Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites

16 Oct 2023

Abstract excerpt

MOTIVATION: The rapid and extensive transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global health emergency, affecting millions of people and causing an immense socioeconomic impact. The identification of SARS-CoV-2 phosphorylation sites plays an important role in unraveling the complex molecular mechanisms behind infection and the resulting alterations...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Machine Learning in Bioinformatics
Share to SARS-CoV-2 and COVID-19 Research

Source

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Identifiers

PMID: 37847658
DOI: 10.1093/bioinformatics/btad627

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sitesPMID 37847658Read ↗

DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approachSemantic proximity Read ↗

Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer DevelopmentSemantic proximity Read ↗

DE-MHAIPs: Identification of SARS-CoV-2 phosphorylation sites based on differential evolution multi-feature learning and multi-head attention mechanismSemantic proximity Read ↗

Deciphering signaling specificity with interpretable deep neural networksSemantic proximity Read ↗

Machine Learning and Deep Learning Frameworks for Human–Virus Protein–Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and ChallengesSemantic proximity Read ↗

SDNN-PPI: Self-attention with deep neural networks effect on protein-protein interaction predictionSemantic proximity Read ↗

In vitro Kinase-to-Phosphosite database (iKiP-DB) predicts kinase activity in phosphoproteomic datasetsSemantic proximity Read ↗

CoPhosK: A Method for Comprehensive Kinase Substrate Annotation Using Co-phosphorylation AnalysisSemantic proximity Read ↗

Computational analysis of phosphoproteomics: progresses and perspectives.Semantic proximity Read ↗

Inferring Disease Associated Phosphorylation Sites via Random Walk on Multi-Layer Heterogeneous Network.Semantic proximity Read ↗

EPSD 2.0: An Updated Database of Protein Phosphorylation Sites across Eukaryotic SpeciesSemantic proximity Read ↗

Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention–based neural networksSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.