The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and r...
Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural l...
IEEE journal of biomedical and health informatics
Feb 5, 2024
Medical image segmentation plays an important role in diagnosis. Since the introduction of U-Net, numerous advancements have been implemented to enhance its performance and expand its applicability. The advent of Transformers in computer vision has l...
IEEE journal of biomedical and health informatics
Feb 5, 2024
Cell instance segmentation (CIS) via light microscopy and artificial intelligence (AI) is essential to cell and gene therapy-based health care management, which offers the hope of revolutionary health care. An effective CIS method can help clinicians...
Journal of radiological protection : official journal of the Society for Radiological Protection
Jan 29, 2024
This study assesses the efficacy of Generative Pre-Trained Transformers (GPT) published by OpenAI in the specialised domains of radiological protection and health physics. Utilising a set of 1064 surrogate questions designed to mimic a health physics...
BACKGROUND: Large language models, such as ChatGPT, are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the internet. However, medical texts, such as clinical not...
BACKGROUND: In the field of computational personalized medicine, drug response prediction (DRP) is a critical issue. However, existing studies often characterize drugs as strings, a representation that does not align with the natural description of m...
Learning effective molecular feature representation to facilitate molecular property prediction is of great significance for drug discovery. Recently, there has been a surge of interest in pre-training graph neural networks (GNNs) via self-supervised...
Deep foundation pit settlement prediction based on machine learning is widely used for ensuring the safety of construction, but previous studies are limited to not fully considering the spatial correlation between monitoring points. This paper propos...
Accessing and utilizing geospatial data from various sources is essential for developing scientific research to address complex scientific and societal challenges that require interdisciplinary knowledge. The traditional keyword-based geosearch appro...