Andrea Tangherloni

This study examines scaling laws in single-cell RNA sequencing foundation models, showing how performance scales with model size and training compute.

pysigscore scores gene signatures in bulk and single-cell RNA-seq data, validating its performance on multiple datasets.

GENESIS converts Multiome GEX data into synthetic scRNA-Seq profiles using generative models, improving cell-type classification accuracy.

SMGen generates synthetic biochemical reaction network models for benchmarking, showing it can create models with 512 species and reactions in under 7 seconds.

FiCoS is a GPU-powered deterministic simulator for biochemical networks that provides fine- and coarse-grained parallelization, achieving up to 855x speedup over CPU-based simulators.

The paper introduces scAEspy, a tool using autoencoders to improve scRNA-seq data integration and cell-type identification by outperforming existing batch-effect removal methods.

ACDC automates cell detection and counting in time-lapse microscopy images, achieving high accuracy without large annotated datasets.