Compressing neural networks via formal methods.

Advancements in Neural Networks have led to larger models, challenging implementation on embedded devices with memory, battery, and computational constraints. Consequently, network compression has flourished, offering solutions to reduce operations and parameters. However, many methods rely on heuristics, often requiring re-training for accuracy.

Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE.

The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities.

Is a Novel Target of Mutant p53 in Breast and Ovarian Cancer Cell Lines.

is the most frequently mutated gene in human cancers. Most genomic alterations are missense mutations, which cause a loss of its tumour suppressor functions while providing mutant p53 (mut_p53) with oncogenic features (gain-of-function). Loss of p53 tumour suppressor functions alters the transcription of both protein-coding and non-protein-coding genes.

Draft genome sequences of 14 spp. strains, representatives of a collection of 200 strains.

Lactobacilli have a fundamental role in the food industry as starters and probiotics, therefore, requiring special attention concerning food safety. In this work, 14 strains selected accordingly to their genetic fingerprint and physiologic characteristics are presented as representatives of a collection of 200 strains.

A sense of life: computational and experimental investigations with models of biochemical and evolutionary processes.

We collaborate in a research program aimed at creating a rigorous framework, experimental infrastructure, and computational environment for understanding, experimenting with, manipulating, and modifying a diverse set of fundamental biological processes at multiple scales and spatio-temporal modes. The novelty of our research is based on an approach that (i) requires coevolution of experimental science and theoretical techniques and (ii) exploits a certain universality in biology guided by a parsimonious model of evolutionary mechanisms operating at the genomic level and manifesting at the proteomic, transcriptomic, phylogenic, and other higher levels. Our current program in "systems biology" endeavors to marry large-scale biological experiments with the tools to ponder and reason about large, complex, and subtle natural systems.