ArticleBMC bioinformatics2025
Prediction of drug's anatomical therapeutic chemical (ATC) code by constructing biological profiles of ATC codes.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- A Multimodal Sequence-to-Sequence Model for Automatic Assignment of ATC Codes in Drug Discovery and Repurposing.Journal of chemical information and modeling · 2026Article
- Predicting circRNA subcellular localization by fusing circRNA sequence and network information.Scientific reports · 2026Article
- Root-associated protein prediction using a protein large language model and hypergraph convolutional networks.Scientific reports · 2026Article
- PLysPTM-HGNN: predicting lysine PTM sites of proteins using hybrid graph neural networks.BMC bioinformatics · 2026Article
- Computational and experimental insights into the interaction of the seaweed-derived steroidal metabolite 11α-hydroxyprogesterone with the glucocorticoid receptor.Computational and structural biotechnology journal · 2026Article
- Unveiling Immune Response Mechanisms in Mpox Infection Through Machine Learning Analysis of Time Series Gene Expression Data.Life (Basel, Switzerland) · 2025Article
- Transcriptomic and miRNA Signatures of ChAdOx1 nCoV-19 Vaccine Response Using Machine Learning.Life (Basel, Switzerland) · 2025Article
- Machine Learning Identifies Key Gene Markers Related to Fetal Retina Development at Single-Cell Transcription Level.Investigative ophthalmology & visual science · 2025Article
- Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence.Scientific reports · 2025Article
- Antibiotic Use Thresholds for Carbapenem-Resistant Gram-Negative Bacteria: A Nonlinear Time-Series Study.Infection and drug resistance · 2025Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Anatomical Therapeutic Chemical (ATC) classification system, proposed and maintained by the World Health Organization, is among the most widely used drug classification schemes. Recently, it has become a key research focus in drug repositioning. Computational models often pair drugs with ATC codes to explore drug-ATC code associations. However, the limited information available for ATC codes constrains these models, leaving significant room for improvement.
resultsThis study presents an inference method to identify highly related target proteins, structural features, and side effects for each ATC code, constructing comprehensive biological profiles. Association networks for target proteins, structural features, and side effects are established, and a random walk with restart algorithm is applied to these networks to extract raw associations. A permutation test is then conducted to exclude false positives, yielding robust biological profiles for ATC codes. These profiles are used to construct new ATC code kernels, which are integrated with ATC code kernels from the existing model PDATC-NCPMKL. The recommendation matrix is subsequently generated using the procedures of PDATC-NCPMKL. Cross-validation results demonstrate that the new model achieves AUROC and AUPR values exceeding 0.96.
conclusionThe proposed model outperforms PDATC-NCPMKL and other previous models. Analysis of the contributions of the newly added ATC code kernels confirms the value of biological profiles in enhancing the prediction of drug-ATC code associations.
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