Lana Yeganova

1.1k total citations
38 papers, 596 citations indexed

About

Lana Yeganova is a scholar working on Artificial Intelligence, Molecular Biology and Management Science and Operations Research. According to data from OpenAlex, Lana Yeganova has authored 38 papers receiving a total of 596 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 24 papers in Molecular Biology and 4 papers in Management Science and Operations Research. Recurrent topics in Lana Yeganova's work include Biomedical Text Mining and Ontologies (24 papers), Topic Modeling (16 papers) and Natural Language Processing Techniques (15 papers). Lana Yeganova is often cited by papers focused on Biomedical Text Mining and Ontologies (24 papers), Topic Modeling (16 papers) and Natural Language Processing Techniques (15 papers). Lana Yeganova collaborates with scholars based in United States, France and Australia. Lana Yeganova's co-authors include W. John Wilbur, Sun Kim, Donald C. Comeau, Won Bae Kim, Haibin Liu, Zhiyong Lu, Rezarta Islamaj, Qingyu Chen, Xin Gao and Po‐Ting Lai and has published in prestigious journals such as Bioinformatics, The American Journal of Human Genetics and BMC Bioinformatics.

In The Last Decade

Lana Yeganova

34 papers receiving 582 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lana Yeganova United States 12 367 313 88 66 55 38 596
Meliha Yetisgen-Yildiz United States 15 498 1.4× 468 1.5× 36 0.4× 48 0.7× 38 0.7× 29 765
Po‐Ting Lai Taiwan 13 394 1.1× 378 1.2× 98 1.1× 28 0.4× 19 0.3× 39 676
Louise Deléger France 18 521 1.4× 429 1.4× 35 0.4× 27 0.4× 46 0.8× 46 844
Amber Stubbs United States 13 824 2.2× 471 1.5× 64 0.7× 22 0.3× 82 1.5× 19 978
Michael Lucas Australia 4 790 2.2× 456 1.5× 146 1.7× 61 0.9× 22 0.4× 14 1.1k
Ergin Soysal United States 10 314 0.9× 304 1.0× 25 0.3× 24 0.4× 34 0.6× 15 513
Robert Tinn United States 4 840 2.3× 483 1.5× 166 1.9× 61 0.9× 27 0.5× 5 1.1k
Naoto Usuyama United States 7 867 2.4× 500 1.6× 175 2.0× 63 1.0× 27 0.5× 14 1.2k
Nansu Zong United States 14 201 0.5× 322 1.0× 25 0.3× 162 2.5× 57 1.0× 46 644
裕二 池谷 United States 10 948 2.6× 534 1.7× 175 2.0× 71 1.1× 32 0.6× 19 1.3k

Countries citing papers authored by Lana Yeganova

Since Specialization
Citations

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

Fields of papers citing papers by Lana Yeganova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lana Yeganova

This figure shows the co-authorship network connecting the top 25 collaborators of Lana Yeganova. A scholar is included among the top collaborators of Lana Yeganova 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 Lana Yeganova. Lana Yeganova 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.
Neves, Mariana, Cristian Grozea, Philippe Thomas, et al.. (2024). Findings of the WMT 2024 Biomedical Translation Shared Task: Test Sets on Abstract Level. SPIRE - Sciences Po Institutional REpository. 124–138.
2.
Tian, Shubo, Qiao Jin, Lana Yeganova, et al.. (2023). Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Briefings in Bioinformatics. 25(1). 186 indexed citations
3.
Kim, Won Bae, Lana Yeganova, Donald C. Comeau, W. John Wilbur, & Zhiyong Lu. (2022). Towards a unified search: Improving PubMed retrieval with full text. Journal of Biomedical Informatics. 134. 104211–104211. 6 indexed citations
4.
Islamaj, Rezarta, Lana Yeganova, W. John Wilbur, et al.. (2021). Evolving use of ancestry, ethnicity, and race in genetics research—A survey spanning seven decades. The American Journal of Human Genetics. 108(12). 2215–2223. 22 indexed citations
5.
Kim, Sun, Lana Yeganova, Donald C. Comeau, W. John Wilbur, & Zhiyong Lu. (2018). PubMed Phrases, an open set of coherent phrases for searching biomedical literature. Scientific Data. 5(1). 180104–180104. 13 indexed citations
6.
Yeganova, Lana, et al.. (2018). Discovering themes in biomedical literature using a projection-based algorithm. BMC Bioinformatics. 19(1). 269–269. 1 indexed citations
7.
Yeganova, Lana, Won Bae Kim, Donald C. Comeau, W. John Wilbur, & Zhiyong Lu. (2018). A Field Sensor: computing the composition and intent of PubMed queries. Database. 2018. 3 indexed citations
8.
Kim, Sun, Haibin Liu, Lana Yeganova, & W. John Wilbur. (2015). Extracting drug–drug interactions from literature using a rich feature-based linear kernel approach. Journal of Biomedical Informatics. 55. 23–30. 122 indexed citations
9.
Islamaj, Rezarta, et al.. (2014). Finding abbreviations in biomedical literature: three BioC-compatible modules and four BioC-formatted corpora. Database. 2014(0). bau044–bau044. 10 indexed citations
10.
Liu, Wanli, Rezarta Islamaj, Sun Kim, et al.. (2013). Author name disambiguation for PubMed. Journal of the Association for Information Science and Technology. 65(4). 765–781. 61 indexed citations
11.
Islamaj, Rezarta, Yolanda Gil, Haym Hirsh, et al.. (2013). Reports on the 2012 AAAI Fall Symposium Series. AI Magazine. 34(1). 93–100. 2 indexed citations
12.
Yeganova, Lana, et al.. (2012). Information retrieval and knowledge discovery in biomedical text : papers from the AAAI Fall Symposium. 1 indexed citations
13.
Kim, Won Bae, Lana Yeganova, Donald C. Comeau, & W. John Wilbur. (2012). Identifying well-formed biomedical phrases in MEDLINE® text. Journal of Biomedical Informatics. 45(6). 1035–1041. 3 indexed citations
14.
Yeganova, Lana, Won Bae Kim, Donald C. Comeau, & W. John Wilbur. (2012). Finding biomedical categories in Medline®. Journal of Biomedical Semantics. 3(S3). S3–S3. 5 indexed citations
15.
Islamaj, Rezarta & Lana Yeganova. (2012). Topics in machine learning for biomedical literature analysis and text retrieval. Journal of Biomedical Semantics. 3(S3). S1–S1. 3 indexed citations
16.
Yeganova, Lana, Donald C. Comeau, Won Ho Kim, & W. John Wilbur. (2011). Text Mining Techniques for Leveraging Positively Labeled Data. Meeting of the Association for Computational Linguistics. 155–163. 13 indexed citations
17.
Yeganova, Lana, Donald C. Comeau, & W. John Wilbur. (2010). Identifying Abbreviation Definitions Machine Learning with Naturally Labeled Data. 10. 499–505. 3 indexed citations
18.
Yeganova, Lana, Donald C. Comeau, Won Bae Kim, & W. John Wilbur. (2008). How to interpret PubMed queries and why it matters. Journal of the American Society for Information Science and Technology. 60(2). 264–274. 14 indexed citations
19.
Yeganova, Lana, Larry Smith, & W. John Wilbur. (2004). Identification of related gene/protein names based on an HMM of name variations. Computational Biology and Chemistry. 28(2). 97–107. 16 indexed citations
20.
Yeganova, Lana, et al.. (2001). Robust set separation via exponentials. Nonlinear Analysis. 47(3). 1893–1904. 2 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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