Computational Cancer Genomics Group

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Post-Doctoral Fellows

  • Seokjin Ham
  • A-Reum Nam

Graduate Students

  • Jaejun Lee
  • Adrián Maqueda
  • Manuel Moradiellos

Through big data analysis on cancer patients, we aim to address a fundamental question:
do identical genes or mutations exhibit the same effects in different cellular contexts
? The optimal fitness level of a cancer gene can vary based on the context, and to deepen our understanding, we apply quantitative methods to explore it. First, we investigate how the fitness landscape shifts between primary and metastatic cancers by analysing the combination of alterations. Next, we seek to unravel how the position of mutations affects protein interaction partners and how these perturbations impact phenotypic outcomes. For this, we build a model that leverages state-of-the-art AI-based structural predictions and multi-omics data. Through this interdisciplinary approach, our goal is to gain insights into the diverse modes of action that genes may assume, ultimately contributing to the development of patient-specific treatments and preventive strategies.

Recent publications

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