ArticleESCAPE. European Symposium on Computer Aided Process Engineering2018
Optimal Chemical Grouping and Sorbent Material Design by Data Analysis, Modeling and Dimensionality Reduction Techniques.
Article in ESCAPE. European Symposium on Computer Aided Process Engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Surrogate-Assisted Optimization of Highly Constrained Oil Recovery Processes Using Classification-Based Constraint Modeling.Industrial & engineering chemistry research · 2025Article
- Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data.Chemical engineering science · 2023Article
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- Predicting the Estrogen Receptor Activity of Environmental Chemicals by Single-Cell Image Analysis and Data-driven Modeling.ESCAPE. European Symposium on Computer Aided Process Engineering · 2021Article
- A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities.AIChE journal. American Institute of Chemical Engineers · 2020Article
- Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms.PLoS computational biology · 2020Article
- A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process.AIChE journal. American Institute of Chemical Engineers · 2019Article
- Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization.PloS one · 2019Article
- Development of the Texas A&M Superfund Research Program Computational Platform for Data Integration, Visualization, and Analysis.ESCAPE. European Symposium on Computer Aided Process Engineering · 2019Article
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Authors and funding
9 authors.
Funding
Abstract
The ultimate goal of the Texas A&M Superfund program is to develop comprehensive tools and models for addressing exposure to chemical mixtures during environmental emergency-related contamination events. With that goal, we aim to design a framework for optimal grouping of chemical mixtures based on their chemical characteristics and bioactivity properties, and facilitate comparative assessment of their human health impacts through read-across. The optimal clustering of the chemical mixtures guides the selection of sorption material in such a way that the adverse health effects of each group are mitigated. Here, we perform (i) hierarchical clustering of complex substances using chemical and biological data, and (ii) predictive modeling of the sorption activity of broad-acting materials via regression techniques. Dimensionality reduction techniques are also incorporated to further improve the results. We adopt several recent examples of chemical substances of Unknown or Variable composition Complex reaction products and Biological materials (UVCB) as benchmark complex substances, where the grouping of them is optimized by maximizing the Fowlkes-Mallows (FM) index. The effect of clustering method and different visualization techniques are shown to influence the communication of the groupings for read-across.
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Registered trials
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