Behind the Paper
The real stories behind the latest research papers, from conception to publication, the highs and the lows
Targeting fatty acid synthase in preclinical models of triple-negative breast cancer brain metastases synergizes with chemotherapy and impairs invasion
Metastatic triple-negative breast cancer cells need to acquire the ability to produce new fatty acids to more effectively colonize the brain. Our study investigates how this metabolic vulnerability can be targeted to increase efficacy of chemotherapies and impair cancer cell invasion.
Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning
Our platform uses deep learning to segment optical microscopy images of Drosophila hearts, facilitating the quantification of cardiac parameters for aging and dilated cardiomyopathy. Moreover, using a machine learning approach we able to predict cardiac aging with high accuracy.
Exploring SHANK3 in Neurodevelopment Using Stem Cells and Chemical Biology
Here we delve into our research on Phelan-McDermid syndrome (PMDS), a rare genetic disorder. We focused on SHANK3, an integral protein in neurodevelopment and synaptic function. We explore potential treatment strategies for PMDS, aiming to contribute to drug discovery in neurodevelopmental diseases.
PheSeq: How Bayesian Deep Learning Conceptualizes the Gene-Disease Associations and Bridges ’em with P-values?
"This study introduces PheSeq, a Bayesian deep learning model designed to integrate p-value data from sequence analysis with phenotype descriptions from literature and network data. It improves the robustness and interpretability of gene-disease association studies."
