Lydia Ng

39.5k total citations · 2 hit papers
49 papers, 7.9k citations indexed

About

Lydia Ng is a scholar working on Molecular Biology, Biophysics and Cognitive Neuroscience. According to data from OpenAlex, Lydia Ng has authored 49 papers receiving a total of 7.9k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 16 papers in Biophysics and 12 papers in Cognitive Neuroscience. Recurrent topics in Lydia Ng's work include Single-cell and spatial transcriptomics (17 papers), Cell Image Analysis Techniques (15 papers) and Gene expression and cancer classification (10 papers). Lydia Ng is often cited by papers focused on Single-cell and spatial transcriptomics (17 papers), Cell Image Analysis Techniques (15 papers) and Gene expression and cancer classification (10 papers). Lydia Ng collaborates with scholars based in United States, Spain and United Kingdom. Lydia Ng's co-authors include Michael Hawrylycz, Susan M. Sunkin, Allan R. Jones, Hongkui Zeng, Ed S. Lein, Seung Wook Oh, Linda Madisen, Hong Gu, Theresa A. Zwingman and Hatim A. Zariwala and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Lydia Ng

46 papers receiving 7.8k citations

Hit Papers

A robust and high-throughput Cre reporting and characteri... 2009 2026 2014 2020 2009 2012 1000 2.0k 3.0k 4.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lydia Ng United States 22 3.6k 2.6k 1.8k 750 749 49 7.9k
Michael Hawrylycz United States 32 4.1k 1.1× 2.4k 0.9× 1.7k 1.0× 848 1.1× 773 1.0× 64 8.6k
Susan M. Sunkin United States 28 5.3k 1.5× 2.2k 0.9× 1.7k 0.9× 1.1k 1.5× 781 1.0× 44 10.0k
Linda Madisen United States 27 4.1k 1.1× 2.5k 0.9× 1.3k 0.7× 905 1.2× 588 0.8× 40 8.7k
Seung Wook Oh United States 15 3.3k 0.9× 1.9k 0.7× 1.0k 0.6× 577 0.8× 574 0.8× 32 6.8k
Hatim A. Zariwala United States 11 2.5k 0.7× 1.9k 0.7× 1.4k 0.8× 551 0.7× 544 0.7× 13 6.0k
Allan R. Jones United States 30 4.5k 1.2× 2.2k 0.8× 1.3k 0.7× 897 1.2× 759 1.0× 54 8.8k
Shiaoching Gong United States 30 4.2k 1.1× 3.3k 1.3× 1.8k 1.0× 1.6k 2.1× 733 1.0× 60 8.7k
Ed S. Lein United States 30 4.6k 1.3× 3.4k 1.3× 2.9k 1.6× 1.3k 1.7× 1.1k 1.4× 65 10.3k
Kazunari Miyamichi Japan 25 2.8k 0.8× 3.3k 1.3× 1.9k 1.0× 730 1.0× 323 0.4× 50 7.9k
Theresa A. Zwingman United States 13 3.2k 0.9× 2.2k 0.8× 873 0.5× 805 1.1× 593 0.8× 15 6.3k

Countries citing papers authored by Lydia Ng

Since Specialization
Citations

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

Fields of papers citing papers by Lydia Ng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lydia Ng

