- Kulkarni S.G., Green A.G., Mann B.C., Malatesta S., Kulkarni Goodwin S., Cesare N., Mulaudzi S., Rawoot N., MIC-ML Consortium, Warren R., Jacobson K.R., and Farhat M.R. Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response. Nature Communications, 2026.
- Mulaudzi S., Kulkarni S., Marin M.G., and Farhat M.R. Benchmarking within-sample minority variant detection with short-read sequencing in M. tuberculosis. bioRxiv, 2026.
- Tasmin M., Mohanty S., Kulkarni S., Farhat M.R., and Green A.G. BIG-TB: A benchmark for evaluating prediction and interpretability of sequence-based machine learning using Mycobacterium tuberculosis genomes. bioRxiv, 2026.
- Wijk M., Denti P., Gausi K., Myers B., Carney T., White L.F., Theron D., Parry C.D.H., Horsburgh C.R., Rawoot N., Warren R.M., Court R., Kulkarni S.G., Farhat M.R., Buys C., Malatesta S., Weber S.E., Kulkarni S., McIlleron H., Jacobson K.R., and Kloprogge F. Insights into the Role of Rifampicin Exposure and Clinical Baseline Covariates on the Response to Pulmonary Tuberculosis Treatment. Clinical Infectious Diseases, 2025.
- Kulkarni S.G., Laurent S., Miotto P., Walker T., Chindelevitch L., Nathanson C-M., Ismail N., Rodwell T., and Farhat M.R. Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis. Nature Communications, 2025.
- Floryanzia S., Ramesh P., Mills M., Kulkarni S., Chen G., Shah P., and Lavrich D. Disintegration testing augmented by computer Vision technology. International Journal of Pharmaceutics, 2022.
