1 code implementation • 29 Oct 2020 • Hao Xu, Shengqi Sang, Peizhen Bai, Laurence Yang, Haiping Lu
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks.
1 code implementation • 17 Jun 2021 • Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot, Shuo Zhou, Mustafa Chasmai, Lawrence Schobs
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems.
2 code implementations • 3 Aug 2022 • Peizhen Bai, Filip Miljković, Bino John, Haiping Lu
Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution.
no code implementations • 1 Sep 2023 • Peizhen Bai, Xianyuan Liu, Haiping Lu
Owing to the scarcity of labeled molecules, there has been growing interest in self-supervised learning methods that learn generalizable molecular representations from unlabeled data.