Neural networks : the official journal of the International Neural Network Society
38733793
Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this ...
CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (...
Journal of chemical information and modeling
39197061
Binding of partners and mutations highly affects the conformational dynamics of KRAS4B, which is of significance for deeply understanding its function. Gaussian accelerated molecular dynamics (GaMD) simulations followed by deep learning (DL) and prin...
INTRODUCTION: Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percen...
Neural networks : the official journal of the International Neural Network Society
39293175
Neural Architecture Search (NAS) outperforms handcrafted Neural Network (NN) design. However, current NAS methods generally use hard-coded search spaces, and predefined hierarchical architectures. As a consequence, adapting them to a new problem can ...
Neural networks : the official journal of the International Neural Network Society
39509812
Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The widely adopted objective function of GCL benefits from two key properti...
Deep learning-based automated segmentation of vascular structures in preoperative CT angiography (CTA) images contributes to computer-assisted diagnosis and interventions. While CTA is the common standard, non-contrast CT imaging has the advantage of...
Soil heavy metal pollution in mining areas poses severe challenges to the ecological environment. In recent years, machine learning has been widely used in heavy metal inversion by hyperspectral data. However, deterministic algorithms and probabilist...
Neural networks : the official journal of the International Neural Network Society
39662201
This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering...
Computational representations of knowledge graphs are critical for several tasks in bioinformatics, including large-scale graph analysis and gene function characterization. In this study, we introduce gGN, an unsupervised neural network for learning ...