Semantic similarity and machine learning with ontologies
Ontologies have long been employed in the life sciences to formally represent and reason
over domain knowledge and they are employed in almost every major biological database …
over domain knowledge and they are employed in almost every major biological database …
nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning
Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational
modification and has been reported to be involved in various pathophysiological processes …
modification and has been reported to be involved in various pathophysiological processes …
Owl2vec*: Embedding of owl ontologies
Semantic embedding of knowledge graphs has been widely studied and used for prediction
and statistical analysis tasks across various domains such as Natural Language Processing …
and statistical analysis tasks across various domains such as Natural Language Processing …
Ontoprotein: Protein pretraining with gene ontology embedding
Self-supervised protein language models have proved their effectiveness in learning the
proteins representations. With the increasing computational power, current protein language …
proteins representations. With the increasing computational power, current protein language …
MARPPI: boosting prediction of protein–protein interactions with multi-scale architecture residual network
Protein–protein interactions (PPIs) are a major component of the cellular biochemical
reaction network. Rich sequence information and machine learning techniques reduce the …
reaction network. Rich sequence information and machine learning techniques reduce the …
Contextual semantic embeddings for ontology subsumption prediction
Automating ontology construction and curation is an important but challenging task in
knowledge engineering and artificial intelligence. Prediction by machine learning …
knowledge engineering and artificial intelligence. Prediction by machine learning …
El embeddings: Geometric construction of models for the description logic el++
An embedding is a function that maps entities from one algebraic structure into another
while preserving certain characteristics. Embeddings are being used successfully for …
while preserving certain characteristics. Embeddings are being used successfully for …
TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms
Abstract Motivation Protein–protein interactions (PPIs) play a key role in diverse biological
processes but only a small subset of the interactions has been experimentally identified …
processes but only a small subset of the interactions has been experimentally identified …
Drug target prediction through deep learning functional representation of gene signatures
Many machine learning applications in bioinformatics currently rely on matching gene
identities when analyzing input gene signatures and fail to take advantage of preexisting …
identities when analyzing input gene signatures and fail to take advantage of preexisting …
An integration of deep learning with feature embedding for protein–protein interaction prediction
Protein–protein interactions are closely relevant to protein function and drug discovery.
Hence, accurately identifying protein–protein interactions will help us to understand the …
Hence, accurately identifying protein–protein interactions will help us to understand the …