Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization

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

doi:10.1007/s11517-020-02275-w

Cited by 8 publications

(4 citation statements)

References 40 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%

“…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 ). Convolutional neural networks (CNNs) are the standard model; Liao et al introduced the PSSMs (position-specific scoring matrices) derived from PSI-BLAST ( Altschul et al, 1997 ) for adding evolutionary information to input; Cong et al used the ant-colony optimization for letting the prediction model self-evolving ( Cong et al, 2020 ), which is a trend of deep learning. As another big trend, techniques that have been successfully used in natural language processing have been introduced.…”

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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“…In Liao et al, bidirectional Long-Short-Term Memory (LSTM) and CNN were used to refine amino acid composition sequences and evolution matrices of proteins; next, the outputs from two deep-learning models mentioned previously were concatenated and flattened to one-dimensional classification array [20]. Following that, systems built using representation learning and post-process hybrid classifier algorithms have been created to enhance the bias of positive samples [21,22]. The above sequencebased models were successful in acting on various scenarios and valuable in laying the foundation for subsequent research, while 2D microscope images steadily gain attention due to their objectivity and impressive interpretation.…”

Section: Introductionmentioning

confidence: 99%

Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence

Zou,

Wang,

Wang

et al. 2023

Sensors

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Protein is one of the primary biochemical macromolecular regulators in the compartmental cellular structure, and the subcellular locations of proteins can therefore provide information on the function of subcellular structures and physiological environments. Recently, data-driven systems have been developed to predict the subcellular location of proteins based on protein sequence, immunohistochemistry (IHC) images, or immunofluorescence (IF) images. However, the research on the fusion of multiple protein signals has received little attention. In this study, we developed a dual-signal computational protocol by incorporating IHC images into protein sequences to learn protein subcellular localization. Three major steps can be summarized as follows in this protocol: first, a benchmark database that includes 281 proteins sorted out from 4722 proteins of the Human Protein Atlas (HPA) and Swiss-Prot database, which is involved in the endoplasmic reticulum (ER), Golgi apparatus, cytosol, and nucleoplasm; second, discriminative feature operators were first employed to quantitate protein image-sequence samples that include IHC images and protein sequence; finally, the feature subspace of different protein signals is absorbed to construct multiple sub-classifiers via dimensionality reduction and binary relevance (BR), and multiple confidence derived from multiple sub-classifiers is adopted to decide subcellular location by the centralized voting mechanism at the decision layer. The experimental results indicated that the dual-signal model embedded IHC images and protein sequences outperformed the single-signal models with accuracy, precision, and recall of 75.41%, 80.38%, and 74.38%, respectively. It is enlightening for further research on protein subcellular location prediction under multi-signal fusion of protein.

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Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization

Cited by 8 publications

References 40 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

Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence

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