AIMC Topic: Neural Networks, Computer

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A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection.

IEEE transactions on neural networks and learning systems
Single-cell RNA sequencing (scRNA-seq) technology is famous for providing a microscopic view to help capture cellular heterogeneity. This characteristic has advanced the field of genomics by enabling the delicate differentiation of cell types. Howeve...

ADGAN: Attribute-Driven Generative Adversarial Network for Synthesis and Multiclass Classification of Pulmonary Nodules.

IEEE transactions on neural networks and learning systems
Lung cancer is the leading cause of cancer-related deaths worldwide. According to the American Cancer Society, early diagnosis of pulmonary nodules in computed tomography (CT) scans can improve the five-year survival rate up to 70% with proper treatm...

Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification.

IEEE transactions on neural networks and learning systems
Early time series classification predicts the class label of a given time series before it is completely observed. In time-critical applications, such as arrhythmia monitoring in ICU, early treatment contributes to the patient's fast recovery, and ea...

A Novel Multi-Scale Graph Neural Network for Metabolic Pathway Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Predicting the metabolic pathway classes of compounds in the human body is an important problem in drug research and development. For this purpose, we propose a Multi-Scale Graph Neural Network framework, named MSGNN. The framework includes a subgrap...

SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated proce...

Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.

Interdisciplinary sciences, computational life sciences
MicroRNA (miRNA) serves as a pivotal regulator of numerous cellular processes, and the identification of miRNA-disease associations (MDAs) is crucial for comprehending complex diseases. Recently, graph neural networks (GNN) have made significant adva...

Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing.

Sensors (Basel, Switzerland)
Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effe...

Quasi-synchronization for variable-order fractional complex dynamical networks with hybrid delay-dependent impulses.

Neural networks : the official journal of the International Neural Network Society
This paper focuses on addressing the problem of quasi-synchronization in heterogeneous variable-order fractional complex dynamical networks (VFCDNs) with hybrid delay-dependent impulses. Firstly, a mathematics model of VFCDNs with short memory is est...

Lie-Poisson Neural Networks (LPNets): Data-based computing of Hamiltonian systems with symmetries.

Neural networks : the official journal of the International Neural Network Society
An accurate data-based prediction of the long-term evolution of Hamiltonian systems requires a network that preserves the appropriate structure under each time step. Every Hamiltonian system contains two essential ingredients: the Poisson bracket and...

A review of graph and complex network theory in water distribution networks: Mathematical foundation, application and prospects.

Water research
Graph theory (GT) and complex network theory play an increasingly important role in the design, operation, and management of water distribution networks (WDNs) and these tasks were originally often heavily dependent on hydraulic models. Facing the ge...