Improving the local diagnostic explanations of diabetes mellitus with the ensemble of label noise filters

Information Fusion, Год журнала: 2025, Номер unknown, С. 102928 - 102928

Опубликована: Янв. 1, 2025

Язык: Английский

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High dimensional mislabeled learning

Neurocomputing, Год журнала: 2024, Номер 573, С. 127218 - 127218

Опубликована: Янв. 5, 2024

Язык: Английский

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Drug-target binding affinity prediction based on power graph and word2vec

BMC Medical Genomics, Год журнала: 2025, Номер 18(S1)

Опубликована: Янв. 13, 2025

Abstract Background Drug and protein targets affect the physiological functions metabolic effects of body through bonding reactions, accurate prediction drug-protein target interactions is crucial for drug development. In order to shorten development cycle reduce costs, machine learning methods are gradually playing an important role in field drug-target interactions. Results Compared with other methods, regression-based affinity more representative binding ability. Accurate can effectively time cost retargeting new this paper, a model (WPGraphDTA) based on power graph word2vec proposed. Conclusions model, molecular features module extracted by neural network, then obtained Word2vec method. After feature fusion, they input into three full connection layers obtain value. We conducted experiments Davis Kiba datasets, experimental results showed that WPGraphDTA exhibited good performance.

Язык: Английский

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Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors

Environmental Research, Год журнала: 2024, Номер 258, С. 119248 - 119248

Опубликована: Май 31, 2024

Язык: Английский

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Bridging odorants and olfactory perception through machine learning: A review

Trends in Food Science & Technology, Год журнала: 2024, Номер 153, С. 104700 - 104700

Опубликована: Сен. 5, 2024

Язык: Английский

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The optimised model of predicting protein‐metal ion ligand binding residues

IET Systems Biology, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 1, 2025

Abstract Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, signal transduction. Predicting the protein‐metal ion ligand binding residues (PMILBRs) accurately is a challenging task theoretical calculations. In this study, authors employed fused amino acids their derived information as feature parameters predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results. Subsequently, deep algorithm was incorporated prediction, resulting improved results for sets of Ca 2+ Mg compared previous studies. The validation matrix provided optimal model each ionic residue, exhibiting capability effectively predicting sites metal real protein chains.

Язык: Английский

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Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

International Journal of Quantum Chemistry, Год журнала: 2025, Номер 125(7)

Опубликована: Март 19, 2025

ABSTRACT Machine learning has revolutionized computational chemistry by improving the accuracy of predicting thermodynamic and kinetic properties like activation energies Gibbs free energies, accelerating materials discovery optimizing reaction conditions in both academic industrial applications. This review investigates recent strides applying advanced machine techniques, including transfer learning, for accurately within complex chemical reactions. It thoroughly provides an extensive overview pivotal methods utilized this domain, sophisticated neural networks, Gaussian processes, symbolic regression. Furthermore, prominently highlights commonly adopted frameworks, such as Chemprop, SchNet, DeepMD, which have consistently demonstrated remarkable exceptional efficiency properties. Moreover, it carefully explores numerous influential studies that notably reported substantial successes, particularly focusing on predictive performance, diverse datasets, innovative model architectures profoundly contributed to enhancing methodologies. Ultimately, clearly underscores transformative potential significantly power intricate systems, bearing considerable implications cutting‐edge theoretical research practical

Язык: Английский

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A novel stochastic configuration network with enhanced feature extraction for industrial process modeling

Neurocomputing, Год журнала: 2024, Номер 594, С. 127833 - 127833

Опубликована: Май 9, 2024

Язык: Английский

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Explainable machine learning for high frequency trading dynamics discovery

Information Sciences, Год журнала: 2024, Номер 684, С. 121286 - 121286

Опубликована: Авг. 3, 2024

Язык: Английский

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Environmental impact evaluation using smart real-time weather monitoring systems: a systematic review

Innovative Infrastructure Solutions, Год журнала: 2024, Номер 10(1)

Опубликована: Дек. 26, 2024

Язык: Английский

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