Xiaoning Qian

4.9k total citations
186 papers, 2.4k citations indexed

About

Xiaoning Qian is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Xiaoning Qian has authored 186 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 85 papers in Molecular Biology, 50 papers in Artificial Intelligence and 34 papers in Computer Vision and Pattern Recognition. Recurrent topics in Xiaoning Qian's work include Bioinformatics and Genomic Networks (49 papers), Gene Regulatory Network Analysis (35 papers) and Gene expression and cancer classification (34 papers). Xiaoning Qian is often cited by papers focused on Bioinformatics and Genomic Networks (49 papers), Gene Regulatory Network Analysis (35 papers) and Gene expression and cancer classification (34 papers). Xiaoning Qian collaborates with scholars based in United States, China and Taiwan. Xiaoning Qian's co-authors include Edward R. Dougherty, Eleni Papadonikolaki, Byung-Jun Yoon, Man Li, Tom Li, Yan Liu, Susie Y. Dai, Raymundo Arróyave, Mingyuan Zhou and Shuai Huang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Xiaoning Qian

176 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Xiaoning Qian United States	23	680	462	349	312	244	186	2.4k
Mehmet Gönen Türkiye	26	843 1.2×	924 2.0×	902 2.6×	182 0.6×	501 2.1×	91	3.2k
Paul Bogdan United States	33	541 0.8×	376 0.8×	119 0.3×	128 0.4×	181 0.7×	184	3.4k
Shiyuan Wang China	35	699 1.0×	909 2.0×	488 1.4×	317 1.0×	80 0.3×	411	5.1k
Faisal Saeed Saudi Arabia	29	235 0.3×	901 2.0×	370 1.1×	126 0.4×	214 0.9×	196	3.0k
Abdul Sattar Australia	33	1.9k 2.7×	666 1.4×	152 0.4×	316 1.0×	664 2.7×	290	4.3k
Andreas Weber⋆ Germany	30	184 0.3×	446 1.0×	900 2.6×	95 0.3×	246 1.0×	144	3.3k
Binh P. Nguyen New Zealand	24	375 0.6×	418 0.9×	310 0.9×	52 0.2×	148 0.6×	118	1.8k
Ioannis Ch. Paschalidis United States	33	525 0.8×	1.2k 2.6×	133 0.4×	112 0.4×	300 1.2×	228	4.1k
Asifullah Khan Pakistan	45	1.2k 1.7×	1.8k 3.9×	1.9k 5.5×	157 0.5×	233 1.0×	205	6.2k

Countries citing papers authored by Xiaoning Qian

Since Specialization

Citations

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

Fields of papers citing papers by Xiaoning Qian

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaoning Qian

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

All Works

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20 of 20 papers shown

Brown, Timothy D., et al.. (2025). A composition-based predictive model for the transformation strain of NiTi shape memory alloys. Acta Materialia. 289. 120861–120861. 1 indexed citations

Trehern, William, et al.. (2023). An interpretable boosting-based predictive model for transformation temperatures of shape memory alloys. Computational Materials Science. 226. 112225–112225. 8 indexed citations

Nagashima, Yukihiro, Jashbir Singh, Rita Metrani, et al.. (2023). Genetic and geographical inputs that shape Metabolomic and transcriptomic profiles of melon fruits. Scientia Horticulturae. 321. 112337–112337. 2 indexed citations

Qian, Xiaoning, Li Lynn Tan, Shantenu Jha, et al.. (2023). Optimal decision-making in high-throughput virtual screening pipelines. Patterns. 4(11). 100875–100875. 5 indexed citations

Zhao, Guang, Edward R. Dougherty, Byung-Jun Yoon, Francis J. Alexander, & Xiaoning Qian. (2021). Efficient Active Learning for Gaussian Process Classification by Error Reduction. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 8 indexed citations

Huang, Shuai, et al.. (2021). SURVFIT: Doubly sparse rule learning for survival data. Journal of Biomedical Informatics. 117. 103691–103691. 7 indexed citations

Nagashima, Yukihiro, Jashbir Singh, Rita Metrani, et al.. (2020). Transition of aromatic volatile and transcriptome profiles during melon fruit ripening. Plant Science. 304. 110809–110809. 19 indexed citations

Zhou, Mingyuan, et al.. (2020). Learnable Bernoulli Dropout for Bayesian Deep Learning. International Conference on Artificial Intelligence and Statistics. 3905–3916. 1 indexed citations

Hajiramezanali, Ehsan, et al.. (2020). Bayesian Graph Neural Networks with Adaptive Connection Sampling. International Conference on Machine Learning. 1. 4094–4104. 16 indexed citations

10.

Yoon, Byung-Jun, et al.. (2020). ClusterM: a scalable algorithm for computational prediction of conserved protein complexes across multiple protein interaction networks. BMC Genomics. 21(S10). 615–615. 4 indexed citations

11.

Hajiramezanali, Ehsan, et al.. (2019). Semi-Implicit Graph Variational Auto-Encoders. arXiv (Cornell University). 32. 10712–10723. 18 indexed citations

12.

Hajiramezanali, Ehsan, et al.. (2019). Variational Graph Recurrent Neural Networks. Neural Information Processing Systems. 32. 10700–10710. 13 indexed citations

13.

Nagashima, Yukihiro, Zeyang Ma, Xiaoning Qian, et al.. (2019). Multiple Quality Control Mechanisms in the ER and TGN Determine Subcellular Dynamics and Salt-Stress Tolerance Function of KORRIGAN1. The Plant Cell. 32(2). 470–485. 24 indexed citations

14.

Jiang, Ziyu, Heather L. Evans, William B. Lober, et al.. (2019). A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images. Surgical Infections. 20(7). 555–565. 15 indexed citations

15.

Chang, Xiangyu, William B. Lober, Heather L. Evans, et al.. (2019). Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making. Surgical Infections. 20(7). 546–554. 10 indexed citations

16.

Hajiramezanali, Ehsan, Mahdi Imani, Ulisses Braga-Neto, Xiaoning Qian, & Edward R. Dougherty. (2019). Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty. BMC Genomics. 20(S6). 435–435. 16 indexed citations

17.

Qian, Xiaoning, et al.. (2019). Optimal clustering with missing values. BMC Bioinformatics. 20(S12). 321–321. 15 indexed citations

18.

Li, Weizhi, Xiaoning Qian, & Jim Ji. (2017). Noise-tolerant deep learning for histopathological image segmentation. 3075–3079. 7 indexed citations

19.

Qian, Xiaoning & Edward R. Dougherty. (2016). Bayesian Regression With Network Prior: Optimal Bayesian Filtering Perspective. IEEE Transactions on Signal Processing. 64(23). 6243–6253. 18 indexed citations

20.

Huang, Shuai, et al.. (2015). A Scalable Algorithm for Structured Kernel Feature Selection. International Conference on Artificial Intelligence and Statistics. 781–789. 1 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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