Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism

Cong, Hanhan; Hong, Lu; Cao, Yi; Chen, Yuehui; Liang, Cheng

doi:10.1007/s12539-021-00496-7

Cited by 9 publications

(5 citation statements)

References 77 publications

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“…As noted above, the prediction of protein subcellular localization has always been a playground where the latest machine learning algorithms are introduced. In recent years, deep learning-based methods have become quite popular and thus a number of papers have been published within a few years ( Cong et al, 2020 , 2022 ; Semwal & Varadwaj, 2020 ; Jiang, Wang, Yao, et al, 2021 ; Liao et al, 2021 ; Yuan et al, 2021 ). The architecture of deep learning models has made rapid progress and they have also been applied to bioinformatics, such as protein design ( Ding et al, 2022 ).…”

Section: Deep Learning and Language Model-based Methodsmentioning

confidence: 99%

“…One of them is the use of (multi-head) self-attention mechanism, which was first introduced in Transformer (reviewed in Shreyashree et al, 2022 ). Both Jiang et al and Cong et al report the improvement of prediction performance with the use of the self-attention mechanism ( Jiang, Wang, Yao, et al, 2021 ; Cong et al, 2022 ). Jiang et al also claim that their method shows better performance in suborganellar prediction (see below).…”

Section: Deep Learning and Language Model-based Methodsmentioning

confidence: 99%

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Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Nakai

Wei

2022

Front. Bioinform.

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Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

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Section: Deep Learning and Language Model-based Methodsmentioning

confidence: 99%

Section: Deep Learning and Language Model-based Methodsmentioning

confidence: 99%

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Nakai

Wei

2022

Front. Bioinform.

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“…Mining deeper, Kaleel et al [53] ensemble Deep N-to-1 Convolutional Neural Networks that predict the location of the endomembrane system and secretory pathway versus all others and outperform many state-of-the-art web servers. Cong et al [54] proposed a self-evolving deep convolutional neural network (DCNN) protocol to solve the difficulties in feature correlation between sites and avoid the impact of unknown data distribution while using the self-attention mechanism [55] and a customized loss function to ensure the model performance. In addition, a long short-term memory network (LSTM) which combines the previous states and current inputs is also commonly used [56,57], with Generative Adversarial Network (GAN) [58] and Synthetic Minority Over-sampling Technique (SMOTE) [59] used for synthesizing minority samples to deal with data imbalance.…”

Section: Sequences-based Ai Approachesmentioning

confidence: 99%

A Review for Artificial Intelligence Based Protein Subcellular Localization

Xiao,

Zou,

Wang

et al. 2024

Biomolecules

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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.

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“…An attention mechanism can be defined as a function that allows the model to focus on important information only, e.g., finding amino acids with higher attention weights, or focusing on attention weights of each function considered. Similarly, MPSLP [23] also uses a Self-Attention Mechanism with Deep Convolutional Neural Networks. This predictor integrates features through amino acid index distribution and physicochemical properties.…”

Section: Literature Reviewmentioning

confidence: 99%

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks

Gillani,

Pollastri

2024

IJMS

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The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein’s function. It can potentially provide information about protein–protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.

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Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism

Cited by 9 publications

References 77 publications

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

A Review for Artificial Intelligence Based Protein Subcellular Localization

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks

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