Here is the list of our current and past projects

1- MLA project:

*Looking for the prognostic factors in AML using Machine Learning methods More information

2- LncRNA Cancer Project:

*Regulatory networks of lncRNAs in Breast cancer More information

3- Oncofactors Survival Analysis:

*Survival analysis for the oncofactors in TCGA using clinical and gene expression data More information

4- miRNA_Drugs Interactome:

*Interactions between anticancer drugs and miRNAs/lncRNAs in breast cancer More Information

5- cellular aging and Cellular senescence:

*We are looking at the interaction networks dynamics during cellular aging and senescence More Information

6- Lipids and Protein metabolism in diabetes and cancer:

*In these projects, we are looking at the metabolism-related gene regulatory networks in various diseases, including breast cancer, diabetes More Information

Future projects (lab’s new directions):

1. Multimodal Omics Integration for Aging Biomarkers

Expand beyond transcriptomics by incorporating epigenomics, proteomics, and metabolomics data. The aim is to employ deep learning to identify predictive aging signatures across tissues and disease states.

2. Single-Cell AI for Senescence Microenvironment Mapping

Integrate single-cell RNA-seq and spatial transcriptomics to characterize cellular aging. Applying AI-based clustering and trajectory inference to uncover senescence-associated cell states in cancer and neurodegeneration.

3. AI-integrated into Drug Repurposing approaches for Age-Related Diseases

Using knowledge generative AI to predict senolytic compounds or rejuvenation therapeutics. To create a pipeline to maximize the advantage of public compound libraries and omics perturbation databases.

4. Longitudinal Aging Cohort Modeling

Using European biobank datasets to build longitudinal machine learning models for aging trajectories. The aim is to make predictive tools relevant to precision geriatrics, especially in oncology and chronic inflammation.

5. “Digital Twin” Models of Cellular Aging

Develop computational “digital twins” of cells or organ systems using simulations of aging dynamics. Merging molecular expertise with mechanistic modeling to test intervention hypotheses in silico.

6. Neurodegenerative Aging Signatures

Diving deeper into aging mechanisms in the brain—associated transcriptomic dysregulation with cognitive decline. Integrating AI tools to bridge molecular patterns with imaging, cognitive scores, and neuroinflammation biomarkers