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HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.
BMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFA quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.
Sci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFSpecific Rosetta-based protein-peptide prediction protocol allows the design of novel cholinesterase inhibitor peptides.
Bioorg Chem
January 2025
Laboratorio de Peptidos Bioactivos, Department of Organic Chemistry, Faculty of Biochemistry and Biological Sciences, National University of the Littoral, Ciudad Universitaria UNL, 3000 Santa Fe, Argentina; National Scientific and Technical Research Council (CONICET), Ministry of Science, Technology and Innovation, Godoy Cruz 2290, Ciudad de Buenos Aires, Argentina. Electronic address:
The search for novel cholinesterase inhibitors is essential for advancing treatments for neurodegenerative disorders such as Alzheimer's disease (AD). In this study, we employed the Rosetta pepspec module, originally developed for designing peptides targeting protein-protein interactions, to design de novo peptides targeting the peripheral aromatic site (PAS) of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). A total of nine peptides were designed for human AChE (hAChE), T.
View Article and Find Full Text PDFIdentification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning.
J Mol Neurosci
January 2025
Lanzhou University Second Hospital, The Second Medical College of Lanzhou University, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the accuracy of early identification, thereby enabling clinicians to intervene promptly and reduce patient disability and mortality rates. Furthermore, the application of predictive biomarkers facilitates the development of personalized treatment plans tailored to the specific conditions of individual patients, optimizing treatment outcomes and improving prognoses.
View Article and Find Full Text PDFRemaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM.
Sensors (Basel)
January 2025
School of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, China.
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain.
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