Dianke Li

449 total citations · 1 hit paper
10 papers, 213 citations indexed

About

Dianke Li is a scholar working on Molecular Biology, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Dianke Li has authored 10 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 4 papers in Oncology and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Dianke Li's work include Machine Learning in Bioinformatics (3 papers), Bioinformatics and Genomic Networks (3 papers) and Single-cell and spatial transcriptomics (2 papers). Dianke Li is often cited by papers focused on Machine Learning in Bioinformatics (3 papers), Bioinformatics and Genomic Networks (3 papers) and Single-cell and spatial transcriptomics (2 papers). Dianke Li collaborates with scholars based in China, United States and Spain. Dianke Li's co-authors include Gening Jiang, Huansha Yu, Peng Zhang, Lele Zhang, Liangdong Sun, Shuangyi Li, Yilv Yan, Haoran Xia, Junjie Hu and Jin Wang and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Cells.

In The Last Decade

Dianke Li

9 papers receiving 212 citations

Hit Papers

Tumor microenvironment remodeling after neoadjuvant immun... 2023 2026 2024 2025 2023 50 100 150
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. Tumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing
    2023 152 citations Junjie Hu, Lele Zhang et al. Genome Medicine profile →

Peers

Dianke Li
Shuangyi Li China
Ziwen Yu China
Avi Spira United States
Antonietta Bacchiocchi United States
Jasmine A. McQuerry United States
Marie Naigeon France
Georgia Spain United Kingdom
Giulio Mandelli Italy
Soren Charmsaz United States
Karma Dacosta United States
Shuangyi Li China
Dianke Li
Citations per year, relative to Dianke Li Dianke Li (= 1×) peers Shuangyi Li

Countries citing papers authored by Dianke Li

Since Specialization
Citations

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

Fields of papers citing papers by Dianke Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dianke Li

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

All Works

10 of 10 papers shown
1.
Yang, Xiaodi, Dianke Li, Jiajun Li, et al.. (2025). Graph neural network integrated with pretrained protein language model for predicting human–virus protein–protein interactions. Briefings in Bioinformatics. 26(5).
2.
3.
Li, Dianke, et al.. (2024). Integration of protein sequence and protein–protein interaction data by hypergraph learning to identify novel protein complexes. Briefings in Bioinformatics. 25(4). 3 indexed citations
4.
Liu, Yuan, Dianke Li, Yang Li, et al.. (2024). A protein sequence-based deep transfer learning framework for identifying human proteome-wide deubiquitinase-substrate interactions. Nature Communications. 15(1). 4519–4519. 13 indexed citations
5.
Hu, Junjie, Lele Zhang, Haoran Xia, et al.. (2023). Tumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing. Genome Medicine. 15(1). 14–14. 152 indexed citations breakdown →
6.
7.
Dai, Jie, Xinsheng Zhu, Dianke Li, et al.. (2022). Sleeve resection after neoadjuvant chemoimmunotherapy in the treatment of locally advanced non-small cell lung cancer. Translational Lung Cancer Research. 11(2). 188–200. 13 indexed citations
8.
Li, Dianke, Huansha Yu, Junjie Hu, et al.. (2022). Comparative profiling of single-cell transcriptome reveals heterogeneity of tumor microenvironment between solid and acinar lung adenocarcinoma. Journal of Translational Medicine. 20(1). 423–423. 13 indexed citations
9.
10.
Zhang, Jing, Haiping Zhang, Lele Zhang, et al.. (2022). Single‐Cell Transcriptome Identifies Drug‐Resistance Signature and Immunosuppressive Microenvironment in Metastatic Small Cell Lung Cancer. SHILAP Revista de lepidopterología. 3(2). 2100060–2100060. 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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