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Zheng Chen

9 papers hereh-index 323 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author9

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.LG5
  • cs.AI3
  • cs.IR1
same name
  • Zheng Chen — 28 papers, h 8
  • Zheng Chen — 24 papers, h 20
  • Zheng Chen — 19 papers, h 6
  • Zheng Chen — 14 papers, h 4
  • Zheng Chen — 12 papers, h 9
  • Zheng Chen — 12 papers, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing 2025Show all

4 papers · 1 filter

cs.IR2025

DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research

Zifeng Wang, Zheng Chen, Ziwei Yang +5

Biomedical knowledge graphs (KGs) encode vast, heterogeneous information spanning literature, genes, pathways, drugs, diseases, and clinical trials, but leveraging them collectivel…

cs.LG2025

ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation

Rikuto Kotoge, Ziwei Yang, Zheng Chen +4

Retrieving targeted pathways in biological knowledge bases, particularly when incorporating wet-lab experimental data, remains a challenging task and often requires downstream anal…

cs.LG2025

Tokenizing Single-Channel EEG with Time-Frequency Motif Learning

Jathurshan Pradeepkumar, Xihao Piao, Zheng Chen +1

Foundation models are reshaping EEG analysis, yet an important problem of EEG tokenization remains a challenge. This paper presents TFM-Tokenizer, a novel tokenization framework th…

cs.LG2025

Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer

Lingwei Zhu, Zheng Chen, Yukie Nagai +1

This paper adds to the growing literature of reinforcement learning (RL) for healthcare by proposing a novel paradigm: augmenting any predictor with Rule-based RL Layer (RRLL) that…

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