Chenkai Li

424 total citations · 1 hit paper
18 papers, 256 citations indexed

About

Chenkai Li is a scholar working on Molecular Biology, Microbiology and Media Technology. According to data from OpenAlex, Chenkai Li has authored 18 papers receiving a total of 256 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 8 papers in Microbiology and 4 papers in Media Technology. Recurrent topics in Chenkai Li's work include Antimicrobial Peptides and Activities (8 papers), Biochemical and Structural Characterization (7 papers) and Advanced Image Fusion Techniques (4 papers). Chenkai Li is often cited by papers focused on Antimicrobial Peptides and Activities (8 papers), Biochemical and Structural Characterization (7 papers) and Advanced Image Fusion Techniques (4 papers). Chenkai Li collaborates with scholars based in Canada, China and United States. Chenkai Li's co-authors include İnanç Birol, René L. Warren, Linda Hoang, Caren C. Helbing, S. Austin Hammond, Lauren C. Bergman, Simon Houston, Caroline E. Cameron, Chen Yang and Titus Wong and has published in prestigious journals such as IEEE Access, Protein Science and Remote Sensing.

In The Last Decade

Chenkai Li

14 papers receiving 252 citations

Hit Papers

AMPlify: attentive deep learning model for discovery of n... 2022 2026 2023 2024 2022 40 80 120
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens
    2022 126 citations Chenkai Li, S. Austin Hammond et al. BMC Genomics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chenkai Li Canada 9 163 137 25 23 21 18 256
Weiqiang Cheng United States 12 209 1.3× 13 0.1× 9 0.4× 40 1.7× 16 0.8× 25 377
Hongwu Lv China 9 303 1.9× 144 1.1× 7 0.3× 8 0.3× 1 0.0× 13 366
Genwei Zhang United States 11 163 1.0× 11 0.1× 6 0.2× 6 0.3× 5 0.2× 21 445
Jielu Yan China 7 451 2.8× 374 2.7× 2 0.1× 35 1.5× 5 0.2× 18 554
Vijayaraghava Seshadri Sundararajan India 7 235 1.4× 82 0.6× 15 0.6× 13 0.6× 4 0.2× 15 298
Luyang Cao China 9 344 2.1× 343 2.5× 252 10.1× 18 0.8× 6 0.3× 14 507
Marco Falda Italy 10 206 1.3× 13 0.1× 28 1.1× 18 0.8× 4 0.2× 23 356
Rongde Qiu United States 10 338 2.1× 26 0.2× 34 1.4× 10 0.4× 31 1.5× 21 406
Chenghua Liu China 10 167 1.0× 6 0.0× 15 0.6× 6 0.3× 2 0.1× 30 316

Countries citing papers authored by Chenkai Li

Since Specialization
Citations

This map shows the geographic impact of Chenkai Li'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 Chenkai Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chenkai Li more than expected).

Fields of papers citing papers by Chenkai Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chenkai Li. 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 Chenkai Li. The network helps show where Chenkai Li may publish in the future.

Co-authorship network of co-authors of Chenkai Li

This figure shows the co-authorship network connecting the top 25 collaborators of Chenkai Li. A scholar is included among the top collaborators of Chenkai Li 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 Chenkai Li. Chenkai Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Chen, Dapeng, Chenkai Li, Guangzhu Peng, et al.. (2025). Visually Impaired People Recognize Virtual Objects Through 3D Reconstructed Shapes. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 33. 4396–4408.
2.
Yanai, Anat, Chenkai Li, Lauren Coombe, et al.. (2025). Antimicrobial peptides with high bioactivity against MDR isolates: Addressing public health concerns. Microbial Pathogenesis. 207. 107893–107893.
3.
Li, Chenkai, Anat Yanai, Lauren Coombe, et al.. (2025). Mining the UniProtKB /Swiss‐Prot database for antimicrobial peptides. Protein Science. 34(4). e70083–e70083. 1 indexed citations
4.
Li, Chenkai, et al.. (2025). Research on a fast signal recognition method for a laser screen measurement system based on LSTM. Measurement. 254. 117905–117905.
5.
Wang, Hongmei, Chenkai Li, Qin Wu, & Jingyu Wang. (2024). An Improved DETR Based on Angle Denoising and Oriented Boxes Refinement for Remote Sensing Object Detection. Remote Sensing. 16(23). 4420–4420. 1 indexed citations
6.
Li, Chenkai, Lauren Coombe, Anat Yanai, et al.. (2024). De novo synthetic antimicrobial peptide design with a recurrent neural network. Protein Science. 33(8). e5088–e5088. 8 indexed citations
7.
Yanai, Anat, et al.. (2024). Structure‐aware deep learning model for peptide toxicity prediction. Protein Science. 33(7). e5076–e5076. 10 indexed citations
8.
Li, Chenkai, René L. Warren, & İnanç Birol. (2023). Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction. BMC Research Notes. 16(1). 11–11. 10 indexed citations
9.
Li, Chenkai, et al.. (2023). Infrared and Visible Image Fusion Based on Autoencoder Composed of CNN-Transformer. IEEE Access. 11. 78956–78969. 8 indexed citations
10.
Li, Chenkai, S. Austin Hammond, Chen Yang, et al.. (2022). AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC Genomics. 23(1). 77–77. 126 indexed citations breakdown →
11.
Li, Chenkai, Lauren Coombe, René L. Warren, et al.. (2022). Associating Biological Activity and Predicted Structure of Antimicrobial Peptides from Amphibians and Insects. Antibiotics. 11(12). 1710–1710. 11 indexed citations
12.
Li, Lin, Chenkai Li, Xuanyu Lu, Hongmei Wang, & Daming Zhou. (2022). Multi-focus image fusion with convolutional neural network based on Dempster-Shafer theory. Optik. 272. 170223–170223. 9 indexed citations
13.
Nip, Ka Ming, Chenkai Li, Anat Yanai, et al.. (2022). Mining Amphibian and Insect Transcriptomes for Antimicrobial Peptide Sequences with rAMPage. Antibiotics. 11(7). 952–952. 18 indexed citations
14.
Wang, Hongmei, et al.. (2022). Infrared and visible image fusion based on multi‐channel convolutional neural network. IET Image Processing. 16(6). 1575–1584. 11 indexed citations
15.
Rajan‐Babu, Indhu‐Shree, Readman Chiu, Patricia Birch, et al.. (2021). Genome-wide sequencing as a first-tier screening test for short tandem repeat expansions. Genome Medicine. 13(1). 126–126. 33 indexed citations
16.
Li, Chenkai, et al.. (2021). Power optimization in UAV-based wireless power transmission and collaborative MEC IoT networks. Wireless Networks. 30(5). 3771–3779. 3 indexed citations
17.
Lu, Weidang, et al.. (2021). Deterministic estimator learning automata-based neighbor discovery schemes for D2D networks with directional antennas. Physical Communication. 46. 101329–101329. 3 indexed citations
18.

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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