Danish Aslam

About Me

I am interested in decoding disease biology through the lens of data. My focus is on integrating multiple layers, such as multiomics, medical records, and imaging, to uncover predictive markers and risk scores for complex diseases, including dementia, diabetes, cardiovascular disorders, and autoimmune conditions like rheumatoid arthritis. I am particularly drawn to multimodal data integration and machine learning as ways to address complex biological problems in meaningful and interpretable ways.

At the Supercomputing Facility for Bioinformatics & Computational Biology (SCFBio), Indian Institute of Technology (IIT) Delhi, I work at the intersection of computational biology, biophysical chemistry, and artificial intelligence under the supervision of Prof. B. Jayaram. I am co-developing a Genome Workbench, a framework based on genomic physical fingerprinting to identify and predict regulatory and structural elements such as promoters, enhancers, gene boundaries, and exon–intron junctions in eukaryotes. Earlier, I contributed to a large-scale study that profiled about 4.6 million genomic elements across four kingdoms (Animalia, Plantae, Fungi, Protista), which revealed distinct biophysical signals that can guide eukaryotic genome annotation. As part of my master’s dissertation, I also co-developed ChemEXIN, a tool for predicting exon–intron boundaries using the physicochemical properties of DNA in humans, mice, and worms.

In parallel, I serve as a Bioinformatics Associate at the Mineral Nutrition Laboratory, Division of Plant Physiology, Indian Agricultural Research Institute (IARI) Delhi, in collaboration with Dr. Renu Pandey. My work focuses on the genome-wide identification of Phosphate Starvation Response-1-like binding (P1BS) elements in rice promoters. I developed a custom motif-finding pipeline and combined it with RNA-seq-based expression analysis to study gene regulation under abiotic stresses such as drought, salinity, heat, and cold.

Earlier, at the Computational Intelligence and Bioinformatics lab, I conceptualized and carried out a gene-to-drug molecular dynamics study that identified a small molecule candidate capable of simultaneously targeting multiple key genes (APP, BACE1, APOE, GSK3B, MAPT, PSEN1) implicated in Alzheimer’s disease.

Across all of these projects, one constant has been my relationship with data. I see every dataset as an opportunity to ask new questions, integrate multiple perspectives, and draw biological meaning. I believe no dataset is ever wasted; some simply take longer to reveal their story.

Topics of interest:

• Integrative analysis of multimodal datasets (genomics, transcriptomics, medical records, imaging) for disease risk and biomarker discovery
• Machine learning and deep learning for predictive genomics and functional element classification
• Genome-wide association studies (GWAS) and downstream integration for diagnostic and prognostic marker identification
• Neurogenomics and disease progression, with a focus on dementia, Alzheimer’s disease, and other complex disorders
• Spatial and single-cell omics for decoding gene regulation at cellular resolution
• Data-driven and interpretable research design across biological layers

I graduated with a B.Sc. in Life Science (focus on Botany, Chemistry, Zoology) at the University of Delhi in 2021 and obtained an M.Sc. in Bioinformatics at the Department of Computer Science, Jamia Millia Islamia in 2023.

In my free time, I enjoy reading, writing poetry, and planning my days beforehand.