Machine Learning and Deep Learning methods for predictive modelling from Raman spectra in bioprocessing

• URL: http://arxiv.org/abs/2005.02935v1
• Date: Wed, 6 May 2020 16:15:08 GMT
• Title: Machine Learning and Deep Learning methods for predictive modelling from Raman spectra in bioprocessing
• Authors: Semion Rozov
• Abstract summary: This work focused on pretreatment methods of Raman spectra for the facilitation of the regression task using Machine Learning and Deep Learning methods. In the majority of cases, this robustness allowed to outperform conventional Raman models in terms of prediction error and prediction robustness.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In chemical processing and bioprocessing, conventional online sensors are limited to measure only basic process variables like pressure and temperature, pH, dissolved O and CO$_2$ and viable cell density (VCD). The concentration of other chemical species is more difficult to measure, as it usually requires an at-line or off-line approach. Such approaches are invasive and slow compared to on-line sensing. It is known that different molecules can be distinguished by their interaction with monochromatic light, producing different profiles for the resulting Raman spectrum, depending on the concentration. Given the availability of reference measurements for the target variable, regression methods can be used to model the relationship between the profile of the Raman spectra and the concentration of the analyte. This work focused on pretreatment methods of Raman spectra for the facilitation of the regression task using Machine Learning and Deep Learning methods, as well as the development of new regression models based on these methods. In the majority of cases, this allowed to outperform conventional Raman models in terms of prediction error and prediction robustness.

Related papers

• Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review [63.31328039424469]
  This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to optimize downstream reward functions. We explain the application of various RL algorithms, including PPO, differentiable optimization, reward-weighted MLE, value-weighted sampling, and path consistency learning.
  arXiv  Detail & Related papers  (2024-07-18T17:35:32Z)
• Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms [88.74308282658133]
  Reization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics. Recent studies have revealed that, when applied to long-term reinforcement learning problems, model-based RP PGMs may experience chaotic and non-smooth optimization landscapes. We propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.
  arXiv  Detail & Related papers  (2023-10-30T18:43:21Z)
• Machine Learning Small Molecule Properties in Drug Discovery [44.62264781248437]
  We review a wide range of properties, including binding affinities, solubility, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) We discuss existing popular descriptors and embeddings, such as chemical fingerprints and graph-based neural networks. Finally, techniques to provide an understanding of model predictions, especially for critical decision-making in drug discovery are assessed.
  arXiv  Detail & Related papers  (2023-08-02T22:18:41Z)
• Comparing Spectroscopy Measurements in the Prediction of in Vitro Dissolution Profile using Artificial Neural Networks [0.0]
  Dissolution testing is part of the target product quality that is essential in approving new products in the pharmaceutical industry. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. Raman and near-infrared (NIR) spectroscopies are two fast and complementary methods that provide information on the tablets' physical and chemical properties.
  arXiv  Detail & Related papers  (2022-10-19T04:34:04Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression [71.7560927415706]
  latent hybridisation model (LHM) integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system. We evaluate LHM on synthetic data as well as real-world intensive care data of COVID-19 patients.
  arXiv  Detail & Related papers  (2021-06-05T11:42:45Z)
• Extracting Chemical-Protein Interactions via Calibrated Deep Neural Network and Self-training [0.8376091455761261]
  "calibration" techniques have been applied to deep learning models to estimate the data uncertainty and improve the reliability. In this study, to extract chemical--protein interactions, we propose a DNN-based approach incorporating uncertainty information and calibration techniques. Our approach has achieved state-of-the-art performance with regard to the Biocreative VI ChemProt task, while preserving higher calibration abilities than those of previous approaches.
  arXiv  Detail & Related papers  (2020-11-04T10:14:31Z)
• Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets [0.0]
  Current automated methods for analyzing spectra are either (a) data-driven, which requires prior knowledge of stellar parameters and elemental abundances, or (b) based on theoretical synthetic models that are susceptible to the gap between theory and practice. We present a hybrid generative domain adaptation method that turns simulated stellar spectra into realistic spectra by applying unsupervised learning to large spectroscopic surveys.
  arXiv  Detail & Related papers  (2020-07-06T23:06:58Z)
• Convolutional neural networks for classification and regression analysis of one-dimensional spectral data [0.0]
  Convolutional neural networks (CNNs) are widely used for image recognition and text analysis. The performance of a CNN was investigated for classification and regression analysis of spectral data.
  arXiv  Detail & Related papers  (2020-05-15T13:20:05Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)
• Regularizing Class-wise Predictions via Self-knowledge Distillation [80.76254453115766]
  We propose a new regularization method that penalizes the predictive distribution between similar samples. This results in regularizing the dark knowledge (i.e., the knowledge on wrong predictions) of a single network. Our experimental results on various image classification tasks demonstrate that the simple yet powerful method can significantly improve the generalization ability.
  arXiv  Detail & Related papers  (2020-03-31T06:03:51Z)

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.