Area of research
Cancer Research · Molecular Biology
Research interest
Research focused on Markov chain and Cancer, with related work in Chromatin, Applied mathematics, Metastasis. Notable publications include 'Mechanisms governing the pioneering and redistribution capabilities of the non-classical pioneer PU.1', 'Modelling cancer progression using Mutual Hazard Networks', and 'Reconstructing Disease Histories in Huge Discrete State Spaces'.
A Scalable Framework for Pan-Cancer Tumor Evolution Analysis Enables Transfer of Progression Mechanisms Across Tumor Entities
<tt> <b>mhn</b> </tt> : a Python package for analyzing cancer progression with Mutual Hazard Networks
Reconstructing Disease Histories in Huge Discrete State Spaces
Modeling metastatic progression from cross-sectional cancer genomics data
Overcoming Observation Bias for Cancer Progression Modeling
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models
Correcting for Observation Bias in Cancer Progression Modeling
Modeling metastatic progression from cross-sectional cancer genomics data
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Overcoming Observation Bias for Cancer Progression Modeling
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Low-rank tensor methods for Markov chains with applications to tumor progression models
Mechanisms governing the pioneering and redistribution capabilities of the non-classical pioneer PU.1
Modelling cancer progression using Mutual Hazard Networks
Modelling cancer progression using Mutual Hazard Networks