Won Min Song, PhD

About Me

Won-Min Song, PhD, an Associate Professor in the Department of Genetics and Genomic Sciences, is a computational biologist versed in integrative multi-omics large-scale data analysis to identify disease mechanisms and therapeutic strategies by leveraging state-of-art network modeling. He has previously established the statistical mechanics of embedded networks in complex manifolds to translate network embedding as an effective modeling of complex real-world networks. This line of research extended to develop a new gene interaction network inference framework, Multiscale Embedded Gene co-Expression Network Analysis (MEGENA), to infer multi-scale interaction networks identifying from loose to compact modules of genes and their key drivers. MEGENA is recognized as a top 10% cited article from PLoS computational biology in 2015 (as of May, 2021). Based on these, data-driven gene regulatory network models integrating mutational, epigenetic, and genomic alterations/regulations have been constructed for complex genetic diseases such as cancers, influenza infection and asthma. These network models have informed key pathways and potential regulators exploited in the disease etiologies, thereby nominate therapeutic strategies targeting the exploited mechanisms. Currently, Dr. Song has extended the integrative network modeling framework to incorporate the cell-level molecular data from single-cell sequencing to dissect the interplay among diverse cells in the disease tissue micro-environment. A recent application to primary melanoma has revealed an intra-tumoral DNA repair pathway, regulated by ZNF180, as a key protumorigenic mechanism that underlies immune-melanoma cases and is associated with poor prognosis.

Lab website: https://labs.icahn.mssm.edu/songlab/

Language
English
Position
ASSOCIATE PROFESSOR | Genetics and Genomic Sciences
Research Topics

Alzheimer's Disease, Bioinformatics, Cancer Genetics, Genomics, Influenza Virus, Systems Biology

Multi-Disciplinary Training Areas

Genetics and Genomic Sciences [GGS]