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MOESM1 of Identification of infectious disease-associated host genes using machine learning techniques

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NIAID Data Ecosystem2026-03-11 收录
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https://figshare.com/articles/dataset/MOESM1_of_Identification_of_infectious_disease-associated_host_genes_using_machine_learning_techniques/11470902
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Additional file 1: Table S1. All the curated infectious diseases-associated human genes from DisGeNET. Table S2. All the mapped gene name to uniprot id using mapping table of DisGeNET. Table S3. Positive dataset for 10-fold cross-validation. Table S4. Positive blind dataset (not used in training or testing of 10-fold cross-validation techniques for developing the prediction model). Table S5. All the disease-associated human reviewed proteins in DisGeNET. Table S6. All the reviewed human proteins collected from UniProtKB dated 12/01/2018. Table S7. All the reviewed human proteins not associated with any diseases. Table S8. Negative dataset for 10-fold cross-validation. Table S9. Negative blind dataset (not used in training or testing of 10-fold cross-validation techniques for developing the prediction model). Table S10. Independent dataset (Befree text mining genes from DisGeNET associated with infectious diseases). Table S11. All human protein-protein interactions (PPIs) from Human Protein Reference Database (HPRD) (Release 9). Table S12. All unique human in HPRD (Release 9). Table S13. All the mapped human protein-protein interactions (PPIs) in uniprot id format. Table S14. All the mapped unique human proteins in uniprot. Table S15. 9 topological properties of protein-protein interaction networks using HPRD PPIs dataset. Table S16. Features wise performance measures on disease and non-disease associated proteins dataset using deep neural network classifier. Table S17. Mixed features based performance on disease and non-disease associated proteins dataset. Table S18. 10 selected features for normalized and filtered PAAC and Network properties. Table S19. 16 selected features for PAAC and Network properties. Table S20. Selected features wise performance measures using different classifier. Table S21. Prediction result on independent dataset. Table S22. Top 100 proteins (genes) are predicted by our proposed DNN based method. Table S23. Significantly enriched disease-ontology terms for top 100 proteins (genes) based on Genetic Association Database (GAD). Table S24. Significantly enriched gene-ontology biological process terms for top 100 proteins (genes).
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2019-12-27
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