This figure shows the co-authorship network connecting the top 25 collaborators of Lydia Ng. A scholar is included among the top collaborators of Lydia Ng 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 Lydia Ng. Lydia Ng 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.
Kunst, Michael, Shenqin Yao, Nicholas A. Lusk, et al.. (2025). Data-driven fine-grained region discovery in the mouse brain with transformers. Nature Communications. 16(1). 8536–8536.
2.
Trouvé, Alain, Laurent Younès, Michael Kunst, et al.. (2024). Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections. Nature Communications. 15(1). 3530–3530. 3 indexed citations
3.
Hawrylycz, Michael, Eitan S Kaplan, Kyle J. Travaglini, et al.. (2024). SEA-AD is a multimodal cellular atlas and resource for Alzheimer’s disease. Nature Aging. 4(10). 1331–1334. 5 indexed citations
4.
Mehta, Ketan, et al.. (2023). Online conversion of reconstructed neural morphologies into standardized SWC format. Nature Communications. 14(1). 7429–7429. 7 indexed citations
5.
Anderson, K., Julie A. Harris, Lydia Ng, et al.. (2021). Highlights from the Era of Open Source Web-Based Tools. Journal of Neuroscience. 41(5). 927–936. 12 indexed citations
6.
Tappan, Susan, Brian S. Eastwood, Nathan O’Connor, et al.. (2019). Automatic navigation system for the mouse brain. The Journal of Comparative Neurology. 527(13). 2200–2211. 20 indexed citations
7.
Billeh, Yazan N., Alexander V. Rodriguez, Michele Bellesi, et al.. (2016). Effects of Chronic Sleep Restriction during Early Adolescence on the Adult Pattern of Connectivity of Mouse Secondary Motor Cortex. eNeuro. 3(2). ENEURO.0053–16.2016. 18 indexed citations
8.
Feng, David, Chris Lau, Lydia Ng, et al.. (2015). Exploration and visualization of connectivity in the adult mouse brain. Methods. 73. 90–97. 11 indexed citations
9.
Kuan, Leonard, Yang Li, Chris Lau, et al.. (2014). Neuroinformatics of the Allen Mouse Brain Connectivity Atlas. Methods. 73. 4–17. 131 indexed citations
10.
Harris, Julie A., Karla E. Hirokawa, Staci A. Sorensen, et al.. (2014). Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation. Frontiers in Neural Circuits. 8. 76–76. 305 indexed citations
11.
Kim, Yongsoo, Kannan Umadevi Venkataraju, Kith Pradhan, et al.. (2014). Mapping Social Behavior-Induced Brain Activation at Cellular Resolution in the Mouse. Cell Reports. 10(2). 292–305. 215 indexed citations
12.
Hawrylycz, Mike, Lydia Ng, Damon T. Page, et al.. (2011). Multi-scale correlation structure of gene expression in the brain. Neural Networks. 24(9). 933–942. 27 indexed citations
13.
Bernard, Amy, Carol L. Thompson, Lydia Ng, et al.. (2011). Cell‐type‐specific consequences of reelin deficiency in the mouse neocortex, hippocampus, and amygdala. The Journal of Comparative Neurology. 519(11). 1 indexed citations
14.
Ng, Lydia, Amy Bernard, Chris Lau, et al.. (2009). An anatomic gene expression atlas of the adult mouse brain. Nature Neuroscience. 12(3). 356–362. 211 indexed citations
15.
Hawrylycz, Mike, Amy Bernard, Chris Lau, et al.. (2009). Areal and laminar differentiation in the mouse neocortex using large scale gene expression data. Methods. 50(2). 113–121. 30 indexed citations
16.
Quina, Lely A., Shirong Wang, Lydia Ng, & Eric E. Turner. (2009). Brn3a and Nurr1 Mediate a Gene Regulatory Pathway for Habenula Development. Journal of Neuroscience. 29(45). 14309–14322. 93 indexed citations
17.
Ng, Lydia, Chris Lau, Susan M. Sunkin, et al.. (2009). Surface-based mapping of gene expression and probabilistic expression maps in the mouse cortex. Methods. 50(2). 55–62. 14 indexed citations
18.
Thompson, Carol L., Dev S. Pathak, Andreas Jeromin, et al.. (2008). Genomic Anatomy of the Hippocampus. Neuron. 60(6). 1010–1021. 304 indexed citations
19.
Kinahan, Paul E., Adam Alessio, Scott D. Wollenweber, et al.. (2007). Motion-free PET: Compensating for patient respiration in whole-body PET/CT imaging. 48(3). 1137–53.
20.
Pathak, Dev S., et al.. (2003). Quantitative image analysis: software systems in drug development trials. Drug Discovery Today. 8(10). 451–458. 15 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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