Junru Jin

1.0k total citations · 1 hit paper
24 papers, 654 citations indexed

About

Junru Jin is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Junru Jin has authored 24 papers receiving a total of 654 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 6 papers in Computational Theory and Mathematics and 6 papers in Materials Chemistry. Recurrent topics in Junru Jin's work include Machine Learning in Bioinformatics (13 papers), RNA and protein synthesis mechanisms (8 papers) and RNA modifications and cancer (6 papers). Junru Jin is often cited by papers focused on Machine Learning in Bioinformatics (13 papers), RNA and protein synthesis mechanisms (8 papers) and RNA modifications and cancer (6 papers). Junru Jin collaborates with scholars based in China, South Korea and Macao. Junru Jin's co-authors include Leyi Wei, Ruheng Wang, Ran Su, Quan Zou, Kenta Nakai, Zhongshen Li, Yi Jiang, Yingying Yu, Wenjia He and Balachandran Manavalan and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Junru Jin

22 papers receiving 649 citations

Hit Papers

DeepBIO: an automated and interpretable deep-learning pla... 2023 2026 2024 2025 2023 25 50 75 100
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. DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis
    2023 104 citations Ruheng Wang, Yi Jiang et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junru Jin China 14 552 135 71 67 66 24 654
Yannan Bin China 15 519 0.9× 98 0.7× 70 1.0× 137 2.0× 19 0.3× 38 657
Xiangeng Wang China 11 325 0.6× 190 1.4× 34 0.5× 23 0.3× 31 0.5× 17 446
Yanyi Chu China 15 696 1.3× 383 2.8× 90 1.3× 44 0.7× 63 1.0× 20 912
Shengli Zhang China 19 878 1.6× 105 0.8× 43 0.6× 51 0.8× 51 0.8× 68 970
En-Ze Deng China 11 1.6k 2.9× 156 1.2× 83 1.2× 76 1.1× 38 0.6× 14 1.7k
Yihe Pang China 8 355 0.6× 60 0.4× 39 0.5× 29 0.4× 27 0.4× 11 401
Lezheng Yu China 12 1.1k 1.9× 253 1.9× 22 0.3× 37 0.6× 23 0.3× 25 1.1k
Chunyan Ao China 11 402 0.7× 50 0.4× 72 1.0× 20 0.3× 29 0.4× 24 519
Ghazaleh Taherzadeh Australia 15 696 1.3× 117 0.9× 14 0.2× 34 0.5× 18 0.3× 26 776

Countries citing papers authored by Junru Jin

Since Specialization
Citations

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

Fields of papers citing papers by Junru Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junru Jin

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

All Works

20 of 20 papers shown
1.
Jin, Junru, Leyi Wei, Hua Shi, et al.. (2025). MCAMEF-BERT: an efficient deep learning method for RNA N7-methylguanosine site prediction via multi-branch feature integration. Briefings in Bioinformatics. 26(5).
2.
Jin, Junru, Ding Wang, Xuetong Yang, et al.. (2025). A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability. Nature Communications. 16(1). 4382–4382. 2 indexed citations
3.
Jin, Junru, Kaixiang Li, Feifei Cui, et al.. (2025). Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices. Neural Networks. 189. 107655–107655.
4.
Li, Kaixiang, et al.. (2025). ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites. Journal of Molecular Biology. 437(6). 168978–168978. 2 indexed citations
5.
Jin, Junru, et al.. (2024). Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction. Journal of Chemical Information and Modeling. 64(15). 6230–6240. 5 indexed citations
6.
Wang, Ding, Junru Jin, Zhongshen Li, et al.. (2024). StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides. Bioinformatics. 40(2). 3 indexed citations
7.
Pang, Chao, et al.. (2023). DrugormerDTI: Drug Graphormer for drug–target interaction prediction. Computers in Biology and Medicine. 161. 106946–106946. 15 indexed citations
8.
Wang, Yu, et al.. (2023). MolCAP: Molecular Chemical reActivity Pretraining and prompted-finetuning enhanced molecular representation learning. Computers in Biology and Medicine. 167. 107666–107666. 3 indexed citations
9.
Wang, Y., Chao Pang, Yuzhe Wang, et al.. (2023). Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks. Nature Communications. 14(1). 6155–6155. 75 indexed citations
10.
Wang, Ruheng, Yi Jiang, Junru Jin, et al.. (2023). DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis. Nucleic Acids Research. 51(7). 3017–3029. 104 indexed citations breakdown →
11.
Yang, Xuetong, Junru Jin, Ruheng Wang, et al.. (2023). CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only. Journal of Chemical Information and Modeling. 64(7). 2807–2816. 18 indexed citations
12.
Jiang, Yi, Ruheng Wang, Junru Jin, et al.. (2023). Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction. Advanced Science. 10(11). e2206151–e2206151. 55 indexed citations
13.
Li, Zhongshen, Junru Jin, Yu Wang, et al.. (2023). ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides. Bioinformatics. 39(3). 15 indexed citations
14.
Jin, Junru, et al.. (2023). Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications. Computers in Biology and Medicine. 164. 107238–107238. 14 indexed citations
15.
Li, Zhongshen, et al.. (2023). PLPMpro: Enhancing promoter sequence prediction with prompt-learning based pre-trained language model. Computers in Biology and Medicine. 164. 107260–107260. 10 indexed citations
16.
Wei, Lesong, Xiucai Ye, Kai Zhang, et al.. (2022). SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning. Briefings in Bioinformatics. 24(1). 37 indexed citations
17.
Wang, Ruheng, Junru Jin, Quan Zou, Kenta Nakai, & Leyi Wei. (2022). Predicting protein–peptide binding residues via interpretable deep learning. Bioinformatics. 38(13). 3351–3360. 62 indexed citations
18.
Jin, Junru, Yingying Yu, & Leyi Wei. (2022). Mouse4mC-BGRU: Deep learning for predicting DNA N4-methylcytosine sites in mouse genome. Methods. 204. 258–262. 18 indexed citations
19.
Yu, Yingying, Wenjia He, Junru Jin, et al.. (2021). iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization. Bioinformatics. 37(24). 4603–4610. 38 indexed citations
20.
He, Wenjia, Yi Jiang, Junru Jin, et al.. (2021). Accelerating bioactive peptide discovery via mutual information-based meta-learning. Briefings in Bioinformatics. 23(1). 45 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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