Guillermo Prol-Castelo
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gprolcastelo.bsky.social
Guillermo Prol-Castelo
@gprolcastelo.bsky.social
30 followers 59 following 21 posts
Bioinformatics predoctoral researcher at @bsc-cns.bsky.social and @upf.edu
#AI #bio
https://github.com/gprolcastelo
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gprolcastelo.bsky.social
We found that the most common uses of VAEs to study cancer include diagnosis, prognosis, and subtyping.
Word cloud of exclusion reasons. Most publications use deep and representation learning applied to cancer for subtyping, diagnosis, and studying prognosis and survival.
June 12, 2025 at 9:43 AM
gprolcastelo.bsky.social
DRL methods are used to learn a low-dimensional embedding from data. Specifically, the VAE can learn said representation, the latent space, keeping non-linear relationships in the original data. Moreover, it is also a generative method, as it can create new, synthetic data from the original data.
Summary figure of the most common methodologies involving representation learning and cancer studies. On the left side of the figure are shown the most common types of data used to train VAEs particularly—and DRL methods more generally—, in cancer studies. After being passed to the encoder, data is embeded into a lower-dimensional representation, the latent space. The latent space has been commonly used for subtyping, survival anlyses (commonly as part of prognostic analyses), and, when it comes to single-cell data, also for pseudo-time trajectories inference. Possible further applications, shown on the right-hand side of the figure, include leveraging the decoder’s generative capabilities to reconstruct noisy data, and align and infer out-of-sample data, which may be used to study cancer progression in time. However, such applications of the decoder remain underexplored in biomedical research.
June 12, 2025 at 9:43 AM
gprolcastelo.bsky.social
4.2. We have seen there are about 2,500 genes’ expressions that are unique to the G3-G4 subgroup, some of which are commonly mutated in MB: KMT2C, MYC, SNCAIP, SYNCRIP, and TP53.
January 13, 2025 at 3:07 PM
gprolcastelo.bsky.social
4. What do we find?

4.1. By identifying and augmenting the patients in the G3-G4 subgroup, we achieved high classification performance, reinforcing that this intermediate group displays distinct features in comparison to G3 and G4.
Boxplot of classification results for G3, G4, and G3-G4, augmented with synthetic data.
January 13, 2025 at 3:07 PM