Machine learning methods help accurate estimation of the hydrogen solubility in biomaterials

Cao, Yan; Karimi, Mehdi; Kamrani, Elham; Nourani, Pejman; Manesh, Afshin Mohammadi; Momenieskandari, Homa; Anqi, Ali E.

doi:10.1016/j.ijhydene.2021.10.259

Cited by 14 publications

(2 citation statements)

References 53 publications

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“…In the traditional GBDT algorithm, the most straightforward approach is to replace the categorical features with the average of the corresponding labels [ 52 ]. In the decision tree, the average value of the label will be used as the criterion for node splitting [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

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Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning

et al. 2022

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Metal-organic frameworks (MOFs) have been widely researched as drug delivery systems due to their intrinsic porous structures. Herein, machine learning (ML) technologies were applied for the screening of MOFs with high drug loading capacity. To achieve this, first, a comprehensive dataset was gathered, including 40 data points from more than 100 different publications. The organic linkers, metal ions, and the functional groups, as well as the surface area and the pore volume of the investigated MOFs, were chosen as the model’s inputs, and the output was the ibuprofen (IBU) loading capacity. Thereafter, various advanced and powerful machine learning algorithms, such as support vector regression (SVR), random forest (RF), adaptive boosting (AdaBoost), and categorical boosting (CatBoost), were employed to predict the ibuprofen loading capacity of MOFs. The coefficient of determination (R2) of 0.70, 0.72, 0.66, and 0.76 were obtained for the SVR, RF, AdaBoost, and CatBoost approaches, respectively. Among all the algorithms, CatBoost was the most reliable, exhibiting superior performance regarding the sparse matrices and categorical features. Shapley additive explanations (SHAP) analysis was employed to explore the impact of the eigenvalues of the model’s outputs. Our initial results indicate that this methodology is a well generalized, straightforward, and cost-effective method that can be applied not only for the prediction of IBU loading capacity, but also in many other biomaterials projects.

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Section: Resultsmentioning

confidence: 99%

“…The CatBoost algorithm is Categorical Features+Gradient Boosting [ 51 ], based on the GBDT algorithm. It is an improved gradient boosting decision tree algorithm and an open-source and modern gradient boosting library [ 49 , 52 ]. It uses multiple weak learners, which are then combined into an assembled algorithm of solid learners [ 53 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning

et al. 2022

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Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Wu,

Xu,

Fang

et al. 2024

Arch Computat Methods Eng

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In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an engineering perspective, biomaterials, biomechanics, and biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. In this context, in silico modelling stands out as an effective and efficient alternative for investigating complex interactive responses in vivo. This paper offers a comprehensive review of the swiftly expanding field of machine learning (ML) techniques, empowering biomedical engineering to develop cutting-edge treatments for addressing healthcare challenges. The review categorically outlines different types of ML algorithms. It proceeds by first assessing their applications in biomaterials, covering such aspects as data mining/processing, digital twins, and data-driven design. Subsequently, ML approaches are scrutinised for the studies on mono-/multi-scale biomechanics and mechanobiology. Finally, the review extends to ML techniques in bioprinting and biomanufacturing, encompassing design optimisation and in situ monitoring. Furthermore, the paper presents typical ML-based applications in implantable devices, including tissue scaffolds, orthopaedic implants, and arterial stents. Finally, the challenges and perspectives are illuminated, providing insights for academia, industry, and biomedical professionals to further develop and apply ML strategies in future studies.

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