Sara Iftikhar

Sara Iftikhar

Researcher in microbial genomics, antimicrobial resistance, and machine learning

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Posts

publications

Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models

Published in Journal of Environmental Management, 2023

Machine learning and explainable AI identify hydro-meteorological drivers of antibiotic-resistance genes at recreational beaches.

Recommended citation: Iftikhar, S., Karim, A. M., Karim, A. M., Karim, M. A., Aslam, M., Rubab, F., ... & Yasir, M. (2023). Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models. Journal of Environmental Management, 328, 116969. https://doi.org/10.1016/j.jenvman.2022.116969

Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials

Published in Separation and Purification Technology, 2023

Artificial neural networks and explainable AI predict the adsorption capacity of industrial dyes on carbon-based materials.

Recommended citation: Sara Iftikhar, Nallain Zahra, Fazila Rubab, Raazia Abrar Sumra, Muhammad Burhan Khan, Ather Abbas, Zeeshan Haider Jaffari, Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials, Separation and Purification Technology, Volume 326, 2023 https://doi.org/10.1016/j.seppur.2023.124891

Adsorption of Cr(VI) ions onto fluorine-free niobium carbide (MXene) and machine learning prediction with high precision

Published in Journal of Environmental Chemical Engineering, 2024

Fluorine-free niobium carbide MXene removes hexavalent chromium from wastewater, with CatBoost reproducing the measured adsorption behavior.

Recommended citation: Rehan Ishtiaq, Nallain Zahra, Sara Iftikhar, Fazila Rubab, Khawar Sultan, Ather Abbas, Sze-Mun Lam, Zeeshan Haider Jaffari, Ki Young Park, Adsorption of Cr(VI) ions onto fluorine-free niobium carbide (MXene) and machine learning prediction with high precision, Journal of Environmental Chemical Engineering, Volume 12, Issue 2, 2024, 112238, ISSN 2213-3437, https://doi.org/10.1016/j.jece.2024.112238. (https://www.sciencedirect.com/science/article/pii/S2213343724003683) https://doi.org/10.1016/j.jece.2024.112238

AquaFetch: A unified Python interface for water resource dataset acquisition and harmonization

Published in Journal of Open Source Software, 2025

A Python package for acquiring and harmonizing diverse water-resource datasets through a consistent interface.

Recommended citation: Abbas, A., Iftikhar, S., & Beck, H. E. (2025). AquaFetch: A unified Python interface for water resource dataset acquisition and harmonization. Journal of Open Source Software, 10(112), 8051. https://doi.org/10.21105/joss.08051

AquaFetch: A unified Python interface for water resource dataset acquisition and harmonization

Published in EGU General Assembly Conference Abstracts, 2025

Conference contribution presenting the AquaFetch data-acquisition and harmonization package.

Recommended citation: Iftikhar, S., Abbas, A., & Beck, H. (2025). AquaFetch: A unified Python interface for water resource dataset acquisition and harmonization. EGU General Assembly Conference Abstracts, EGU25-19958. https://doi.org/10.5194/egusphere-egu25-19958

Methicillin-resistant Staphylococcus aureus in Saudi Arabia: genomic evidence of recent clonal expansion and plasmid-driven resistance dissemination

Published in Frontiers in Microbiology, 2025

A national population-genomic study of MRSA diversity, clonal expansion, and plasmid-mediated resistance in Saudi Arabia.

Recommended citation: Alhejaili, A. Y., Zhou, G., Halawa, H., Huang, J., Fallatah, O., Hirayban, R., Iftikhar, S., et al., Moradigaravand, D., & Al Salem, W. (2025). Methicillin-resistant Staphylococcus aureus in Saudi Arabia: genomic evidence of recent clonal expansion and plasmid-driven resistance dissemination. Frontiers in Microbiology, 16, 1602985. https://doi.org/10.3389/fmicb.2025.1602985

Probabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment

Published in Chemosphere, 2025

Probabilistic machine-learning models predict phosphate adsorption by biochar and quantify prediction uncertainty.

Recommended citation: Iftikhar, S., Ishtiaq, R., Zahra, N., Rubab, F., Lam, S.-M., Abbas, A., & Jaffari, Z. H. (2025). Probabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment. Chemosphere, 370, 144031. https://doi.org/10.1016/j.chemosphere.2024.144031

Genomic signatures and prediction of clinical severity in Klebsiella pneumoniae infections in a multicenter cohort

Published in medRxiv (preprint), 2026

A multicenter study integrating bacterial genomic and clinical data to predict mortality, ICU admission, and length of stay.

Recommended citation: Malaikah, M., Alyami, R. Y., Huang, J., Fallatah, O., Milner, M., Zhou, G., Hirayban, R., Iftikhar, S., et al., & Moradigaravand, D. (2026). Genomic signatures and prediction of clinical severity in Klebsiella pneumoniae infections in a multicenter cohort. medRxiv. https://doi.org/10.64898/2026.02.02.26345332

High-dimensional characterization of genome-environment fitness landscapes in Klebsiella pneumoniae

Published in medRxiv (preprint), 2026

Chemical genomics, bacterial GWAS, and machine learning reveal environment-dependent genetic determinants of bacterial fitness.

Recommended citation: Zhou, G., Williams, G., Millner, M. T., AlHirayban, R., Alosaimi, W., Fallatah, O., Hart, A. J., Malaikah, M., Iftikhar, S., et al., & Moradigaravand, D. (2026). High-dimensional characterization of genome-environment fitness landscapes in Klebsiella pneumoniae. medRxiv. https://doi.org/10.64898/2026.05.28.26354339

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