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Semantics

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Optimising window size of semantic of classification model for identification of in-text citations based on context and intent.

PloS one
Citations in scientific literature act as channels for the sharing, transfer, and development of scientific knowledge. However, not all citations hold the same significance. Numerous taxonomies and machine learning models have been developed to analy...

SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.

Journal of bioinformatics and computational biology
Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Curr...

Consistent semantic representation learning for out-of-distribution molecular property prediction.

Briefings in bioinformatics
Invariant molecular representation models provide potential solutions to guarantee accurate prediction of molecular properties under distribution shifts out-of-distribution (OOD) by identifying and leveraging invariant substructures inherent to the m...

Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale ...

Utilizing semantically enhanced self-supervised graph convolution and multi-head attention fusion for herb recommendation.

Artificial intelligence in medicine
Traditional Chinese herbal medicine has long been recognized as an effective natural therapy. Recently, the development of recommendation systems for herbs has garnered widespread academic attention, as these systems significantly impact the applicat...

A semantic enhancement-based multimodal network model for extracting information from evidence lists.

Neural networks : the official journal of the International Neural Network Society
Courts require the extraction of crucial information about various cases from heterogeneous evidence lists for knowledge-driven decision-making. However, traditional manual screening is complex and inaccurate when confronted with massive evidence lis...

Digital evolution: Novo Nordisk's shift to ontology-based data management.

Journal of biomedical semantics
The amount of biomedical data is growing, and managing it is increasingly challenging. While Findable, Accessible, Interoperable and Reusable (FAIR) data principles provide guidance, their adoption has proven difficult, especially in larger enterpris...

A shape composition method for named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Large language models (LLMs) roughly encode a sentence into a dense representation (a vector), which mixes up the semantic expression of all named entities within a sentence. So the decoding process is easily overwhelmed by sentence-specific informat...

Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation.

Journal of chemical information and modeling
The development of new materials is a time-consuming and resource-intensive process. Deep learning has emerged as a promising approach to accelerate this process. However, accurately predicting crystal structures using deep learning remains a signifi...

A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.

Neuroradiology
PURPOSE: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was dev...