Hang Wei

Orcid: 0000-0002-0579-1716

Affiliations:
  • Xidian University, Xi'an, China
  • Harbin Institute of Technology, School of Computer Science and Technology, Shenzhen, China (former)


According to our database1, Hang Wei authored at least 13 papers between 2020 and 2025.

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

Timeline

Legend:

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Bibliography

2025
A Systemic Pipeline of Identifying lncRNA-Disease Associations to the Prognosis and Treatment of Hepatocellular Carcinoma.
IEEE Trans. Big Data, April, 2025

2024
Multiple types of disease-associated RNAs identification for disease prognosis and therapy using heterogeneous graph learning.
Sci. China Inf. Sci., 2024

IDP-EDL: enhancing intrinsically disordered protein prediction by combining protein language model and ensemble deep learning.
Briefings Bioinform., 2024

2023
iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.
PLoS Comput. Biol., 2023

2022
iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network.
PLoS Comput. Biol., October, 2022

idenMD-NRF: a ranking framework for miRNA-disease association identification.
Briefings Bioinform., 2022

iCircDA-ENR: identification of circRNA-disease associations based on ensemble network representation.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

2021
iLncRNAdis-FB: Identify lncRNA-Disease Associations by Fusing Biological Feature Blocks Through Deep Neural Network.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021

iCircDA-LTR: identification of circRNA-disease associations based on Learning to Rank.
Bioinform., 2021

SMI-BLAST: a novel supervised search framework based on PSI-BLAST for protein remote homology detection.
Bioinform., 2021

iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning.
Briefings Bioinform., 2021

2020
iPiDA-sHN: Identification of Piwi-interacting RNA-disease associations by selecting high quality negative samples.
Comput. Biol. Chem., 2020

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization.
Briefings Bioinform., 2020


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