ArticleFrontiers in plant science2018
MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants.
Article in Frontiers in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Performance of stacking machine learning and volume model for improving corn above ground biomass prediction.Plant phenomics (Washington, D.C.) · 2025Article
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- The boundary of life and death: changes in mitochondrial and cytosolic proteomes associated with programmed cell death ofFrontiers in plant science · 2023Article
- Crop Proteomics under Abiotic Stress: From Data to Insights.Plants (Basel, Switzerland) · 2022Review
- Application of machine learning in understanding plant virus pathogenesis: trends and perspectives on emergence, diagnosis, host-virus interplay and management.Virology journal · 2022Review
- Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.Interdisciplinary sciences, computational life sciences · 2021Article
- Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.Plant physiology · 2021Article
- Isolation and comparative proteomic analysis of mitochondria from the pulp of ripening citrus fruit.Horticulture research · 2021Article
- Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.Scientific reports · 2021Article
- Computational methods for protein localization prediction.Computational and structural biotechnology journal · 2021Review
- Applications of UAS in Crop Biomass Monitoring: A Review.Frontiers in plant science · 2021Review
- MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation.Computational and structural biotechnology journal · 2021Article
- Proteomic and Bioinformatic Profiling of Transporters in Higher Plant Mitochondria.Biomolecules · 2020Review
- Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.Journal of integrative bioinformatics · 2020Article
- The subcellular organisation of Saccharomyces cerevisiae.Current opinion in chemical biology · 2019Review
- Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean.Frontiers in genetics · 2019Article
- Article
- Leaf-Movement-Based Growth Prediction Model Using Optical Flow Analysis and Machine Learning in Plant Factory.Frontiers in plant science · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino acid composition, protein position weight matrix, and gene co-expression information, and trained predictors using deep neural network and support vector machine. Benchmarked on two independent datasets, MU-LOC achieved substantial improvements over six state-of-the-art tools for plant mitochondrial targeting prediction. In addition, MU-LOC has the advantage of predicting plant mitochondrial proteins either possessing or lacking N-terminal pre-sequences. We applied MU-LOC to predict candidate mitochondrial proteins for the whole proteome of Arabidopsis and potato. MU-LOC is publicly available at http://mu-loc.org.
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