Deep convolutional neural networks with genetic algorithm-based synthetic minority over-sampling technique for improved imbalanced data classification

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 156, P. 111491 - 111491

Published: March 11, 2024

Language: Английский

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Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 5, 2025

Effective light-based cancer treatments, such as photodynamic therapy (PDT) and photoactivated chemotherapy (PACT), rely on compounds that are activated by light efficiently, absorb within the therapeutic window (600–850 nm). Traditional prediction methods for these absorption properties, including Time-Dependent Density Functional Theory (TDDFT), often computationally intensive time-consuming. In this study, we explore a machine learning (ML) approach to predict in region of platinum, iridium, ruthenium, rhodium complexes, aiming at streamlining screening potential photoactivatable prodrugs. By compiling dataset 9775 complexes from Reaxys database, trained six classification models, random forests, support vector machines, neural networks, utilizing various molecular descriptors. Our findings indicate Extreme Gradient Boosting Classifier (XGBC) paired with AtomPairs2D descriptors delivers highest predictive accuracy robustness. This ML-based method significantly accelerates identification suitable compounds, providing valuable tool early-stage design development phototherapy drugs. The also allows change relevant structural characteristics base molecule using information supervised approach. Scientific Contribution: proposed predicts ability transition metal-based UV–vis window, key trait phototherapeutic agents. While ML models have been used properties organic molecules, applying metal is novel. model efficient, fast, resource-light, decision tree-based algorithms provide interpretable results. interpretability helps understand rules facilitates targeted modifications convert inactive into potentially active ones.

Language: Английский

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Psychological Impact on First Responders Dispatched to Out-of-Hospital Cardiac Arrest via Smartphone Alerting System: A Longitudinal Survey-Based Study

Resuscitation Plus, Journal Year: 2025, Volume and Issue: unknown, P. 100941 - 100941

Published: March 1, 2025

Language: Английский

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Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 21, 2025

This research assesses multiple predictive models aimed at estimating disintegration time for pharmaceutical oral formulations, based on a dataset comprising nearly 2,000 data points that include molecular, physical, compositional, and formulation attributes. Drug properties were considered as the inputs to estimate output which is tablet time. Advanced machine learning methods, including Bayesian Ridge Regression (BRR), Relevance Vector Machine (RVM), Sparse Learning (SBL) utilized after comprehensive preprocessing involving outlier detection, normalization, feature selection. Grey Wolf Optimization (GWO) was model optimization obtain optimal combinations of hyper-parameters. Among models, SBL stood out its superior performance, achieving highest R² scores lowest Root Mean Square Error (RMSE) Absolute Percentage (MAPE) error rates in both training testing phases. It also demonstrated robustness effectively avoided overfitting. SHapley Additive exPlanations (SHAP) analysis provided valuable insights into contributions, highlighting wetting sodium saccharin key factors influencing

Language: Английский

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Prediction of quantitative function of artificially-designed protein from structural information

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Abstract Artificially designed proteins are widely used in applications such as optogenetics and biosensing. While experimental optimization of these is effective, it also costly labor-intensive. To address this challenge, computational approaches have been developed, primarily relying on sequence-based features. However, protein function inherently tied to its three-dimensional (3D) structure, incorporating structural information could enable more accurate predictions provide deeper biological interpretability. Here, we proposed a structure-based analysis framework called ‘Foldinsight’ for predicting functionalities. In our framework, first predict structures from sequences using AlphaFold2 then utilize properties. Since vary the number atoms lack direct atomic correspondence, applied molecular field mapping, which captures energy states surrounding converts them into fixed-length numerical vectors. This transformation enables application machine learning, allowing properties be predicted structure-derived Applying channelrhodopsin mutants, achieved predictive performance comparable models. Additionally, successfully identified key regions contributing functional differences, highlighting advantage data modeling.

Language: Английский

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A citizen science platform to sample beehive sounds for monitoring ANSP

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124247 - 124247

Published: Jan. 25, 2025

Language: Английский

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Entropy-based genetic feature engineering and multi-classifier fusion for anomaly detection in vehicle controller area networks

Future Generation Computer Systems, Journal Year: 2025, Volume and Issue: unknown, P. 107779 - 107779

Published: Feb. 1, 2025

Language: Английский

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Automated Detection of Shot Events in Game Phases Using GNSS Data from a Single Team

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 95 - 105

Published: Jan. 1, 2025

Language: Английский

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Developing a Model to Predict Self-Reported Student Performance during Online Education Based on the Acoustic Environment

Sustainability, Journal Year: 2024, Volume and Issue: 16(11), P. 4411 - 4411

Published: May 23, 2024

In recent years, great developments in online university education have been observed, favored by advances ICT. There are numerous studies on the perception of academic performance classes, influenced aspects a very diverse nature; however, acoustic environment students at home, which can certainly affect activities, has barely evaluated. This study assesses influence home students’ self-reported performance. assessment is performed calculating prediction models using Recursive Feature Elimination method with 40 initial features and following classifiers: Random Forest, Gradient Boosting, Support Vector Machine. The optimal number predictors their relative importance were also was assessed metrics such as accuracy area under receiver operating characteristic curve (ROC_AUC-score). model smallest (with 14 predictors, 9 them about perceived environment) best achieves an 0.7794; furthermore, maximum difference for same algorithm between 33 0.03. Consequently, simplicity ease interpretation, reduced variables preferred.

Language: Английский

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Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 369, P. 122279 - 122279

Published: Aug. 31, 2024

Language: Английский

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