Fadhl Alakwaa

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

This study identifies a MEF2C transcription factor network that regulates endothelial cell proliferation in diabetic kidney disease and shows that SGLT2i treatment can reverse its dysregulation.

This study shows dapagliflozin reduces hypoxia-induced metabolic stress, cell death, and fibrosis in kidney organoids derived from human pluripotent stem cells.

The paper presents a multi-modal atlas of human kidney disease, identifying cellular and spatial mechanisms of injury resolution or failure and linking them to clinical outcomes.

The paper uses ontologies to integrate clinical, pathological, and molecular kidney data for precision medicine analysis.

The paper defines the chromatin landscape of healthy and injured kidney cells using multi-omics data, identifying regulatory elements and transcription factor networks involved in injury response.

The paper identifies metabolites in umbilical cord blood linked to maternal pre-pregnant obesity using metabolomics in a multi-ethnic cohort.

Lilikoi is an R package that enables personalized pathway-based classification using metabolomics data through four modules for mapping, transformation, selection, and classification.

This study compares deep learning and machine learning methods for classifying estrogen receptor status in breast cancer metabolomics data, finding deep learning achieves higher accuracy.