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Machine and Deep Learning for Prediction of Subcellular Localization.

Gaofeng Pan¹, Chao Sun¹, Zijun Liao^1,2

¹Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.

Methods in Molecular Biology (Clifton, N.J.)

|July 8, 2021

Summary

Protein subcellular localization prediction (PSLP) uses machine learning to identify protein locations in cells. This review covers state-of-the-art methods, introduces a new convolutional neural network approach, and discusses potential challenges.

Keywords:

Deep learning Evolution information Feature extraction Machine learning Multi-label classification Protein sequence Protein subcellular localization prediction

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Area of Science:

Computational biology
Bioinformatics
Proteomics

Background:

Protein subcellular localization prediction (PSLP) is crucial for understanding protein function and cellular processes.
Traditional experimental methods for PSLP are costly and time-consuming.
Machine learning and deep learning have emerged as powerful computational approaches for PSLP.

Purpose of the Study:

To provide a comprehensive overview of machine learning methods for protein subcellular localization prediction.
To introduce a novel prediction method utilizing protein sequences and convolutional neural networks (CNNs).
To highlight potential challenges and limitations associated with the proposed CNN-based method.

Main Methods:

Review of state-of-the-art machine learning algorithms applied to PSLP.
Description of datasets and feature extraction techniques used in PSLP.
Introduction of a simple prediction model employing protein sequences and a CNN classifier.

Main Results:

The review categorizes and summarizes various machine learning techniques for PSLP.
The proposed CNN model demonstrates a straightforward yet effective approach to predicting protein locations.
Analysis of the strengths and weaknesses of the CNN-based PSLP method is presented.

Conclusions:

Machine learning, particularly deep learning with CNNs, offers efficient solutions for PSLP.
The presented CNN method provides a valuable tool for computational biologists.
Further research is needed to address the identified challenges in PSLP.