Xulong Wang

750 total citations
44 papers, 431 citations indexed

About

Xulong Wang is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Xulong Wang has authored 44 papers receiving a total of 431 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 9 papers in Artificial Intelligence and 6 papers in Genetics. Recurrent topics in Xulong Wang's work include Myeloproliferative Neoplasms: Diagnosis and Treatment (6 papers), Machine Learning in Healthcare (6 papers) and Dementia and Cognitive Impairment Research (6 papers). Xulong Wang is often cited by papers focused on Myeloproliferative Neoplasms: Diagnosis and Treatment (6 papers), Machine Learning in Healthcare (6 papers) and Dementia and Cognitive Impairment Research (6 papers). Xulong Wang collaborates with scholars based in China, United Kingdom and United States. Xulong Wang's co-authors include Darwin K. Berg, Po Yang, Natalia V. Gounko, Kerri A. Massey, Adrian F. Lozada, Jingjing Duan, Jun Qi, Zhaoping Liu, Yun Yang and Renchang Zhao and has published in prestigious journals such as Journal of Clinical Oncology, Journal of Neuroscience and Blood.

In The Last Decade

Xulong Wang

38 papers receiving 423 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xulong Wang China 11 161 93 47 37 33 44 431
Michael J. Rigby United States 10 104 0.6× 71 0.8× 43 0.9× 32 0.9× 35 1.1× 21 403
Zihuai He United States 15 348 2.2× 39 0.4× 59 1.3× 46 1.2× 24 0.7× 66 714
Hua Tao China 14 274 1.7× 61 0.7× 27 0.6× 25 0.7× 8 0.2× 38 518
Tianzhou Ma United States 10 235 1.5× 52 0.6× 21 0.4× 95 2.6× 17 0.5× 49 605
Tianyi Chen China 14 54 0.3× 40 0.4× 43 0.9× 77 2.1× 24 0.7× 37 430
Xiaoxing Liu China 14 553 3.4× 57 0.6× 37 0.8× 41 1.1× 39 1.2× 45 873
Tonatiuh Peña Centeno Germany 7 274 1.7× 52 0.6× 31 0.7× 45 1.2× 19 0.6× 13 398
Brian M. Schilder United States 13 258 1.6× 43 0.5× 134 2.9× 28 0.8× 14 0.4× 20 532
Janko Dietzsch Germany 15 369 2.3× 46 0.5× 34 0.7× 39 1.1× 18 0.5× 28 678
Hemi Malkki Netherlands 11 156 1.0× 112 1.2× 54 1.1× 134 3.6× 7 0.2× 68 583

Countries citing papers authored by Xulong Wang

Since Specialization
Citations

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

Fields of papers citing papers by Xulong Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xulong Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Xulong Wang. A scholar is included among the top collaborators of Xulong Wang 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 Xulong Wang. Xulong Wang 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.
Mascarenhas, John, Raajit K. Rampal, Prithviraj Bose, et al.. (2024). Phase 3 randomized double-blind study evaluating selinexor, an XPO1 inhibitor, plus ruxolitinib in JAKi-naïve myelofibrosis.. Journal of Clinical Oncology. 42(16_suppl). TPS6594–TPS6594. 1 indexed citations
2.
Tantravahi, Srinivas K., Ashwin Kishtagari, Sanjay Mohan, et al.. (2024). MPN-527 Selinexor and Ruxolitinib Impact on Symptom Burden in Patients With Myelofibrosis Is Potentially Driven by Inhibition of NF-κB and Activation of P53 Pathways. Clinical Lymphoma Myeloma & Leukemia. 24. S437–S438. 1 indexed citations
3.
Wang, Xulong, et al.. (2024). Wearable-Sensor-Based Weakly Supervised Parkinson’s Disease Assessment with Data Augmentation. Sensors. 24(4). 1196–1196. 5 indexed citations
4.
Wang, Xulong, Jun Qi, Yun Yang, et al.. (2024). Selecting and Evaluating Key MDS-UPDRS Activities Using Wearable Devices for Parkinson's Disease Self-Assessment. ODU Digital Commons (Old Dominion University). 1. 177–189. 7 indexed citations
5.
Wang, Xulong, et al.. (2024). Unsupervised Transfer Aided Lifelong Regression for Learning New Tasks Without Target Output. IEEE Transactions on Knowledge and Data Engineering. 36(9). 4981–4995. 2 indexed citations
6.
Wang, Xulong, et al.. (2024). Integrating Visualised Automatic Temporal Relation Graph into Multi-Task Learning for Alzheimer's Disease Progression Prediction. IEEE Transactions on Knowledge and Data Engineering. 36(10). 5206–5220. 5 indexed citations
7.
Wang, Xulong, et al.. (2024). Informative relationship multi-task learning: Exploring pairwise contribution across tasks’ sharing knowledge. Knowledge-Based Systems. 301. 112187–112187. 1 indexed citations
8.
Wang, Xulong, et al.. (2024). Machine-Learning-Based Performance Prediction for CDN Cache Groups in Meta Computing. IEEE Internet of Things Journal. 12(10). 13612–13624.
9.
10.
11.
Wang, Xulong, et al.. (2023). Empirical Analysis of Regularised Multi-Task Learning for Modelling Alzheimer’s Disease Progression. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 21. 4444–4451. 1 indexed citations
12.
Wang, Xulong, et al.. (2022). Oscillation Theorems for Two Classes of Fractional Neutral Differential Equations. Journal of Applied Mathematics and Physics. 10(10). 3037–3052. 1 indexed citations
13.
Pang, Zhen, Xiang Wang, Xulong Wang, et al.. (2021). A Multi-modal Data Platform for Diagnosis and Prediction of Alzheimer’s Disease Using Machine Learning Methods. Mobile Networks and Applications. 26(6). 2341–2352. 14 indexed citations
14.
Uyar, Aslı, et al.. (2020). Staging Alzheimer’s Disease in the Brain and Retina of B6.APP/PS1 Mice by Transcriptional Profiling. Journal of Alzheimer s Disease. 73(4). 1421–1434. 19 indexed citations
15.
Yang, Po, Jun Qi, Shuhao Zhang, et al.. (2020). Feasibility study of mitigation and suppression strategies for controlling COVID-19 outbreaks in London and Wuhan. PLoS ONE. 15(8). e0236857–e0236857. 30 indexed citations
16.
Wang, Xulong, Vivek M. Philip, Guruprasad Ananda, et al.. (2018). A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer’s Disease. Genetics. 209(1). 51–64. 15 indexed citations
17.
Wang, Xulong, Thomas J. Sproule, Gregory J. Christianson, et al.. (2017). Precocious Interleukin 21 Expression in Naive Mice Identifies a Natural Helper Cell Population in Autoimmune Disease. Cell Reports. 21(1). 208–221. 16 indexed citations
18.
Wang, Xulong, et al.. (2013). Activation of α7‐containing nicotinic receptors on astrocytes triggers AMPA receptor recruitment to glutamatergic synapses. Journal of Neurochemistry. 127(5). 632–643. 37 indexed citations
19.
Lozada, Adrian F., Xulong Wang, Natalia V. Gounko, et al.. (2012). Glutamatergic Synapse Formation is Promoted by α7-Containing Nicotinic Acetylcholine Receptors. Journal of Neuroscience. 32(22). 7651–7661. 116 indexed citations
20.
Wang, Xulong, et al.. (2009). [Modulation and function of calcium signaling in retinal horizontal cells.].. PubMed. 61(1). 1–8. 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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