Protein interaction networks: centrality, modularity, dynamics, and applications
In the post-genomic era, proteomics has achieved significant theoretical and practical
advances with the development of high-throughput technologies. Especially the rapid …
advances with the development of high-throughput technologies. Especially the rapid …
DeepDISOBind: accurate prediction of RNA-, DNA-and protein-binding intrinsically disordered residues with deep multi-task learning
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many
IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported …
IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported …
BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction
Motivation The identification of compound–protein interactions (CPIs) is an essential step in
the process of drug discovery. The experimental determination of CPIs is known for a large …
the process of drug discovery. The experimental determination of CPIs is known for a large …
BridgeDPI: a novel graph neural network for predicting drug–protein interactions
Motivation Exploring drug–protein interactions (DPIs) provides a rapid and precise approach
to assist in laboratory experiments for discovering new drugs. Network-based methods …
to assist in laboratory experiments for discovering new drugs. Network-based methods …
A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches
W Wang, Y Shuai, Q Yang, F Zhang… - Briefings in …, 2024 - academic.oup.com
Proteins play an important role in life activities and are the basic units for performing
functions. Accurately annotating functions to proteins is crucial for understanding the …
functions. Accurately annotating functions to proteins is crucial for understanding the …
Predicting the survival of cancer patients with multimodal graph neural network
In recent years, cancer patients survival prediction holds important significance for
worldwide health problems, and has gained many researchers attention in medical …
worldwide health problems, and has gained many researchers attention in medical …
[HTML][HTML] GOProFormer: a multi-modal transformer method for gene ontology protein function prediction
Protein Language Models (PLMs) are shown to be capable of learning sequence
representations useful for various prediction tasks, from subcellular localization, evolutionary …
representations useful for various prediction tasks, from subcellular localization, evolutionary …
[HTML][HTML] Protein function prediction with gene ontology: from traditional to deep learning models
TTD Vu, J Jung - PeerJ, 2021 - peerj.com
Protein function prediction is a crucial part of genome annotation. Prediction methods have
recently witnessed rapid development, owing to the emergence of high-throughput …
recently witnessed rapid development, owing to the emergence of high-throughput …
A deep learning framework for predicting protein functions with co-occurrence of GO terms
The understanding of protein functions is critical to many biological problems such as the
development of new drugs and new crops. To reduce the huge gap between the increase of …
development of new drugs and new crops. To reduce the huge gap between the increase of …
Accurate prediction of human essential proteins using ensemble deep learning
Essential proteins are considered the foundation of life as they are indispensable for the
survival of living organisms. Computational methods for essential protein discovery provide …
survival of living organisms. Computational methods for essential protein discovery provide …