ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks

Wang, Congjing; Wang, Yifei; Ding, Pengju; Li, Shan; Yu, Xu; Yu, Bin

doi:10.1016/j.compbiomed.2024.107944

Cited by 7 publications

(4 citation statements)

References 70 publications

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“…But knowledge data are limited and only applicable to wellcurated proteins, which limits the predictive power of this kind of method for novel or newly discovered proteins. In recent studies [75][76][77], different kinds of information are fused together for better model performance, given that computational methods excel with high dimensional data as inputs.…”

Section: Knowledge-based Methodsmentioning

confidence: 99%

“…Though the integration strategy is simple, this method can help create various decision-making systems that lead to more robust and accurate predictors. For instance, a multi-view model like ML-FGAT [76] incorporates most of the feature types (e.g., sequence, evolutionary information, physicochemical property, etc. ), which minimizes the perturbation of extraneous data in predictive tasks while concurrently enhancing the descriptive capability.…”

Section: Knowledge-based Ai Approachesmentioning

confidence: 99%

“…Luo et al [69] have also reduced the dimensionality of feature vectors by constructing autoencoders to obtain a better feature representation for downstream analysis. In ML-FGAT [76], the interpretability is strengthened by analyzing the attention weight parameters. Explainable and understandable frameworks will give more reliable predictions that benefit further studies from a biological perspective.…”

Section: Challengesmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Knowledge-based Methodsmentioning

confidence: 99%

Section: Knowledge-based Ai Approachesmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

See 1 more Smart Citation

A Review for Artificial Intelligence Based Protein Subcellular Localization

Xiao,

Zou,

Wang

et al. 2024

Biomolecules

View full text Add to dashboard Cite

show abstract

Section: Knowledge-based Methodsmentioning

confidence: 99%

A Review for Artificial Intelligence Based Protein Subcellular Localization

Xiao,

Zou,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

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 subcel-lular 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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Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

Gillani,

Pollastri

2024

Proteins

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Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular localization. The predictive model used in this article is based on a deep – convolutional architecture. We tested configurations of Deep N‐to‐1 convolutional neural networks of various depths and widths during experimentation for the evaluation of better‐performing values across a diverse set of eight classes. For without alignment assessment, sequences are encoded using one‐hot encoding, converting each character into a numerical representation, which is straightforward for non‐numerical data and useful for machine learning models. For with alignments assessment, multiple sequence alignments (MSAs) are created using PSI‐BLAST, capturing evolutionary information by calculating frequencies of residues and gaps. The average difference in peak performance between models with alignments and without alignments is approximately 15.82%. The average difference in the highest accuracy achieved with alignments compared with without alignments is approximately 15.16%. Thus, extensive experimentation indicates that higher alignment accuracy implies a more reliable model and improved prediction accuracy, which can be trusted to deliver consistent performance across different layers and classes of subcellular localization predictions. This research provides valuable insights into prediction accuracies with and without alignments, offering bioinformaticians an effective tool for better understanding while potentially reducing the need for extensive experimental validations. The source code and datasets are available at http://distilldeep.ucd.ie/SCL8/.

show abstract

ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks

Cited by 7 publications

References 70 publications

A Review for Artificial Intelligence Based Protein Subcellular Localization

A Review for Artificial Intelligence Based Protein Subcellular Localization

A Review for Artificial Intelligence Based Protein Subcellular Localization

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

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