International journal of biological macromolecules
Jan 21, 2025
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental metho...
We present a built-in physics neural network architecture, known as inelastic Constitutive Artificial Neural Network (iCANN), to discover the inelastic phenomenon of tensional homeostasis. In this course, identifying the optimal model and material pa...
BACKGROUND: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to e...
Selenium (Se) is an indispensable trace element to human health, yet its biological tolerance threshold is relatively narrow. The potential application of machine learning methods to indirectly predict the Se content in crops across regional areas, t...
Journal of chemical information and modeling
Jan 21, 2025
Skin corrosion assessment is an essential toxicity end point that addresses safety concerns for topical dosage forms and cosmetic products. Previously, skin corrosion assessments required animal testing; however, differences in skin architecture and ...
The most deadly type of skin cancer is melanoma. A visual examination does not provide an accurate diagnosis of melanoma during its early to middle stages. Therefore, an automated model could be developed that assists with early skin cancer detection...
Cancer imaging : the official publication of the International Cancer Imaging Society
Jan 21, 2025
BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significa...
BACKGROUND: Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take info...
Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has pro...
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was s...
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