ArticleBMC bioinformatics2021
Predicting subcellular location of protein with evolution information and sequence-based deep learning.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Emerging approaches for characterizing spatial and temporal dynamics of pathogen-induced organelle remodeling.Cell systems · 2026Review
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- DEPDC7 as a potential tumor suppressor in hepatocellular carcinoma: preliminary evidence for targeting the JAK1/STAT3 axis.Frontiers in oncology · 2026Article
- Classification of driver and passenger mutations in different cancer types using deep neural networks.Bioinformatics advances · 2026Article
- Comprehensive analysis of the BID gene to uncover the role of novel alternative splicing isoforms in colon adenocarcinoma: a systematic review of literature and bioinformatics analysis.Cancer cell international · 2025Review
- ProteinFormer: protein subcellular localization based on bioimages and modified pre-trained transformer.BMC genomics · 2025Article
- Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions.Proteins · 2025Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell.Bioengineering (Basel, Switzerland) · 2024Article
- Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology.Journal of proteome research · 2024Review
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence.Sensors (Basel, Switzerland) · 2023Article
- Genome-wide identification and characterization of glutathione3 Biotech · 2023Article
- Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.Frontiers in bioinformatics · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
backgroundProtein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins. In order to improve the prediction accuracy, we present a deep learning-based method to predict protein subcellular locations.
resultsOur method utilizes not only amino acid compositions sequence but also evolution matrices of proteins. Our method uses a bidirectional long short-term memory network that processes the entire protein sequence and a convolutional neural network that extracts features from protein sequences. The position specific scoring matrix is used as a supplement to protein sequences. Our method was trained and tested on two benchmark datasets. The experiment results show that our method yields accurate results on the two datasets with an average precision of 0.7901, ranking loss of 0.0758 and coverage of 1.2848.
conclusionThe experiment results show that our method outperforms five methods currently available. According to those experiments, we can see that our method is an acceptable alternative to predict protein subcellular location.
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