Li‐Lian Yuan

925 total citations
28 papers, 660 citations indexed

About

Li‐Lian Yuan is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Physiology. According to data from OpenAlex, Li‐Lian Yuan has authored 28 papers receiving a total of 660 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cellular and Molecular Neuroscience, 14 papers in Molecular Biology and 7 papers in Physiology. Recurrent topics in Li‐Lian Yuan's work include Neuroscience and Neuropharmacology Research (13 papers), Ion channel regulation and function (5 papers) and Tryptophan and brain disorders (4 papers). Li‐Lian Yuan is often cited by papers focused on Neuroscience and Neuropharmacology Research (13 papers), Ion channel regulation and function (5 papers) and Tryptophan and brain disorders (4 papers). Li‐Lian Yuan collaborates with scholars based in United States, China and Sweden. Li‐Lian Yuan's co-authors include Théoden I. Netoff, Xiaofeng Yang, Steven M. Rothman, M Parent, Junfeng Su, Barry Ganetzky, Lidan Wang, Lang Wang, Vanja Đurić and Sarah Clayton and has published in prestigious journals such as Science, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Li‐Lian Yuan

27 papers receiving 654 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Li‐Lian Yuan United States 13 331 260 138 86 63 28 660
Jiaqian Ren United States 12 208 0.6× 216 0.8× 105 0.8× 76 0.9× 70 1.1× 18 546
Daniela Valadão Rosa Brazil 16 232 0.7× 167 0.6× 64 0.5× 135 1.6× 120 1.9× 44 613
Renata Leke Brazil 16 349 1.1× 219 0.8× 141 1.0× 187 2.2× 114 1.8× 22 861
Daniel Herrera United States 12 324 1.0× 245 0.9× 176 1.3× 32 0.4× 88 1.4× 29 864
Rebeca Martínez-Turrillas Spain 12 459 1.4× 323 1.2× 105 0.8× 38 0.4× 42 0.7× 14 660
Robert J. Kotloski United States 12 422 1.3× 238 0.9× 116 0.8× 185 2.2× 62 1.0× 23 704
Gabriel Maisonnave Arisi Brazil 16 341 1.0× 181 0.7× 79 0.6× 189 2.2× 75 1.2× 21 814
Nobue Kitanaka Japan 18 473 1.4× 446 1.7× 85 0.6× 43 0.5× 105 1.7× 63 1.0k
Lilah Toker Israel 16 200 0.6× 307 1.2× 89 0.6× 137 1.6× 112 1.8× 27 757
Sarah Lane United States 12 679 2.1× 416 1.6× 140 1.0× 32 0.4× 109 1.7× 17 998

Countries citing papers authored by Li‐Lian Yuan

Since Specialization
Citations

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

Fields of papers citing papers by Li‐Lian Yuan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Li‐Lian Yuan

