About Me
Welcome! I'm Giang Pham, a PhD student in computer science specializing in Computational Biology.
My research focuses on modeling and analyzing biological systems,
with a current emphasis on assessing gene importance within Boolean networks.
Research
My current research explores the application of Shapley values to analyze Boolean networks.
This work aims to measure the importance of internal nodes within these networks relative to a target node,
across diverse environmental conditions represented by different sets of input factors.
Publications
- Gene importance assessment based on shapley value for boolean networks:
Validation and scalability analysis
- In proc. of 12th Int. Symposium “From Data to Models and back (DataMod 2024)”,
Lecture Notes in Computer Science, Springer, in press.
Boolean networks offer a powerful framework for modeling gene interactions,
providing valuable insights into cellular behavior and disease mechanisms.
Identifying key genes is critical for advancing scientific understanding and drug development,
but the complexity of biological networks often requires extensive simulations that are difficult to interpret.
This paper investigates a novel approach for evaluating gene importance within Boolean networks,
using Shapley values from cooperative game theory.
The method quantifies each gene's contribution to overall network behavior, yielding a metric for gene importance.
We demonstrate the method’s effectiveness using a case study on the CD4+ T cell differentiation model,
validating our predictions against established literature.
Additionally, we compare this approach with traditional network analysis metrics to highlight its advantages.
We further evaluate the method’s scalability across six models of different sizes,
concluding that it is particularly well-suited for small to medium-scale networks.
Lastly, we explore potential improvements through the application of network propagation techniques.
- Preliminary results on Shapley value notions and propagation methods for boolean networks
- In proc. of 11th Int. Symposium “From Data to Models and back (DataMod 2023)”,
Lecture Notes in Computer Science, Springer, in press.
The Shapley value is a concept in coalition game theory, with applications extending across various fields, including computational biology.
In this domain, the Shapley value is primarily used to assess the importance of nodes within biological networks.
In this study, we tackle the problem of evaluating the significance of nodes in gene regulatory networks modeled as Boolean networks.
We introduce two game settings for Boolean networks, along with three different notions of the Shapley value.
Furthermore, we present preliminary results on a propagation method that enables the Shapley value to be
extended from one set of nodes to others within the network.
