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Chen-Yang Su

dblp:276/1233 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0001-6071-4660ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 70% Computational science and engineering · 30%
Artificial intelligence
1 paper
Reinforcement learning · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics › genetic study design
mendelian randomization
0.812024
MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures · Bioinform. 2024
Computational science and engineering
sensitivity analysis
0.812024
MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures · Bioinform. 2024
Bioinformatics and computational biology
statistical genetics
0.812024
MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures · Bioinform. 2024
Machine learning › Reinforcement learning › imitation learning
inverse reinforcement learning
0.512021
Regularized Inverse Reinforcement Learning · ICLR 2021
Bioinformatics and computational biology
genetic epidemiology
0.212024
MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

instrumental variable analysis · 0.8core gene hypothesis · 0.8regularization · 0.5
YearPublicationVenuePosition
2024 MR Corge: sensitivity analysis of Mendelian randomization based on the core gene hypothesis for polygenic exposures
abstract
SUMMARY: Mendelian randomization is being utilized to assess causal effects of polygenic exposures, where many genetic instruments are subject to horizontal pleiotropy. Existing methods for detecting and correcting for horizontal pleiotropy have important assumptions that may not be fulfilled. Built upon the core gene hypothesis, we developed MR Corge for performing sensitivity analysis of Mendelian randomization. MR Corge identifies a small number of putative core instruments that are more likely to affect genes with a direct biological role in an exposure and obtains causal effect estimates based on these instruments, thereby reducing the risk of horizontal pleiotropy. Using positive and negative controls, we demonstrated that MR Corge estimates aligned with established biomedical knowledge and the results of randomized controlled trials. MR Corge may be widely applied to investigate polygenic exposure-outcome relationships. AVAILABILITY AND IMPLEMENTATION: An open-sourced R package is available at https://github.com/zhwm/MRCorge.
Wenmin Zhang, Chen-Yang Su, Satoshi Yoshiji, Tianyuan Lu
Bioinform.2
2021 Regularized Inverse Reinforcement Learning
Wonseok Jeon, Chen-Yang Su, Paul Barde, Thang Doan, Derek Nowrouzezahrai, Joelle Pineau
ICLR2