Abstract Background Not being well controlled by therapy with inhaled corticosteroids and long‐acting β2 agonist bronchodilators is a major concern for severe‐asthma patients. The current treatment option for these patients is the use of biologicals such as anti‐IgE treatment, omalizumab, as an add‐on therapy. Despite the accepted use of omalizumab, patients do not always benefit from it. Therefore, there is a need to identify reliable biomarkers as predictors of omalizumab response. Methods Two novel computational algorithms, machine‐learning based Recursive Ensemble Feature Selection (REFS) and rule‐based algorithm Logic Explainable Networks (LEN), were used on open accessible mRNA expression data from moderate‐to‐severe asthma patients to identify genes as predictors of omalizumab response. Results With REFS, the number of features was reduced from 28,402 genes to 5 genes while obtaining a cross‐validated accuracy of 0.975. The 5 responsiveness predictive genes encode the following proteins: Coiled‐coil domain‐ containing protein 113 ( CCDC113 ), Solute Carrier Family 26 Member 8 ( SLC26A ), Protein Phosphatase 1 Regulatory Subunit 3D ( PPP1R3D ), C‐Type lectin Domain Family 4 member C ( CLEC4C ) and LOC100131780 (not annotated). The LEN algorithm found 4 identical genes with REFS: CCDC113, SLC26A8 PPP1R3D and LOC100131780 . Literature research showed that the 4 identified responsiveness predicting genes are associated with mucosal immunity, cell metabolism, and airway remodeling. Conclusion and clinical relevance Both computational methods show 4 identical genes as predictors of omalizumab response in moderate‐to‐severe asthma patients. The obtained high accuracy indicates that our approach has potential in clinical settings. Future studies in relevant cohort data should validate our computational approach.