Development of Regression Model to Predicting Yield Strength for Different Steel Grades

Tumrate, Charanjeet Singh; Chowdhury, Shambo Roy; Mishra, Dhaneshwar

doi:10.1088/1755-1315/796/1/012033

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…In benchmark tests, PGCN outperformed other ML models for all HCV variants (Figure 2A), achieving more than 90% test accuracy for all datasets, including the combined dataset. We evaluated PGCN performance using different metrics besides accuracy, including F1 score, Precision, Recall, Area under curve (AUC), and Average Precision (AP), all standard evaluation metrics for machine learning tasks with imbalanced data [47][48][49][50] . PGCN had the highest F1, Recall, and AP scores of the benchmarked methods (example: 93.53% F1, 90.44% Recall, 96.05% AP for A171T protease using sequence features only) (see details in Table S2).…”

Section: Pgcn Performs Better Than Baseline ML Models For Hcv Proteas...mentioning

confidence: 99%

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network

Lubin

Sarma

et al. 2023

Preprint

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Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key post-translational modification involved in physiology and disease. The ability to robustly and rapidly predict protease substrate specificity would also enable targeted proteolytic cleavage (editing) by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally-derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the three-dimensional structure and energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically-grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases: the NS3/4 protease from the Hepatitis C virus (HCV) and the Tobacco Etch Virus (TEV) proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pre-trained PGCN model to guide the design of TEV protease libraries for cleaving two non-canonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at residue positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

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Section: Pgcn Performs Better Than Baseline ML Models For Hcv Proteas...mentioning

confidence: 99%

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network

Lubin

Sarma

et al. 2023

Preprint

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Prediction of concrete compressive strength employing machine learning techniques

Malhotra

Mishra

Tumrate

2023

Materials Today: Proceedings

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Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning

Huang,

Adityawardhana,

Sanjaya

2023

J. Compos. Sci.

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Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy composites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study introduces a novel use of machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites, providing an innovative and cost-effective alternative to conventional methods. Various regression models, including decision tree regression, random forest regression, extra tree regression, and XGBoost regression, were employed to forecast the yield strength of magnesium alloy composites reinforced with diverse materials. This approach leverages existing research data on matrix type, reinforcement type, heat treatment, and mechanical working. The XGBoost Regression model outperformed the others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis from the best model indicated that the reinforcement particle form had the most significant influence on the mechanical properties. Our research also identified the optimized parameters for achieving the highest yield strength at 186.99 MPa. This study successfully demonstrated the effectiveness of ML as a valuable, novel tool for optimizing the production parameters of magnesium matrix composites.

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Development of Regression Model to Predicting Yield Strength for Different Steel Grades

Cited by 4 publications

References 4 publications

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network

Prediction of concrete compressive strength employing machine learning techniques

Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning

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