Biological networks
This work focuses on building theoretically driven models of genetic, cellular and protein interactions to understand the role of connectivity in function, regulation and disease. By mapping structural relations across pheno and genotypic features of biological systems, we aim to build entirely new paradigms of biological interactions that will dramatically improve disease prevention strategies.
Featured publications
Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior
Social and asocial learning in zebrafish are encoded by a shared brain network that is differentially modulated by local activation
Improving the generalizability of protein-ligand binding predictions with AI-Bind
Recent publications
Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior
Volume-Optimal Persistence Homological Scaffolds of Hemodynamic Networks Covary with MEG Theta-Alpha Aperiodic Dynamics
A Network-Based Framework to Discover Treatment-Response–Predicting Biomarkers for Complex Diseases
Decoding the Foodome: Molecular Networks Connecting Diet and Health
Featured news coverage
Northeastern network scientist part of international research group selected for $75M One Brave Idea Research Award
News@Northeastern, October 2016 |
Featured project
The Foodome project aims to understand environmental components of coronary heart disease (CHD). The goal is to categorize food ingredients into their chemical constituents in order to identify precise chemical mechanisms that explain how ingested chemicals lead to CHD. The project will take on an immense data collection effort tracking food intake across large populations to capture individualized chemical palettes and determine stability of individuals’ food fingerprint over time. This work will result in the first ever database cataloging an exhaustive list of chemicals that humans consume, which will be used to explore complex relationships between food intake and disease risk.
Associated faculty
Major funders
