Imbalance deep multi‐instance learning for predicting isoform–isoform interactions-Reference-Cited by-同舟云学术

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Imbalance deep multi‐instance learning for predicting isoform–isoform interactions

Published:2021-02-25 Issue:6 Volume:36 Page:2797-2824
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J Intell Syst

Author:

Yu Guoxian¹²³^ORCID,Zeng Jie²^ORCID,Wang Jun²³^ORCID,Zhang Hong²^ORCID,Zhang Xiangliang⁴^ORCID,Guo Maozu⁵^ORCID

Affiliation:

1. School of Software Shandong University Jinan China

2. College of Computer and Information Science Southwest University Chongqing China

3. Joint SDU‐NTU Centre for Artificial Intelligence Research Shandong University Jinan China

4. CEMSE King Abdullah University of Science and Technology Thuwal Saudi Arabia

5. School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22402

Reference59 articles.

1. Solving the multiple instance problem with axis-parallel rectangles

2. Multiple instance learning: A survey of problem characteristics and applications

3. YanY WangX GuoX FangJ LiuW HuangJ.Deep multi‐instance learning with dynamic pooling. In:Asian Conference on Machine Learning; 2018:662‐667.

4. XingY YuG DomeniconiC WangJ ZhangZ GuoM.Multi‐view multi‐instance multi‐label learning based on collaborative matrix factorization. In:AAAI Conference on Artificial Intelligence; 2019:5508‐5515.

5. XingY YuG WangJ DomeniconiC ZhangX.Weakly‐supervised multi‐view multi‐instance multi‐label learning. In: Proceedings of the Twenty‐Ninth International Joint Conference on Artificial Intelligence;2020:3124‐3130.

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