Renfei Ma

586 total citations
13 papers, 130 citations indexed

About

Renfei Ma is a scholar working on Molecular Biology, Artificial Intelligence and Surgery. According to data from OpenAlex, Renfei Ma has authored 13 papers receiving a total of 130 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 3 papers in Artificial Intelligence and 2 papers in Surgery. Recurrent topics in Renfei Ma's work include Machine Learning in Bioinformatics (3 papers), Bioinformatics and Genomic Networks (3 papers) and Protein Structure and Dynamics (2 papers). Renfei Ma is often cited by papers focused on Machine Learning in Bioinformatics (3 papers), Bioinformatics and Genomic Networks (3 papers) and Protein Structure and Dynamics (2 papers). Renfei Ma collaborates with scholars based in United Kingdom, New Zealand and China. Renfei Ma's co-authors include Luca Parisi, Daniel Neagu, Felician Campean, Lantian Yao, Tzong-Yi Lee, Shangfu Li, Wenshuo Li, Hsien‐Da Huang, Mansour Youseffi and Adam Bartlett and has published in prestigious journals such as Expert Systems with Applications, Knowledge-Based Systems and Briefings in Bioinformatics.

In The Last Decade

Renfei Ma

12 papers receiving 129 citations

Peers

Renfei Ma

MB

EPIDE

AI

NEURO

PHYSI

Cheng Ju China

Hongyu Kang China

Henri‐Pierre Charles France

Hang Yuan China

Cheng Ji China

Jiawei Su China

Noriko Arai Japan

Mingui Zhang China

Rheeda L. Ali United States

Rubén Doste United Kingdom

Cheng Ju China

Renfei Ma

28 ×0.8

MB

8 ×0.4

EPIDE

23 ×1.1

AI

6 ×0.4

NEURO

11 ×0.8

PHYSI

Citations per year, relative to Renfei Ma Renfei Ma (= 1×) peers Cheng Ju

Countries citing papers authored by Renfei Ma

Since Specialization

Citations

This map shows the geographic impact of Renfei Ma's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Renfei Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Renfei Ma more than expected).

Fields of papers citing papers by Renfei Ma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Renfei Ma. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Renfei Ma. The network helps show where Renfei Ma may publish in the future.

Co-authorship network of co-authors of Renfei Ma

This figure shows the co-authorship network connecting the top 25 collaborators of Renfei Ma. A scholar is included among the top collaborators of Renfei Ma based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Renfei Ma. Renfei Ma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Zhou, Xiaoyan, Luca Parisi, Wentao Huang, et al.. (2025). A novel integrative multimodal classifier to enhance the diagnosis of Parkinson’s disease. Briefings in Bioinformatics. 26(2). 2 indexed citations

2.

Parisi, Luca, Daniel Neagu, Narrendar RaviChandran, Renfei Ma, & Felician Campean. (2024). Optimal evolutionary framework-based activation function for image classification. Knowledge-Based Systems. 299. 112025–112025. 3 indexed citations

3.

Xu, Meng‐Yang, et al.. (2024). 3D physiologically-informed deep learning for drug discovery of a novel vascular endothelial growth factor receptor-2 (VEGFR2). Heliyon. 10(16). e35769–e35769. 3 indexed citations

4.

Youseffi, Mansour, et al.. (2024). A Novel Artificial Intelligence-Driven Technique for Enhancing Medical Imaging Techniques to Detect Non-Small Cell Lung Cancer. Bradford Scholars (University of Bradford). 1–6. 1 indexed citations

5.

Ma, Renfei, Shangfu Li, Luca Parisi, et al.. (2023). Holistic similarity-based prediction of phosphorylation sites for understudied kinases. Briefings in Bioinformatics. 24(2). 3 indexed citations

6.

Luo, Mengqi, Shangfu Li, Yuxuan Pang, et al.. (2022). Extraction of microRNA–target interaction sentences from biomedical literature by deep learning approach. Briefings in Bioinformatics. 24(1). 5 indexed citations

7.

Ma, Renfei, Shangfu Li, Wenshuo Li, et al.. (2022). KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-Specific Phosphorylation Sites. Genomics Proteomics & Bioinformatics. 21(1). 228–241. 26 indexed citations

8.

Ma, Renfei, et al.. (2022). Neuroevolutionary intelligent system to aid diagnosis of motor impairments in children. Applied Intelligence. 52(9). 10757–10767.

9.

Parisi, Luca, et al.. (2021). m-ark-Support Vector Machine for Early Detection of Parkinson’s Disease from Speech Signals. 15. 34–41. 8 indexed citations

10.

Parisi, Luca, Daniel Neagu, Renfei Ma, & Felician Campean. (2021). Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson’s disease and COVID-19. Expert Systems with Applications. 187. 115892–115892. 44 indexed citations

11.

Parisi, Luca, et al.. (2021). Ηyper-sinh-Convolutional Neural Network for Early Detection of Parkinson’s Disease from Spiral Drawings. 9. 1–7. 10 indexed citations

12.

Ma, Renfei, et al.. (2019). Anatomically based simulation of hepatic perfusion in the human liver. International Journal for Numerical Methods in Biomedical Engineering. 35(9). e3229–e3229. 17 indexed citations

13.

Ma, Renfei, et al.. (2019). Modeling the hepatic arterial flow in living liver donor after left hepatectomy and postoperative boundary condition exploration. International Journal for Numerical Methods in Biomedical Engineering. 36(3). e3268–e3268. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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