Computational Biology in Genomic Research

Computational biology is central to modern genetics and genomics. The increasing scale and complexity of high-dimensional data - generated by advanced sequencing, molecular profiling, and clinical systems - require powerful analytical approaches to extract meaningful insights. Using cutting-edge computational tools, researchers can now analyze multi-omic and health data to identify genetic risk factors, predict therapeutic responses, and inform the development of precision treatments.

At the Department of Genetics and Genomic Sciences (GGS) at the Icahn School of Medicine at Mount Sinai, we leverage computational methods to uncover the molecular underpinnings of disease and accelerate the discovery of new therapeutic strategies. Our team includes experts in biostatistics, machine learning, data science, and software engineering, all working to develop innovative solutions for large-scale genomic analysis.

Key areas of focus include:

  • Algorithm development for disease risk prediction using genetic and clinical/EHR data.
  • Functional annotation of genetic variants (e.g., SNPs/SNVs) and genes.
  • Integrative multi-omics analysis for improving diagnosis, prognosis, and treatment response prediction.
  • Drug discovery informatics, including computational tools to predict drug response, toxicity, and identify novel therapeutic targets.
  • Modeling gene and protein regulatory networks to understand cellular behavior in development and disease.

Our work has contributed to transformative advances in both research and clinical care. By developing new computational approaches and applying them to complex biological questions, we aim to unlock the potential of genomic and multi-omic data to improve human health. Through this work, we are helping to lay the foundation for more personalized, predictive, and effective healthcare.

Genetics and Genomic Sciences Faculty