AIMC Topic: Neural Networks, Computer

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NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields.

Interdisciplinary sciences, computational life sciences
The process of discovering new drugs related to microbes through traditional biological methods is lengthy and costly. In response to these issues, a new computational model (NRGCNMDA) is proposed to predict microbe-drug associations. First, Node2vec...

Machine learning assisted multi-signal nanozyme sensor array for the antioxidant phenolic compounds intelligent recognition.

Food chemistry
Identifying antioxidant phenolic compounds (APs) in food plays a crucial role in understanding their biological functions and associated health benefits. Here, a bifunctional Cu-1,3,5-benzenetricarboxylic acid (Cu-BTC) nanozyme was successfully prepa...

[Construction of an artificial intelligence-assisted system for auxiliary detection of auricular point features based on the YOLO neural network].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion
OBJECTIVE: To develop an artificial intelligence-assisted system for the automatic detection of the features of common 21 auricular points based on the YOLOv8 neural network.

Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.

Neural networks : the official journal of the International Neural Network Society
Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. Th...

Searching to extrapolate embedding for out-of-graph node representation learning.

Neural networks : the official journal of the International Neural Network Society
Out-of-graph node representation learning aims at learning about newly arrived nodes for a dynamic graph. It has wide applications ranging from community detection, recommendation system to malware detection. Although existing methods can be adapted ...

Graph neural networks in histopathology: Emerging trends and future directions.

Medical image analysis
Histopathological analysis of whole slide images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fail to capture the intricate spatial dependencies inherent i...

Rad4XCNN: A new agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI mode...

SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.

NAR genomics and bioinformatics
Small proteins (≤100 amino acids) play important roles across all life forms, ranging from unicellular bacteria to higher organisms. In this study, we have developed SProtFP which is a machine learning-based method for functional annotation of prokar...

ProPr54 web server: predicting σ promoters and regulon with a hybrid convolutional and recurrent deep neural network.

NAR genomics and bioinformatics
σ serves as an unconventional sigma factor with a distinct mechanism of transcription initiation, which depends on the involvement of a transcription activator. This unique sigma factor σ is indispensable for orchestrating the transcription of genes ...

Sparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.

International journal of computer assisted radiology and surgery
PURPOSE: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We pr...