Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37847658", "kind": "science-record", "revision": 3, "content_hash": "sha256:7013e9f0dc3c1f6005bb1a46f2f712d543cc2521161f867b7b0ad7b3394cebab", "created_at": "2026-11-01T19:02:42Z", "updated_at": "2026-11-01T22:10:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites.", "doi": "10.1093/bioinformatics/btad627", "pmid": "37847658", "authors": ["Shihu Jiao", "Xiucai Ye", "Chunyan Ao", "Tetsuya Sakurai", "Quan Zou", "Lei Xu"], "journal": "Bioinformatics (Oxford, England)", "year": 2023, "abstract": "MOTIVATION: The rapid and extensive transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global health emergency, affecting millions of people and causing an immense socioeconomic impact. The identification of SARS-CoV-2 phosphorylation sites plays an important role in unraveling the complex molecular mechanisms behind infection and the resulting alterations in host cell pathways. However, currently available prediction tools for identifying these sites lack accuracy and efficiency.\nRESULTS: In this study, we presented a comprehensive biological function analysis of SARS-CoV-2 infection in a clonal human lung epithelial A549 cell, revealing dramatic changes in protein phosphorylation pathways in host cells. Moreover, a novel deep learning predictor called PSPred-ALE is specifically designed to identify phosphorylation sites in human host cells that are infected with SARS-CoV-2. The key idea of PSPred-ALE lies in the use of a self-adaptive learning embedding algorithm, which enables the automatic extraction of context sequential features from protein sequences. In addition, the tool uses multihead attention module that enables the capturing of global information, further improving the accuracy of predictions. Comparative analysis of features demonstrated that the self-adaptive learning embedding features are superior to hand-crafted statistical features in capturing discriminative sequence information. Benchmarking comparison shows that PSPred-ALE outperforms the state-of-the-art prediction tools and achieves robust performance. Therefore, the proposed model can effectively identify phosphorylation sites assistant the biomedical scientists in understanding the mechanism of phosphorylation in SARS-CoV-2 infection.\nAVAILABILITY AND IMPLEMENTATION: PSPred-ALE is available at https://github.com/jiaoshihu/PSPred-ALE and Zenodo (https://doi.org/10.5281/zenodo.8330277).", "claims": [{"id": "claim-data-access", "statement": "Supplementary data are available at Bioinformatics online.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37847658", "locator": "sections[18] \"Supplementary data\" paragraph 2", "quote": "Supplementary data are available at Bioinformatics online."}], "state": "complete"}, {"id": "claim-target-result", "statement": "PSPred-ALE outperformed the three other predictors on the independent test dataset, with ACC, AUC, and MCC approximately 2.1%–2.9%, 1.3%–3.1%, and 2.9%–5.7% higher, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37847658", "locator": "sections[10] \"3.2 Performance comparison of PSPred-ALE with existing predictors\" paragraph 5", "quote": "Specifically, the ACC, AUC, and MCC of our model is about 2.1%–2.9%, 1.3%–3.1%, and 2.9%–5.7% higher than those of other three predictors, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "PSPred-ALE outperformed the three other predictors on the independent test dataset, with ACC, AUC, and MCC approximately 2.1%–2.9%, 1.3%–3.1%, and 2.9%–5.7% higher, respectively.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37847658"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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