Albert-László Barabási

Northeastern University
Country flagUnited States

Research Interests

Explore related searches

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

Articles (11)

Year:

2023

Understanding natural and traditional medicine can lead to world-changing drug discoveries. Despite the therapeutic effectiveness of individual herbs, traditional Chinese medicine (TCM) lacks a scientific foundation and is often considered a myth. In this study, we establish a network medicine framework and reveal the general TCM treatment principle as the topological relationship between disease symptoms and TCM herb targets on the human protein interactome. We find that proteins associated with a symptom form a network module, and the network proximity of an herb’s targets to a symptom module is predictive of the herb’s effectiveness in treating the symptom. These findings are validated using patient data from a hospital. We highlight the translational value of our framework by predicting herb-symptom treatments with therapeutic potential. Our network medicine framework reveals the scientific foundation of TCM and establishes a paradigm for understanding the molecular basis of natural medicine and predicting disease treatments.

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Collaborators (7)

Sofia Moco

University of Amsterdam

NETHERLANDS
View details

Xiaobo Zhou

Associate Professor

Brigham and Women's Hospital

UNITED STATES
View details

Xiangyi Meng

Assistant Professor

Rensselaer Polytechnic Institute

UNITED STATES
View details

Istvan Kovacs

Assistant Professor

Northwestern University

UNITED STATES
View details

Reka Albert

Distinguished Professor

Pennsylvania State University

UNITED STATES
View details

Katalin Eszter Müller

Semmelweis University

HUNGARY
View details

Alexander Gates

Assistant Professor

University of Virginia

UNITED STATES
View details
ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.