2 citations · 2 across the 3 of their papers we have counts for
3 papers
Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
Lorenzo Bini, Stephane Marchand-Maillet
We present LaplaceGNN, a novel self-supervised graph learning framework that bypasses the need for negative sampling by leveraging spectral bootstrapping techniques. Our method int…
LapDDPM: A Conditional Graph Diffusion Model for scRNA-seq Generation with Spectral Adversarial Perturbations
Lorenzo Bini, Stephane Marchand-Maillet
Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data, especially with conditional control, is challenging due to its high dimen…
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking
Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou +2
This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data. The dataset comprises bone marrow samples from 30…