Cargando…

Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly

[Image: see text] While polymerization-induced self-assembly (PISA) has become a preferred synthetic route toward amphiphilic block copolymer self-assemblies, predicting their phase behavior from experimental design is extremely challenging, requiring time and work-intensive creation of empirical ph...

Descripción completa

Detalles Bibliográficos
Autores principales:	Lu, Yiwen, Yalcin, Dilek, Pigram, Paul J., Blackman, Lewis D., Boley, Mario
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	American Chemical Society 2023
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10268968/ https://www.ncbi.nlm.nih.gov/pubmed/37208794 http://dx.doi.org/10.1021/acs.jcim.3c00460

Ejemplares similares

Permeable Protein-Loaded Polymersome Cascade Nanoreactors by Polymerization-Induced Self-Assembly
por: Blackman, Lewis D., et al.
Publicado: (2017)

Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete
por: Rajczakowska, Magdalena, et al.
Publicado: (2023)

Comparison of photo- and thermally initiated polymerization-induced self-assembly: a lack of end group fidelity drives the formation of higher order morphologies
por: Blackman, Lewis D., et al.
Publicado: (2017)

Stability of Self-Assembled Polymeric Micelles in Serum
por: Lu, Jiao, et al.
Publicado: (2011)

Phase Diagrams of Polymerization-Induced Self-Assembly Are Largely Determined by Polymer Recombination
por: Petrov, Artem, et al.
Publicado: (2022)

An Interpretable Machine-Learning Algorithm to Predict Disordered Protein Phase Separation Based on Biophysical Interactions
por: Cai, Hao, et al.
Publicado: (2022)

On the interpretability of machine learning-based model for predicting hypertension
por: Elshawi, Radwa, et al.
Publicado: (2019)

Interpretable machine learning prediction of all-cause mortality
por: Qiu, Wei, et al.
Publicado: (2022)

Hydrothermal polymerization of porous aromatic polyimide networks and machine learning-assisted computational morphology evolution interpretation
por: Lahnsteiner, Marianne, et al.
Publicado: (2021)

Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning
por: Liu, Yanqin, et al.
Publicado: (2023)

Phase Diagram for Ideal Diblock-Copolymer Micelles Compared to Polymerization-Induced Self Assembly
por: Gavrilov, Alexey A., et al.
Publicado: (2020)

DNA–Polymer Nanostructures by RAFT Polymerization and Polymerization‐Induced Self‐Assembly
por: Lückerath, Thorsten, et al.
Publicado: (2020)

Interpretable Machine Learning with Python
por: Masis, Serg
Publicado: (2021)

Interpretable machine learning for genomics
por: Watson, David S.
Publicado: (2021)

Confinement of Therapeutic Enzymes in Selectively Permeable Polymer Vesicles by Polymerization-Induced Self-Assembly (PISA) Reduces Antibody Binding and Proteolytic Susceptibility
por: Blackman, Lewis D., et al.
Publicado: (2018)

Machine learning of material properties: Predictive and interpretable multilinear models
por: Allen, Alice E. A., et al.
Publicado: (2022)

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction
por: Jabal, Mohamed Sobhi, et al.
Publicado: (2022)

Polymerization-induced thermal self-assembly (PITSA)
por: Figg, C. Adrian, et al.
Publicado: (2015)

Chirality-controlled polymerization-induced self-assembly
por: Li, Haolan, et al.
Publicado: (2022)

When self-assembly meets interfacial polymerization
por: Shen, Qin, et al.
Publicado: (2023)

On the importance of interpretable machine learning predictions to inform clinical decision making in oncology
por: Lu, Sheng-Chieh, et al.
Publicado: (2023)

Editorial: Human-Interpretable Machine Learning
por: Tolomei, Gabriele, et al.
Publicado: (2022)

Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning
por: Lu, Xiao, et al.
Publicado: (2022)

Comparison and interpretability of machine learning models to predict severity of chest injury
por: Kulshrestha, Sujay, et al.
Publicado: (2021)

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis
por: Rosnati, Margherita, et al.
Publicado: (2021)

Interpretable Machine Learning Model for Locoregional Relapse Prediction in Oropharyngeal Cancers
por: Giraud, Paul, et al.
Publicado: (2020)

An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis
por: Conti, Allegra, et al.
Publicado: (2023)

Application of interpretable machine learning algorithms to predict distant metastasis in osteosarcoma
por: Bai, Bing‐li, et al.
Publicado: (2022)

Machine learning-based prediction model and visual interpretation for prostate cancer
por: Chen, Gang, et al.
Publicado: (2023)

Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke
por: Lee, Joonwon, et al.
Publicado: (2023)

Predicting Monomers for Use in Aqueous Ring-Opening Metathesis Polymerization-Induced Self-Assembly
por: Varlas, Spyridon, et al.
Publicado: (2019)

Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study
por: Hu, Chang, et al.
Publicado: (2022)

Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
por: Wang, Yu, et al.
Publicado: (2023)

Block copolymer microparticles comprising inverse bicontinuous phases prepared via polymerization-induced self-assembly
por: Yang, Pengcheng, et al.
Publicado: (2019)

Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers
por: Xie, Wenxiu, et al.
Publicado: (2021)

Chemoadaptive Polymeric Assemblies by Integrated Chemical Feedback in Self-Assembled Synthetic Protocells
por: Pearce, Samuel, et al.
Publicado: (2021)

Identifying domains of applicability of machine learning models for materials science
por: Sutton, Christopher, et al.
Publicado: (2020)

Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art
por: Champa-Bujaico, Elizabeth, et al.
Publicado: (2022)

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models
por: Fan, Yongxian, et al.
Publicado: (2023)

Comparison of Machine Learning Methods towards Developing Interpretable Polyamide Property Prediction
por: Lee, Franklin Langlang, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_l35nn3s8ch4uftnr89ug60ss5m