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Zheng Ma

5 papers hereh-index 214 citations7 works total

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

author position
  • first author2
  • middle author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • q-bio.BM1
same name
  • Zheng Ma — 24 papers, h 17
  • Zheng Ma — 12 papers, h 2
  • Zheng Ma — 11 papers, h 5
  • Zheng Ma — 11 papers, h 31
  • Zheng Ma — 11 papers, h 4
  • Zheng Ma — 7 papers, h 7

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
most citedDisentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing

1 citations · 2 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026★ 1 cited

GraphPI: Efficient Protein Inference with Graph Neural Networks

Zheng Ma, Jiazhen Chen, Lei Xin +1

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application i…

cs.LG2025

Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection

Jiazhen Chen, Xiuqin Liang, Sichao Fu +2

Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years, which aims to identify data anomalous patterns utilizing only unlabeled node informati…

cs.LG2025

Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs

Jiazhen Chen, Sichao Fu, Zheng Ma +3

Semi-supervised graph anomaly detection (GAD) has recently received increasing attention, which aims to distinguish anomalous patterns from graphs under the guidance of a moderate…

cs.LG2024

Towards Cross-domain Few-shot Graph Anomaly Detection

Jiazhen Chen, Sichao Fu, Zhibin Zhang +4

Few-shot graph anomaly detection (GAD) has recently garnered increasing attention, which aims to discern anomalous patterns among abundant unlabeled test nodes under the guidance o…

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