This figure shows the co-authorship network connecting the top 25 collaborators of Li‐Lian Yuan. A scholar is included among the top collaborators of Li‐Lian Yuan 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 Li‐Lian Yuan. Li‐Lian Yuan 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.
He, Jian, Yanling Wu, Li‐Lian Yuan, et al.. (2025). An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph. Journal of Pharmaceutical Analysis. 15(8). 101347–101347.
2.
Xue, Yu, et al.. (2025). Evolutionary neural architecture search based on pre-trained surrogate model. Applied Soft Computing. 186. 114145–114145. 1 indexed citations
3.
Rhodes, Justin S., et al.. (2024). Estrogen-mediated individual differences in female rat voluntary running behavior. Journal of Applied Physiology. 136(3). 592–605. 3 indexed citations
4.
Love, C.E., et al.. (2023). Risk of bias assessment tool for systematic review and meta-analysis of the gut microbiome. SHILAP Revista de lepidopterología. 4. e13–e13. 2 indexed citations
5.
Yuan, Li‐Lian, et al.. (2023). The persistence of stress-induced physical inactivity in rats: an investigation of central monoamine neurotransmitters and skeletal muscle oxidative stress. Frontiers in Behavioral Neuroscience. 17. 1169151–1169151. 3 indexed citations
6.
Yuan, Li‐Lian, et al.. (2022). Short-term Traffic Flow Prediction by Graph Deep Learning with Spatial Temporal Modeling. 172–177. 5 indexed citations
7.
Fan, Jun, Lingling Yu, Jing Ren, et al.. (2021). [Chaihu Guizhi decoction produces antidepressant-like effects via sirt1-p53 signaling pathway].. PubMed. 41(3). 399–405. 5 indexed citations
8.
Wauson, Eric, et al.. (2020). Corticosterone as a Potential Confounding Factor in Delineating Mechanisms Underlying Ketamine’s Rapid Antidepressant Actions. Frontiers in Pharmacology. 11. 590221–590221. 9 indexed citations
9.
Wang, Jinzhao, Cheng Long, Kaiyuan Li, et al.. (2018). Potent block of potassium channels by MEK inhibitor U0126 in primary cultures and brain slices. Scientific Reports. 8(1). 8808–8808. 11 indexed citations
10.
Đurić, Vanja, et al.. (2016). Comorbidity Factors and Brain Mechanisms Linking Chronic Stress and Systemic Illness. Neural Plasticity. 2016. 1–16. 59 indexed citations
11.
Yuan, Li‐Lian, Eric Wauson, & Vanja Đurić. (2016). Kinase-mediated signaling cascades in mood disorders and antidepressant treatment. Journal of Neurogenetics. 30(3-4). 178–184. 7 indexed citations
12.
Pisansky, Marc T., Robert J. Wickham, Stephanie J.B. Fretham, et al.. (2013). Iron deficiency with or without anemia impairs prepulse inhibition of the startle reflex. Hippocampus. 23(10). 952–962. 42 indexed citations
13.
Cheng, Shaowu, Dongfeng Cao, David A. Hottman, et al.. (2013). Farnesyltransferase Haplodeficiency Reduces Neuropathology and Rescues Cognitive Function in a Mouse Model of Alzheimer Disease. Journal of Biological Chemistry. 288(50). 35952–35960. 35 indexed citations
14.
Yang, Xiaofeng, et al.. (2012). Levetiracetam has an activity‐dependent effect on inhibitory transmission. Epilepsia. 53(3). 469–476. 46 indexed citations
15.
Yang, Xiaofeng, et al.. (2011). A new mechanism for antiepileptic drug action: vesicular entry may mediate the effects of levetiracetam. Journal of Neurophysiology. 106(3). 1227–1239. 86 indexed citations
16.
Wang, Lang, et al.. (2010). Dendritic mechanisms controlling the threshold and timing requirement of synaptic plasticity. Hippocampus. 21(3). 288–297. 26 indexed citations
17.
Parent, M, Lidan Wang, Junfeng Su, Théoden I. Netoff, & Li‐Lian Yuan. (2009). Identification of the Hippocampal Input to Medial Prefrontal Cortex In Vitro. Cerebral Cortex. 20(2). 393–403. 119 indexed citations
18.
Yuan, Li‐Lian, Andreas Jeromin, José J. Rodrı́guez, et al.. (2006). Manipulating Kv4.2 identifies a specific component of hippocampal pyramidal neuron A‐current that depends upon Kv4.2 expression. Journal of Neurochemistry. 99(4). 1207–1223. 18 indexed citations
19.
Yuan, Li‐Lian & Barry Ganetzky. (1999). A Glial-Neuronal Signaling Pathway Revealed by Mutations in a Neurexin-Related Protein. Science. 283(5406). 1343–1345. 47 indexed citations
20.
Yuan, Li‐Lian & Xiong‐Li Yang. (1997). Selective suppression of rod signal transmission by cobalt ions of low levels in carp retina. Science in China Series C Life Sciences. 40(2). 128–136. 4 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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