Epigenetics is central to tumorigenesis, but the co-evolution of the cancer epigenome and its microenvironment is severely understudied. Here, we measure chromatin accessibility and transcriptome, at single cell resolution, of a set of normal colon, primary colorectal cancers and metastases, and identify recurrent epig...
Alexandra Livanova, Luca Azzolin, F. Cambuli et al.· bioRxiv· 0 citations
It is argued that population genetics provides the mathematical framework needed to extract evolutionary dynamics from cancer genomes and it is shown how models of mutation, selection and drift transform allele frequencies from descriptive measurements into quantitative estimates of clonal fitness and evolutionary timi...
G. Caravagna, Trevor A. Graham, A. Sottoriva· Nature Reviews. Cancer· 1 citation
The results show that graph-based signal propagation is a biologically grounded alternative to latent-shift perturbation modeling and can improve the recovery of sparse perturbation-induced transcriptional effects.
Michele Calabrò, Patrick Sheehan, F. Cambuli et al.· bioRxiv· 0 citations
The results demonstrate that state-based temporal modeling provides an effective and scalable inductive bias for learning from dynamic biological networks, opening new directions for temporal graph learning as an AI-driven simulation of cellular processes.
Manuel Dileo, A. Sottoriva· Machine-mediated learning· 0 citations
An open-source Bayesian inference tool that determines the multiplicity and copy number of driver mutations from tumor sequencing datasets is presented, an open-source Bayesian inference tool that determines the multiplicity and copy number of driver mutations from tumor sequencing datasets.
N. Calonaci, E. Krasniqi, D. Čolić et al.· Nature Genetics· 0 citations
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