Jaume Bacardit

This paper introduces MacqD, a deep learning model for detecting macaques in complex lab environments, achieving high F1-scores across various scenarios.

The paper identifies gene biomarkers to detect load stress in engineered E. coli strains using machine learning.

This paper introduces an algorithm to convert raw DNA origami sequences back into schematic designs, enabling easier modification and reuse of existing nanostructures.

This paper develops a computational method to select a robust gene expression biomarker panel for stress sensing in Bacillus subtilis using machine learning, gene regulatory networks, and co-expression networks.

This study uses single-cell RNA-sequencing to analyze fetal bone marrow development and identifies disruptions in hematopoiesis in Down syndrome.

This study introduces t-SNE transformation as an alternative to log normalization for single-cell RNA-seq, showing improved detection of local features in datasets.

The paper uses single-cell transcriptomics to map hematopoiesis in human fetal liver, identifying cell differentiation trajectories and shifts in hematopoietic composition during development.

This study uses a crowdsourcing approach to stratify ALS patients, identifying four distinct clusters with unique predictors and improving outcome prediction.