Search Results for author: Jian Chu

Found 6 papers, 0 papers with code

Fidelity-based Probabilistic Q-learning for Control of Quantum Systems

no code implementations8 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.

Q-Learning

The Computational Drug Repositioning without Negative Sampling

no code implementations29 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.

Self-supervised Learning for Label Sparsity in Computational Drug Repositioning

no code implementations1 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.

Data Augmentation Drug Discovery +1

Neural Observer with Lyapunov Stability Guarantee for Uncertain Nonlinear Systems

no code implementations27 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.

GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction

no code implementations18 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.

Contrastive Learning Drug Discovery +1

The Graph Convolutional Network with Multi-representation Alignment for Drug Synergy Prediction

no code implementations27 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.