Adil Alsiyabi

This paper develops a 14-protein plasma biomarker panel for prostate cancer diagnosis using machine learning, achieving high accuracy and outperforming existing methods.

This study uses machine learning to identify minimal gene signatures predicting antibiotic resistance in Pseudomonas aeruginosa with high accuracy.

The paper identifies a four-gene panel using machine learning that captures redox, proliferative, and immune pathway dysregulation in breast cancer, achieving high accuracy in tumor-healthy discrimination.

This study investigates lignin degradation mechanisms in Rhodopseudomonas palustris using multi-omics and CRISPRi to identify essential pathways and improve sustainable valorization of lignin.

This study identifies a thermodynamic bottleneck in the TCA cycle of S. aureus that favors acetate overflow metabolism for bioenergetic efficiency.

The paper uses dynamic modeling to analyze sphingolipid biosynthesis in Arabidopsis, identifying key enzymes critical for homeostasis and cell viability.

This paper develops a machine learning framework to predict antimicrobial resistance using transcriptomics data, emphasizing the need for high-quality datasets and feature reduction methods.

This paper reconstructs the first genome-scale metabolic and expression model for Rhodopseudomonas palustris to study anaerobic-photoheterotrophic growth and identify key regulatory mechanisms.

The paper uses computational modeling to predict regulatory roles of ORM proteins in the plant sphingolipid pathway, identifying both repressive and activating effects on key enzymes.

The paper develops a genome-scale metabolic model of Rhodopseudomonas palustris CGA009 to analyze interactions between photosynthesis, CO2 fixation, and the quinone pool, finding that the quinone redox state regulates the CBB pathway.