a Seminar on Network Biology

I recently gave a seminar at Cornell Weill College of Medicien on the the complexity of cancer analysis. This was a highlight talk about how network based analysis is utilized for discoveries in personalized medicine. See related papers here: (Zhang et al., 2024; Durmaz et al., 2017)


Abstract: Unveiling the Complexity of Cancer: From Network Analysis to Personalized Medicine

Network analysis is revolutionizing our understanding of cancer, offering insights into its complex mechanisms and paving the way for personalized medicine. We will explore this multifaceted approach, beginning with its ability to decipher the functional consequences of specific mutations. Through the analysis of interactions within biological networks, we can discover how mutations, such as those in the APC gene in colorectal cancer, trigger cascading effects and interfere with cellular pathways. This understanding of dysregulated pathways forms the foundation for patient stratification. By analyzing network alterations in cancer patients, we can group individuals based on their unique pathway disruptions. Features discovered by frequent subgraph mining offer insights into the underlying disease mechanisms. This approach holds potential for both prognosis prediction and the development of tailored treatment strategies. As an example, we will explain discovering distinct patient groups in low-grade glioma using our unsupervised bottom-up approach. Specific subnetwork alterations both validate our approach and reveal previously unknown subgroups with distinct clinical needs. This exploration of network analysis in cancer research highlights its transformative power in unraveling the complexities of this disease and paving the way for more targeted therapies.

See the Seminar Announcement.

References

2024

  1. nSEA: n-Node Subnetwork Enumeration Algorithm Identifies Lower Grade Glioma Subtypes with Altered Subnetworks and Distinct Prognostics
    Zhihan Zhang, Christiana Wang, Ziyin Zhao, and 4 more authors
    In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024 , 2024

2017

  1. Frequent subgraph mining of personalized signaling pathway networks groups patients with frequently dysregulated disease pathways and predicts prognosis
    Arda Durmaz, Tim AD Henderson, Douglas Brubaker, and 1 more author
    In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017 , 2017