results of high-fidelity simulations.
arXiv Detail & Related papers (2020-10-28T12:43:46Z) - Towards an Automatic Analysis of CHO-K1 Suspension Growth in
Microfluidic Single-cell Cultivation [63.94623495501023]
We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data.
Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
arXiv Detail & Related papers (2020-10-20T08:36:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.