Dániel L. Barabási

This paper develops Deep-STARmap and Deep-RIBOmap for 3D spatial transcriptomics and translatomics in thick tissue blocks, enabling analysis of gene expression and immune interactions in human skin cancer.

The paper analyzes eight connectomes and finds their degree distributions follow lognormal patterns, attributing this to a stochastic multiplicative process governing neuron size.

This study shows that visual experience and neuronal activity are not required for the development of visually guided swimming behavior in larval zebrafish.

The paper introduces a framework for generating network topologies based on genetic-inspired compatibility rules, demonstrating its ability to produce structured graphs and a Random Genetic family with distinct properties.

This paper introduces a developmental framework for machine learning that mimics neuroevolutionary principles to improve task performance and metalearning capabilities.