EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework

Yu, Lezheng; Zhang, Yonglin; Xue, Li; Liu, Fengjuan; Jing, Runyu; Luo, Jiesi

doi:10.1016/j.csbj.2023.09.036

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…Users can submit one or multiple sequences in FASTA format for prediction by a single click. In particular, as numerous previous studies have indicated that ensemble models are able to achieve significantly improved performance over the original baseline models ( Wang et al 2019 , Xie et al 2021 , Yu et al 2023 , Liu et al 2024 ), the ARTNet models trained on pos_art_346, pos_art_346_random, and pos_whole were used to build an ensemble method. To meet the demands of different users for further interpretation of the prediction results, the web server provides three modes, comprehensive, medium, and strict, to report positive sequences supported by at least one model, at least two models, and all three models, respectively.…”

Section: Resultsmentioning

confidence: 99%

A deep learning method to predict bacterial ADP-ribosyltransferase toxins

Zheng,

Zhou,

Chen

et al. 2024

Bioinformatics

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Motivation ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in training ARTNet. Subsequently, we employed a data optimization strategy by utilizing ART-related domain subsequences instead of the primary full sequences, thereby significantly enhancing the performance of ARTNet. ARTNet achieved a Matthew’s correlation coefficient (MCC) of 0.9351 and an F1-score (macro) of 0.9666 on repeated independent test datasets, outperforming three other deep learning models and six traditional machine learning models in terms of time efficiency and accuracy. Furthermore, we empirically demonstrated the ability of ARTNet to predict novel bARTTs across domain superfamilies without sequence similarity. We anticipate that ARTNet will greatly facilitate the screening and identification of novel bARTTs from bacterial genomes. Availability and implementation ARTNet is publicly accessible at http://www.mgc.ac.cn/ARTNet/. The source code of ARTNet is freely available at https://github.com/zhengdd0422/ARTNet/.

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