Jun Huan

638 total citations
29 papers, 411 citations indexed

About

Jun Huan is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jun Huan has authored 29 papers receiving a total of 411 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 8 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jun Huan's work include Machine Learning in Bioinformatics (8 papers), Protein Structure and Dynamics (7 papers) and Bioinformatics and Genomic Networks (7 papers). Jun Huan is often cited by papers focused on Machine Learning in Bioinformatics (8 papers), Protein Structure and Dynamics (7 papers) and Bioinformatics and Genomic Networks (7 papers). Jun Huan collaborates with scholars based in United States, China and Netherlands. Jun Huan's co-authors include Wei Wang, Daniel Williams, Yi Jia, Alexander Tropsha, Deepak Bandyopadhyay, Jack Snoeyink, Jan F. Prins, Jintao Zhang, Hongliang Fei and Jingshan Huang and has published in prestigious journals such as BMC Bioinformatics, Journal of Theoretical Biology and Protein Science.

In The Last Decade

Jun Huan

28 papers receiving 388 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jun Huan United States	11	182	147	143	93	64	29	411
Vincenzo Bonnici Italy	11	226 1.2×	132 0.9×	145 1.0×	53 0.6×	22 0.3×	30	432
Patrick Hoffman United States	5	54 0.3×	126 0.9×	157 1.1×	70 0.8×	24 0.4×	8	330
Sean A. Irvine United Kingdom	7	268 1.5×	105 0.7×	70 0.5×	26 0.3×	44 0.7×	13	539
Liang Huai Yang China	10	152 0.8×	121 0.8×	32 0.2×	76 0.8×	112 1.8×	37	406
Liang Kong China	11	348 1.9×	193 1.3×	52 0.4×	34 0.4×	71 1.1×	33	598
Aaron Elkiss United States	6	111 0.6×	180 1.2×	39 0.3×	77 0.8×	85 1.3×	7	415
Achille Zappa Ireland	7	106 0.6×	76 0.5×	25 0.2×	18 0.2×	22 0.3×	14	277
ChengXiang Zhai United States	5	70 0.4×	117 0.8×	39 0.3×	20 0.2×	137 2.1×	11	288
Gilles Bisson France	9	100 0.5×	214 1.5×	55 0.4×	31 0.3×	86 1.3×	15	311
Faisal N. Abu-Khzam Lebanon	11	110 0.6×	120 0.8×	57 0.4×	29 0.3×	23 0.4×	47	490

Countries citing papers authored by Jun Huan

Since Specialization

Citations

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

Fields of papers citing papers by Jun Huan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Huan

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Huang, Jingshan, Glen M. Borchert, Dejing Dou, & Jun Huan. (2017). Bioinformatics in MicroRNA Research. Methods in molecular biology. 24 indexed citations

Lan, Chao, Jianxin Wang, & Jun Huan. (2016). Towards a theoretical understanding of negative transfer in collective matrix factorization. Uncertainty in Artificial Intelligence. 367–376. 3 indexed citations

Huang, Jingshan, Karen Eilbeck, Barry Smith, et al.. (2016). The Non-Coding RNA Ontology (NCRO): a comprehensive resource for the unification of non-coding RNA biology. Journal of Biomedical Semantics. 7(1). 24–24. 7 indexed citations

Huo, Hongwei, et al.. (2016). RefSelect: a reference sequence selection algorithm for planted (l, d) motif search. BMC Bioinformatics. 17(S9). 266–266. 4 indexed citations

Peng, Xiaoqing, Jianxin Wang, Jun Huan, & Fang‐Xiang Wu. (2016). Double-layer clustering method to predict protein complexes based on power-law distribution and protein sublocalization. Journal of Theoretical Biology. 395. 186–193. 6 indexed citations

Huan, Jun, Yuxin Bao, Yanyou Wu, et al.. (2014). Identification of quantitative trait loci conferring blast resistance in Bodao, a japonica rice landrace. Genetics and Molecular Research. 13(4). 9756–9765. 6 indexed citations

Huo, Hongwei, et al.. (2014). An efficient motif finding algorithm for large DNA data sets. 397–402. 6 indexed citations

Huo, Hongwei, et al.. (2013). StemFinder: An efficient algorithm for searching motif stems over large alphabets. 473–476. 2 indexed citations

Hasan, Mohammad Al, Jun Huan, Jake Y. Chen, & Mohammed J. Zaki. (2012). Biological Knowledge Discovery and Data Mining. Scientific Programming. 20(1). 1–2. 1 indexed citations

10.

Huan, Jun, et al.. (2010). Towards site-based protein functional annotations. International Journal of Data Mining and Bioinformatics. 4(4). 452–452. 2 indexed citations

11.

Jia, Yi & Jun Huan. (2010). Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism. BMC Bioinformatics. 11(S6). 12 indexed citations

12.

Lee, Doheon, Russ B. Altman, Min Song, & Jun Huan. (2009). Proceedings of the third international workshop on Data and text mining in bioinformatics. 1 indexed citations

13.

Huan, Jun, et al.. (2009). Feature Selection in the Tensor Product Feature Space. PubMed. 1004–1009. 10 indexed citations

14.

Jia, Yi, et al.. (2009). Towards comprehensive structural motif mining for better fold annotation in the "twilight zone" of sequence dissimilarity. BMC Bioinformatics. 10(S1). S46–S46. 25 indexed citations

15.

Bandyopadhyay, Deepak, Jun Huan, Jan M. Prins, et al.. (2009). Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications. Journal of Computer-Aided Molecular Design. 23(11). 785–797. 6 indexed citations

16.

Bandyopadhyay, Deepak, Jun Huan, Jan F. Prins, et al.. (2009). Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development. Journal of Computer-Aided Molecular Design. 23(11). 773–784. 11 indexed citations

17.

Fei, Hongliang & Jun Huan. (2008). Structure feature selection for graph classification. 991–1000. 21 indexed citations

18.

Williams, Daniel, Jun Huan, & Wei Wang. (2007). Graph Database Indexing Using Structured Graph Decomposition. 976–985. 108 indexed citations

19.

Bandyopadhyay, Deepak, Jun Huan, Jinze Liu, et al.. (2006). Structure‐based function inference using protein family‐specific fingerprints. Protein Science. 15(6). 1537–1543. 32 indexed citations

20.

Huan, Jun, Deepak Bandyopadhyay, Wei Wang, et al.. (2005). Comparing Graph Representations of Protein Structure for Mining Family-Specific Residue-Based Packing Motifs. Journal of Computational Biology. 12(6). 657–671. 49 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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