AIMC Topic: Algorithms

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DeepPrep: an accelerated, scalable and robust pipeline for neuroimaging preprocessing empowered by deep learning.

Nature methods
Neuroimaging has entered the era of big data. However, the advancement of preprocessing pipelines falls behind the rapid expansion of data volume, causing substantial computational challenges. Here we present DeepPrep, a pipeline empowered by deep le...

Prediction of circRNA-Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor.

Biomolecules
Circular RNAs (circRNAs) have attracted increasing attention for their roles in human diseases, making the prediction of circRNA-disease associations (CDAs) a critical research area for advancing disease diagnosis and treatment. However, traditional ...

Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets.

Scientific reports
Diabetes Mellitus (DM) is a global health challenge, and accurate early detection is critical for effective management. The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PI...

Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.

Scientific reports
Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations in existing diagnostic tests. Machine learning (ML) offers a promising avenue for addressing these challenges by leveraging demographic and clinical da...

Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.

European radiology experimental
BACKGROUND: Gadolinium-enhanced "sampling perfection with application-optimized contrasts using different flip angle evolution" (SPACE) sequence allows better visualization of brain metastases (BMs) compared to "magnetization-prepared rapid acquisiti...

Using near-infrared hyperspectral imaging combined with machine learning to predict the components and the origin of Radix Paeoniae Rubra.

Analytical methods : advancing methods and applications
The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content...

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.

IEEE transactions on neural networks and learning systems
The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is ...

CDNA-SNN: A New Spiking Neural Network for Pattern Classification Using Neuronal Assemblies.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) mimic their biological counterparts more closely than their predecessors and are considered the third generation of artificial neural networks. It has been proven that networks of spiking neurons have a higher computati...

Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes.

IEEE transactions on neural networks and learning systems
Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust r...

Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition.

IEEE transactions on neural networks and learning systems
Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same...