AIMC Topic: Neural Networks, Computer

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MMBERT: a unified framework for biomedical named entity recognition.

Medical & biological engineering & computing
Named entity recognition (NER) is an important task in natural language processing (NLP). In recent years, NER has attracted much attention in the biomedical field. However, due to the lack of biomedical named entity identification datasets, the comp...

Graph neural networks for the identification of novel inhibitors of a small RNA.

SLAS discovery : advancing life sciences R & D
MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzin...

Deep learning based soft-sensor for continuous chlorophyll estimation on decentralized data.

Water research
Monitoring the concentration of pigments like chlorophyll (Chl) in water-bodies is a key task to contribute to their conservation. However, with the existing sensor technology, measurement in real-time and with enough frequency to ensure proper risk ...

Unraveling the complexities of pathological voice through saliency analysis.

Computers in biology and medicine
The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice ...

Convolutional Neural Network for Fully Automated Cerebellar Volumetry in Children in Comparison to Manual Segmentation and Developmental Trajectory of Cerebellar Volumes.

Cerebellum (London, England)
The purpose of this study was to develop a fully automated and reliable volumetry of the cerebellum of children during infancy and childhood using deep learning algorithms in comparison to manual segmentation. In addition, the clinical usefulness of ...

Almost periodic quasi-projective synchronization of delayed fractional-order quaternion-valued neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper examines the issue of almost periodic quasi-projective synchronization of delayed fractional-order quaternion-valued neural networks. First, using a direct method rather than decomposing the fractional quaternion-valued system into four eq...

Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Convolutional neural network (CNN) systems that automatically detect abnormalities from small-bowel capsule endoscopy (SBCE) images are still experimental, and no studies have directly compared the clinical usefulness of different...

Deep graph convolutional network for small-molecule retention time prediction.

Journal of chromatography. A
The retention time (RT) is a crucial source of data for liquid chromatography-mass spectrometry (LCMS). A model that can accurately predict the RT for each molecule would empower filtering candidates with similar spectra but differing RT in LCMS-base...

FAFuse: A Four-Axis Fusion framework of CNN and Transformer for medical image segmentation.

Computers in biology and medicine
Medical image segmentation is crucial for accurate diagnosis and treatment in the medical field. In recent years, convolutional neural networks (CNNs) and Transformers have been frequently adopted as network architectures in medical image segmentatio...

Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network.

BMC medical informatics and decision making
BACKGROUND: Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP), the recognition of nega...