Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units

Zhao, Zujin; Gui, Jingyu; Yao, Angela; Le, Nguyen Quoc Khanh; Chua, Matthew Chin Heng

doi:10.1021/acsomega.2c05881

Cited by 23 publications

(5 citation statements)

References 28 publications

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“…The architectures for these methods include convolutional neural networks (CNNs), gated recurrent units (GRUs) and graph neural networks (GNNs) (Table 1). These methods are TOXIFY (26), ToxVec (33), ToxDL (22), ToxinPred2 (23), ATSE (34), ToxIBTL and ToxIBTL Variational Information Bottleneck (ToxIBTL VIB) (35,36). Contemporary Toxin classification models achieve competitive results, though sometimes they are high performing in a limited scope.…”

Section: Contemporary Methodsmentioning

confidence: 99%

Towards a Dataset for State of the Art Protein Toxin Classification

Challacombe,

Haas

2024

Preprint

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In-silico toxin classification assists in industry and academic endeavors and is critical for biosecurity. For instance, proteins and peptides hold promise as therapeutics for a myriad of conditions, and screening these biomolecules for toxicity is a necessary component of synthesis. Additionally, with the expanding scope of biological design tools, improved toxin classification is essential for mitigating dual-use risks. Here, a general toxin classifier that is capable of addressing these demands is developed. Applications forin-silicotoxin classification are discussed, conventional and contemporary methods are reviewed, and criteria defining current needs for general toxin classification are introduced. As contemporary methods and their datasets only partially satisfy these criteria, a comprehensive approach to toxin classification is proposed that consists of training and validating a single sequence classifier, BioLMTox, on an improved dataset that unifies current datasets to align with the criteria. The resulting benchmark dataset eliminates ambiguously labeled sequences and allows for direct comparison against nine previous methods. Using this comprehensive dataset, a simple fine-tuning approach with ESM-2 was employed to train BioLMTox, resulting in accuracy and recall validation metrics of 0.964 and 0.984, respectively. This LLM-based model does not use traditional alignment methods and is capable of identifying toxins of various sequence lengths from multiple domains of life in sub-second time frames.

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Section: Contemporary Methodsmentioning

confidence: 99%

Towards a Dataset for State of the Art Protein Toxin Classification

Challacombe,

Haas

2024

Preprint

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“…They achieved an impressive AUROC of 85.00% in terms of predictive accuracy. In another study examining adverse treatment reactions, Zhao et al [207] created a model to forecast protein and peptide toxicity, incorporating channel attention mechanisms to improve feature extraction and reduce dimensionality. Their approach resulted in high accuracy rates of 97.38% and 95.03%.…”

Section: Treatment Responsementioning

confidence: 99%

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

Cong,

Wang,

Zhou

et al. 2024

Brain-X

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This comprehensive review aims to clarify the growing impact of Transformer‐based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long‐range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence‐centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre‐trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer‐based models in neuroscience, neurology, and psychiatry.

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“…Transformer-based language models can be divided into two main groups: those that are trained from scratch and those that are pretrained [18]. A scratch-trained model is a transformer model explicitly trained for a particular NLP task without using any pre-existing knowledge from other tasks or datasets [29][30][31]. The scratch-trained model is trained on a specific task-specific dataset, which is generally smaller than the pre-training datasets used for pre-trained models.…”

Section: Significance Of the Studymentioning

confidence: 99%

Leveraging transformers‐based language models in proteome bioinformatics

2023

Proteomics

Self Cite

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In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer‐based NLP models have gained significant attention for their ability to process variable‐length input sequences in parallel, using self‐attention mechanisms to capture long‐range dependencies. In this review paper, we discuss the recent advancements in transformer‐based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer‐based NLP models to revolutionize proteome bioinformatics.

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Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units

Cited by 23 publications

References 28 publications

Towards a Dataset for State of the Art Protein Toxin Classification

Towards a Dataset for State of the Art Protein Toxin Classification

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

Leveraging transformers‐based language models in proteome bioinformatics

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