Shuting Jin

Orcid: 0000-0002-8113-9367

According to our database1, Shuting Jin authored at least 16 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.
PLoS Comput. Biol., November, 2023

Improving molecular representation learning with metric learning-enhanced optimal transport.
Patterns, April, 2023

KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023

RareDR: A Drug Repositioning Approach for Rare Diseases Based on Knowledge Graph.
Proceedings of the Advanced Intelligent Computing Technology and Applications, 2023

2022
LaGAT: link-aware graph attention network for drug-drug interaction prediction.
Bioinform., December, 2022

preMLI: a pre-trained method to uncover microRNA-lncRNA potential interactions.
Briefings Bioinform., 2022

DeepTTA: a transformer-based model for predicting cancer drug response.
Briefings Bioinform., 2022

Drug-target interactions prediction via deep collaborative filtering with multiembeddings.
Briefings Bioinform., 2022

Tinkling: Research on the design of a Bianqing interactive system for children based on experiential learning.
Proceedings of the 15th International Symposium on Computational Intelligence and Design, 2022

2021
HeTDR: Drug repositioning based on heterogeneous networks and text mining.
Patterns, 2021

An authorisation certificate-based access control model.
Int. J. Secur. Networks, 2021

Heterogeneous network-based drug repurposing for COVID-19.
CoRR, 2021

Application of deep learning methods in biological networks.
Briefings Bioinform., 2021

A Meta-Path based Drug-Target Prediction Model with Collaborative Attention Mechanisms.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021

2020
A Semantic Inference Based Method for Privacy Measurement.
IEEE Access, 2020

A multi-task learning method for analyzing microbiota as cancer immunotherapy signal.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020


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