Assessment of several machine learning methods towards reliable prediction of hormone receptor binding affinity

Wong, Jia Haur; Zidar, Jernej; Ho, Junming; Wang, Yi; Lee, Kee Khoon; Zheng, Jianmin; Sullivan, Michael B.; You, Xiaorong; Kriegel, Robert M.

doi:10.1016/j.cdc.2017.05.002

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…For good performance, the value of CC should be close to 1 (Wong et al, 2017;Golkarnarenji et al, 2018;Shihabudheen and Pillai, 2017). In all of the above equations, y i is the experimental data,ŷ i is the predicted data, y is the experimental average data and N is the size of the data set.…”

Section: 222mentioning

confidence: 98%

“…SVM algorithm in regression (support vector regression [SVR]) offers attractive possibilities for uncertain modeling and optimizing, which is more applicable for complex systems that are difficult to be described by physical models. In the past few years, there has been increasing interest in the application of SVR approaches in various engineering fields (Ahmad et al, 2015;Fernandez-Lozano et al, 2016;Yang et al, 2013;Mannodi-Kanakkithodi et al, 2016;Yang et al, 2017;Wong et al, 2017;Kazemian et al, 2013). Also, it has varied applications in many fields of research and has been recently used in predicting the properties of materials that are difficult to obtain by the experimental methods, such as Owolabi et al (2015) has successfully applied SVR to predict the average surface energy of materials.…”

Section: Introductionmentioning

confidence: 99%

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Predictor and optimizer system on selective catalytic reduction of NO in activated carbons based on experiment and computational intelligence technique

Yang

Song

et al. 2020

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Purpose Nitrogen oxides (NOx) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NOx hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods. Design/methodology/approach Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model. Findings The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data. Originality/value SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

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Section: 222mentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Predictor and optimizer system on selective catalytic reduction of NO in activated carbons based on experiment and computational intelligence technique

Yang

Song

et al. 2020

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“…Recent technological advancements have been significantly contributing to the revolutionary development of computational models, leading to a paradigm shift from 2D to 3D models. Notably, advancements in machine learning, particularly artificial intelligence, have enabled the development of models capable of predicting hormone receptor binding affinity (Wong et al, 2017). It is worth highlighting the substantial contribution of in silico simulation in predicting the human response to COVID vaccines.…”

Section: Replacementmentioning

confidence: 99%

A Brief Review of the European Directive on 3Rs and Facilitating Animal Experimentation

Ali

2023

AAK

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The value of animals to humankind extends beyond their provision of food and physical resources; they also serve a wide range of purposes, including their role in experimentation. Given that animals play a substantial part in experimentation, it becomes imperative to improve their living conditions, at the very least as a form of compensation. As a result, the driving force behind the enactment of various legislations, including the European Union Directive (2010/63/EU), is human morality. This directive primarily pertains to specific European member states and is fundamentally aimed at safeguarding the welfare of animals used in experimentation by advocating for the implementation of the 3Rs: replacement, reduction, and refinement. Nevertheless, concerns have arisen, particularly regarding how the provisions of the directive might constrain or influence the advancement of experimentation, notably within the biomedical field. This review explores the nexus between Directive 2010/63/EU and the facilitation of animal experimentation, with a specific focus on the 3Rs. Findings reveal that the judicious application of the 3Rs markedly enhances the progress of experimentation without compromising the quality of outcomes. Furthermore, recent studies propose that the 3Rs alone may not suffice, suggesting the need to introduce additional relevant Rs. Hence, the current decrees are deemed adequate for a certain period, with potential modifications in the future. In order to encourage the advancement of experimentation without jeopardizing animal welfare, it is imperative to conduct periodic, routine reviews.

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“…Machine learning (ML) is a subset of artificial intelligence (AI) that covers a wide variety of modelling tools used for a vast range of data processing tasks; it has gained popularity over the last decade across the majority of scientific disciplines [68]. There are many studies that have employed ML models to primarily forecast NM's safety and toxicity [69][70][71]. Mirzaei, et al [72] developed a tool to predict the antimicrobial capacity expressed as zone of inhibition of various NMs using regression models.…”

Section: Machine Learningmentioning

confidence: 99%

A Supervised Machine-Learning Prediction of Textile’s Antimicrobial Capacity Coated with Nanomaterials

et al. 2021

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Textile materials, due to their large surface area and moisture retention capacity, allow the growth of microorganisms, causing undesired effects on the textile and on the end-users. The textile industry employs nanomaterials (NMs)/composites and nanofibers to enhance textile features such as water/dirt-repellent, conductivity, antistatic properties, and enhanced antimicrobial properties. As a result, textiles with antimicrobial properties are an area of interest to both manufacturers and researchers. In this study, we present novel regression models that predict the antimicrobial activity of nano-textiles after several washes. Data were compiled following a literature review, and variables related to the final product, such as the experimental conditions of nano-coating (finishing technologies) and the type of fabric, the physicochemical (p-chem) properties of NMs, and exposure variables, were extracted manually. The random forest model successfully predicted the antimicrobial activity with encouraging results of up to 70% coefficient of determination. Attribute importance analysis revealed that the type of NM, shape, and method of application are the primary features affecting the antimicrobial capacity prediction. This tool helps scientists to predict the antimicrobial activity of nano-textiles based on p-chem properties and experimental conditions. In addition, the tool can be a helpful part of a wider framework, such as the prediction of products functionality embedded into a safe by design paradigm, where products’ toxicity is minimized, and functionality is maximized.

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Assessment of several machine learning methods towards reliable prediction of hormone receptor binding affinity

Cited by 7 publications

References 27 publications

Predictor and optimizer system on selective catalytic reduction of NO in activated carbons based on experiment and computational intelligence technique

Predictor and optimizer system on selective catalytic reduction of NO in activated carbons based on experiment and computational intelligence technique

A Brief Review of the European Directive on 3Rs and Facilitating Animal Experimentation

A Supervised Machine-Learning Prediction of Textile’s Antimicrobial Capacity Coated with Nanomaterials

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