Salvatore Milite

PhD student in Computational Biology @HumanTechnopole | I have no idea of what I am doing

[Salvatore Milite @smilite_]

🔗 This is a very small step, but I believe that these autodiff agent-based models provide a nice way of simulating biological ensembles with some level of physical constraints and realism. On that line, see also similar work with cell Potts models.
https://t.co/hL1YHYYkuR

[Salvatore Milite @smilite_]

📄 In this work, we try to bridge two parallel research areas: the well-established literature on agent-based modeling in biological systems and recent advances in learning update rules in CA from data with autodifferentiation.

https://t.co/4EsZECaEYV

[Salvatore Milite @smilite_]

📊 Compared to traditional agent-based modeling with ABC inference, we achieve better results with less manual tuning. In complex domains, coming up with a reasonable set of actions for the ABC is hard.

[Andrea Sottoriva @AndreaSottoriva]

How do you make sense of single cell multi-modal data beyond clustering? We need better methods that incorporate biological principles! Check out our latest method MIDAA, deep learning for single-cell multiomics with evolutionary principles: https://t.co/T4Vy0l6vW3

[Giulio Caravagna @gcaravagna]

[https://t.co/HewhGoAiKF] Cancer biomarker discovery is historically mutation-centric, often neglecting the key role of genomics instability. Recently, we asked ourselves how do mutations and copy number alterations (CNAs) determine prognosis and tropism? 😋😋1/n