ArticleFrontiers in genetics2020
Identification of Protein Subcellular Localization With Network and Functional Embeddings.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 64 citations in OpenAlex.
- Single-Cell Protein Assays in Context: From 2D to 3D and In Situ Analysis.Annual review of analytical chemistry (Palo Alto, Calif.) · 2026Review
- Medium-sized protein language models perform well at transfer learning on realistic datasets.Scientific reports · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.bioRxiv : the preprint server for biology · 2025Article
- Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology.Journal of proteome research · 2024Review
- Article
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- Navigating the human-monkeypox virus interactome: HuPoxNET atlas reveals functional insights.Frontiers in microbiology · 2024Article
- UniKP: a unified framework for the prediction of enzyme kinetic parameters.Nature communications · 2023Article
- A review from biological mapping to computation-based subcellular localization.Molecular therapy. Nucleic acids · 2023Review
- Survey of Protein Sequence Embedding Models.International journal of molecular sciences · 2023Article
- Unannotated Open Reading Frame inmicroPublication biology · 2023Article
- Screening gene signatures for clinical response subtypes of lung transplantation.Molecular genetics and genomics : MGG · 2022Article
- Detecting Blood Methylation Signatures in Response to Childhood Cancer Radiotherapy via Machine Learning Methods.Biology · 2022Article
- Identifying Functions of Proteins in Mice With Functional Embedding Features.Frontiers in genetics · 2022Article
- Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions.BioMed research international · 2022Article
- Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.Frontiers in cell and developmental biology · 2022Article
- Exploring the Genomic Patterns in Human and Mouse Cerebellums Via Single-Cell Sequencing and Machine Learning Method.Frontiers in genetics · 2022Article
- Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects.Computational and mathematical methods in medicine · 2022Article
- Identifying luminal and basal mammary cell specific genes and their expression patterns during pregnancy.PloS one · 2022Article
- Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods.BioMed research international · 2022Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein-protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.