Leo J. Lee

8.4k total citations · 3 hit papers
16 papers, 5.1k citations indexed

About

Leo J. Lee is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Leo J. Lee has authored 16 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 3 papers in Oncology and 2 papers in Immunology. Recurrent topics in Leo J. Lee's work include RNA Research and Splicing (10 papers), RNA modifications and cancer (9 papers) and RNA and protein synthesis mechanisms (6 papers). Leo J. Lee is often cited by papers focused on RNA Research and Splicing (10 papers), RNA modifications and cancer (9 papers) and RNA and protein synthesis mechanisms (6 papers). Leo J. Lee collaborates with scholars based in Canada, United States and China. Leo J. Lee's co-authors include Brendan J. Frey, Benjamin J. Blencowe, Qun Pan, Ofer Shai, Hui Xiong, Serge Gueroussov, Michael K. K. Leung, Quaid Morris, Nebojša Jojić and Yoseph Barash and has published in prestigious journals such as Science, Nature Genetics and Genes & Development.

In The Last Decade

Leo J. Lee

16 papers receiving 5.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing

2008 2.8k citations Qun Pan, Ofer Shai et al. Nature Genetics profile →
The human splicing code reveals new insights into the genetic determinants of disease

2014 829 citations Hui Xiong, Babak Alipanahi et al. Science profile →
The Evolutionary Landscape of Alternative Splicing in Vertebrate Species

2012 710 citations Nuno L. Barbosa‐Morais, Manuel Irimia et al. Science profile →

Peers

Leo J. Lee

MB

CR

GENET

PS

IMMUN

Yoseph Barash United States

Julien Gagneur Germany

Joshua J. Waterfall United States

Carsten O. Daub Japan

Zhaolei Zhang Canada

Sven Rahmann Germany

Yan Cui United States

Giulio Pavesi Italy

Hilmar Lapp United States

Hamed S. Najafabadi Canada

Yoseph Barash United States

Leo J. Lee

3.0k ×0.7

MB

481 ×0.6

CR

386 ×0.9

GENET

121 ×0.5

PS

166 ×0.8

IMMUN

Citations per year, relative to Leo J. Lee Leo J. Lee (= 1×) peers Yoseph Barash

Countries citing papers authored by Leo J. Lee

Since Specialization

Citations

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

Fields of papers citing papers by Leo J. Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leo J. Lee. 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 Leo J. Lee. The network helps show where Leo J. Lee may publish in the future.

Co-authorship network of co-authors of Leo J. Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Leo J. Lee. A scholar is included among the top collaborators of Leo J. Lee 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 Leo J. Lee. Leo J. Lee 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:

16 of 16 papers shown

1.

Young, Adamo, et al.. (2022). A graph neural network approach for molecule carcinogenicity prediction. Bioinformatics. 38(Supplement_1). i84–i91. 22 indexed citations

2.

Liu, Geng, Guixue Hou, Haitao Xiang, et al.. (2022). IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry. Biomolecules. 12(4). 579–579. 5 indexed citations

3.

Tang, Yunxia, et al.. (2020). The co-stimulation of anti-CD28 and IL-2 enhances the sensitivity of ELISPOT assays for detection of neoantigen-specific T cells in PBMC. Journal of Immunological Methods. 484-485. 112831–112831. 6 indexed citations

4.

Li, Youping, et al.. (2019). Abstract 3383: EPIC: MHC-I epitope prediction integrating mass spectrometry derived motifs and tissue-specific expression profiles. 3383–3383. 1 indexed citations

5.

Peng, Lihua, Leo J. Lee, Heng Xiong, et al.. (2016). Characterization of RNA editome in primary and metastatic lung adenocarcinomas. Oncotarget. 8(7). 11517–11529. 7 indexed citations

6.

Xiong, Hui, Babak Alipanahi, Leo J. Lee, et al.. (2014). The human splicing code reveals new insights into the genetic determinants of disease. Science. 347(6218). 1254806–1254806. 829 indexed citations breakdown →

7.

Leung, Michael K. K., Hui Xiong, Leo J. Lee, & Brendan J. Frey. (2014). Deep learning of the tissue-regulated splicing code. Bioinformatics. 30(12). i121–i129. 329 indexed citations

8.

Barash, Yoseph, Jorge Vaquero-Garcia, Juan González‐Vallinas, et al.. (2013). AVISPA: a web tool for the prediction and analysis of alternative splicing. Genome biology. 14(10). R114–R114. 31 indexed citations

9.

Barbosa‐Morais, Nuno L., Manuel Irimia, Qun Pan, et al.. (2012). The Evolutionary Landscape of Alternative Splicing in Vertebrate Species. Science. 338(6114). 1587–1593. 710 indexed citations breakdown →

10.

Kakaradov, Boyko, Hui Xiong, Leo J. Lee, Nebojša Jojić, & Brendan J. Frey. (2012). Challenges in estimating percent inclusion of alternatively spliced junctions from RNA-seq data. BMC Bioinformatics. 13(S6). S11–S11. 40 indexed citations

11.

Lahiry, Piya, Leo J. Lee, Brendan J. Frey, et al.. (2011). Transcriptional Profiling of Endocrine Cerebro-Osteodysplasia Using Microarray and Next-Generation Sequencing. PLoS ONE. 6(9). e25400–e25400. 9 indexed citations

12.

Ramani, Arun, John A. Calarco, Qun Pan, et al.. (2010). Genome-wide analysis of alternative splicing in Caenorhabditis elegans. Genome Research. 21(2). 342–348. 128 indexed citations

13.

Blencowe, Benjamin J., et al.. (2009). Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes. Genes & Development. 23(12). 1379–1386. 127 indexed citations

14.

Pan, Qun, Ofer Shai, Leo J. Lee, Brendan J. Frey, & Benjamin J. Blencowe. (2009). Addendum: Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature Genetics. 41(6). 762–762. 17 indexed citations

15.

Pan, Qun, Ofer Shai, Leo J. Lee, Brendan J. Frey, & Benjamin J. Blencowe. (2008). Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature Genetics. 40(12). 1413–1415. 2791 indexed citations breakdown →

16.

Deng, Li, Leo J. Lee, Hagai Attias, & Alex Acero. (2006). Adaptive Kalman Filtering and Smoothing for Tracking Vocal Tract Resonances Using a Continuous-Valued Hidden Dynamic Model. IEEE Transactions on Audio Speech and Language Processing. 15(1). 13–23. 35 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.

Explore authors with similar magnitude of impact