no code implementations • 14 Feb 2024 • David Oniani, Jordan Hilsman, Chengxi Zang, Junmei Wang, Lianjin Cai, Jan Zawala, Yanshan Wang
In this paper, we first propose a new task, which is the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task.
1 code implementation • 11 Oct 2021 • Chengxi Zang, Fei Wang
We propose a general supervised contrastive loss $\mathcal{L}_{\text{Contrastive Cross Entropy} } + \lambda \mathcal{L}_{\text{Supervised Contrastive Regularizer}}$ for learning both binary classification (e. g. in-hospital mortality prediction) and multi-label classification (e. g. phenotyping) in a unified framework.
no code implementations • 11 Jan 2021 • Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki, Chengxi Zang, Nidhi Naik, Sulaiman Somani, Jessica K. De Freitas, Ishan Paranjpe, Akhil Vaid, Riccardo Miotto, Girish N. Nadkarni, Marinka Zitnik, ArifulAzad, Fei Wang, Ying Ding, Benjamin S. Glicksberg
This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
1 code implementation • 20 Jul 2020 • Karan Yang, Chengxi Zang, Fei Wang
Drug discovery aims at designing novel molecules with specific desired properties for clinical trials.
1 code implementation • 17 Jun 2020 • Chengxi Zang, Fei Wang
Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process.
1 code implementation • 18 Aug 2019 • Chengxi Zang, Fei Wang
To address these challenges, we propose to combine Ordinary Differential Equation Systems (ODEs) and Graph Neural Networks (GNNs) to learn continuous-time dynamics on complex networks in a data-driven manner.