no code implementations • 27 Nov 2023 • Xinxing Yang, Genke Yang, Jian Chu
The computational model based on deep learning concatenates the representation of multiple drugs and the corresponding cell line feature as input, and the output is whether the drug combination can have an inhibitory effect on the cell line.
no code implementations • 18 Jul 2023 • Xinxing Yang, Genke Yang, Jian Chu
Moreover, most previous studies tended to design complicated representation learning module, while uniformity, which is used to measure representation quality, is ignored.
no code implementations • 27 Aug 2022 • Song Chen, Shengze Cai, Tehuan Chen, Chao Xu, Jian Chu
In this paper, we propose a novel nonlinear observer based on neural networks, called neural observer, for observation tasks of linear time-invariant (LTI) systems and uncertain nonlinear systems.
no code implementations • 1 Jun 2022 • Xinxing Yang, Genke Yang, Jian Chu
Specifically, we take the drug-disease association prediction problem as the main task, and the auxiliary task is to use data augmentation strategies and contrast learning to mine the internal relationships of the original drug features, so as to automatically learn a better drug representation without supervised labels.
no code implementations • 29 Nov 2021 • Xinxing Yang, Genke Yang, Jian Chu
The limitations of these works are mainly due to the following two reasons: firstly, previous works used negative sampling techniques to treat unvalidated drug-disease associations as negative samples, which is invalid in real-world settings; secondly, the inner product cannot fully take into account the feature information contained in the latent factor of drug and disease.
no code implementations • 8 Jun 2018 • Chunlin Chen, Daoyi Dong, Han-Xiong Li, Jian Chu, Tzyh-Jong Tarn
In this paper, a fidelity-based probabilistic Q-learning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems.