ReviewBiomolecules2024
A Review for Artificial Intelligence Based Protein Subcellular Localization.
Review in Biomolecules, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Artificial Intelligence as a Discovery Engine for Routine Molecular Techniques: Extracting Biological Insight from Western Blotting, ELISA, Immunostaining, and Immunoprecipitation.Cell biochemistry and biophysics · 2026Review
- Discovery and design of potent cell surface display elements.Nature biotechnology · 2026Article
- AlphaGEM enables precise genome-scale metabolic modelling by integrating protein structure alignment with deep-learning-based dark metabolism mining.Nature communications · 2026Article
- BioGraphX: bridging the sequence-structure gap via physicochemical graph encoding for interpretable subcellular localization prediction.Bioinformatics advances · 2026Article
- Genome-wide identification, characterization, and evolutionary analysis of the HSP70 gene family in rice (Oryza sativa L.).BMC plant biology · 2025Article
- ProteinFormer: protein subcellular localization based on bioimages and modified pre-trained transformer.BMC genomics · 2025Article
- Comprehensive analysis of the NAC transcription factor gene family in Sophora tonkinensis Gagnep.BMC plant biology · 2025Article
- Article
- Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions.Proteins · 2025Article
- Construction of AMPK-related circRNA network in mouse myocardial ischemia-reperfusion injury model.BMC cardiovascular disorders · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.