Biomedical data, computational methods and tools for evaluating disease–disease associations
In recent decades, exploring potential relationships between diseases has been an active
research field. With the rapid accumulation of disease-related biomedical data, a lot of …
research field. With the rapid accumulation of disease-related biomedical data, a lot of …
Partial order relation–based gene ontology embedding improves protein function prediction
Protein annotation has long been a challenging task in computational biology. Gene
Ontology (GO) has become one of the most popular frameworks to describe protein …
Ontology (GO) has become one of the most popular frameworks to describe protein …
[HTML][HTML] Hyperbolic hierarchical knowledge graph embeddings for biological entities
N Li, Z Yang, Y Yang, J Wang, H Lin - Journal of Biomedical Informatics, 2023 - Elsevier
Predicting relationships between biological entities can greatly benefit important biomedical
problems. Previous studies have attempted to represent biological entities and relationships …
problems. Previous studies have attempted to represent biological entities and relationships …
An experimental analysis of graph representation learning for Gene Ontology based protein function prediction
Understanding protein function is crucial for deciphering biological systems and facilitating
various biomedical applications. Computational methods for predicting Gene Ontology …
various biomedical applications. Computational methods for predicting Gene Ontology …
Subcellular location of source proteins improves prediction of neoantigens for immunotherapy
A Castro, S Kaabinejadian, H Yari, W Hildebrand… - The EMBO …, 2022 - embopress.org
Antigen presentation via the major histocompatibility complex (MHC) is essential for anti‐
tumor immunity. However, the rules that determine which tumor‐derived peptides will be …
tumor immunity. However, the rules that determine which tumor‐derived peptides will be …
gGN: Representing the Gene Ontology as low-rank Gaussian distributions
Computational representations of knowledge graphs are critical for several tasks in
bioinformatics, including large-scale graph analysis and gene function characterization. In …
bioinformatics, including large-scale graph analysis and gene function characterization. In …
A Self-Supervised Framework for Learning Biological Entities Representation by Fusing Class Information
N Li, Z Yang, J Wang, H Lin - IEEE Journal of Biomedical and …, 2023 - ieeexplore.ieee.org
Ontologies are widely utilized in the biological domain for data annotation, integration, and
analysis. Some representation learning methods have been proposed to learn the …
analysis. Some representation learning methods have been proposed to learn the …
gGN: learning to represent graph nodes as low-rank Gaussian distributions
Unsupervised learning of node representations from knowledge graphs is critical for
numerous downstream tasks, ranging from large-scale graph analysis to measuring …
numerous downstream tasks, ranging from large-scale graph analysis to measuring …
Prediction of Insufficient Accuracy for Patient's Length of Stay using Feed Forward Neural Network by comparing Deep Belief Network
CV Kumar, MS Saravanan… - 2023 Fifth International …, 2023 - ieeexplore.ieee.org
The research is to study the patient's length of stay in intensive care unit (ICU) admissions
each year with their cost and health expenditure. Forecasting in the clinical Decision …
each year with their cost and health expenditure. Forecasting in the clinical Decision …
Immunogenic potential of neopeptides depends on parent protein subcellular location
A Castro, S Kaabinejadian, W Hildebrand, M Zanetti… - bioRxiv, 2021 - biorxiv.org
Antigen presentation via the major histocompatibility complex (MHC) is essential for anti-
tumor immunity, however the rules that determine what tumor-derived peptides will be …
tumor immunity, however the rules that determine what tumor-derived peptides will be …