ArticleMethods in molecular biology (Clifton, N.J.)2021
Machine and Deep Learning for Prediction of Subcellular Localization.
Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.International journal of molecular sciences · 2024Article
- Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.Frontiers in bioinformatics · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein subcellular localization prediction (PSLP), which plays an important role in the field of computational biology, identifies the position and function of proteins in cells without expensive cost and laborious effort. In the past few decades, various methods with different algorithms have been proposed in solving the problem of subcellular localization prediction; machine learning and deep learning constitute a large portion among those proposed methods. In order to provide an overview about those methods, the first part of this article will be a brief review of several state-of-the-art machine learning methods on subcellular localization prediction; then the materials used by subcellular localization prediction is described and a simple prediction method, that takes protein sequences as input and utilizes a convolutional neural network as the classifier, is introduced. At last, a list of notes is provided to indicate the major problems that may occur with this method.
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Registered trials
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