no code implementations • 20 Feb 2024 • Xiangyu Zhao, Zehui Li, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao
These methods cannot fully address the complexities of real-world large-scale networks that often involve higher-order node relations beyond only being pairwise.
no code implementations • 8 Feb 2024 • Zehui Li, Yuhao Ni, William A V Beardall, Guoxuan Xia, Akashaditya Das, Guy-Bart Stan, Yiren Zhao
This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA sequences, and Absorb-Escape, a post-training algorithm designed to refine these sequences.
1 code implementation • 9 Oct 2023 • Zehui Li, Yuhao Ni, Tim August B. Huygelen, Akashaditya Das, Guoxuan Xia, Guy-Bart Stan, Yiren Zhao
On the other hand, Diffusion Models are a promising new class of generative models that are not burdened with these problems, enabling them to reach the state-of-the-art in domains such as image generation.
1 code implementation • 8 Jun 2023 • Zehui Li, Akashaditya Das, William A V Beardall, Yiren Zhao, Guy-Bart Stan
This work presents Genomic Interpreter: a novel architecture for genomic assay prediction.
no code implementations • 8 Jun 2023 • Zehui Li, Xiangyu Zhao, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao
Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets.
no code implementations • 28 Mar 2014 • Wei Pan, Aivar Sootla, Guy-Bart Stan
In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks.
no code implementations • 12 Mar 2013 • Aivar Sootla, Natalja Strelkowa, Damien Ernst, Mauricio Barahona, Guy-Bart Stan
In this paper, we consider the problem of optimal exogenous control of gene regulatory networks.