Go Eun Heo

429 total citations
23 papers, 272 citations indexed

About

Go Eun Heo is a scholar working on Molecular Biology, Artificial Intelligence and Information Systems. According to data from OpenAlex, Go Eun Heo has authored 23 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 15 papers in Artificial Intelligence and 3 papers in Information Systems. Recurrent topics in Go Eun Heo's work include Biomedical Text Mining and Ontologies (18 papers), Semantic Web and Ontologies (7 papers) and Advanced Text Analysis Techniques (7 papers). Go Eun Heo is often cited by papers focused on Biomedical Text Mining and Ontologies (18 papers), Semantic Web and Ontologies (7 papers) and Advanced Text Analysis Techniques (7 papers). Go Eun Heo collaborates with scholars based in South Korea, Australia and United States. Go Eun Heo's co-authors include Min Song, Da-Hee Lee, Won Chul Kim, Su Yeon Kim, Jeong‐Hoon Lee, Chaomei Chen, Yoo Kyung Jeong, Ying Ding, Qing Xie and Karin Verspoor and has published in prestigious journals such as BMC Bioinformatics, Scientometrics and Journal of Biomedical Informatics.

In The Last Decade

Go Eun Heo

19 papers receiving 249 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Go Eun Heo South Korea	9	138	123	38	28	27	23	272
Pradeep Muthukrishnan United States	7	44 0.3×	260 2.1×	75 2.0×	8 0.3×	26 1.0×	11	349
Angelo A. Salatino United Kingdom	11	64 0.5×	226 1.8×	52 1.4×	2 0.1×	23 0.9×	31	339
Amjad Abu-Jbara United States	9	86 0.6×	440 3.6×	105 2.8×	5 0.2×	42 1.6×	16	506
Cédrick Fairon Belgium	9	76 0.6×	345 2.8×	41 1.1×	14 0.5×	3 0.1×	52	465
Kyo Kageura Japan	9	92 0.7×	447 3.6×	92 2.4×	6 0.2×	38 1.4×	92	605
Jennifer D’Souza Germany	11	157 1.1×	322 2.6×	63 1.7×	4 0.1×	9 0.3×	37	374
Erik Velldal Norway	14	56 0.4×	467 3.8×	28 0.7×	3 0.1×	4 0.1×	45	525
Maria Skeppstedt Sweden	14	363 2.6×	546 4.4×	34 0.9×	8 0.3×	5 0.2×	58	636
Tirthankar Ghosal India	11	29 0.2×	298 2.4×	163 4.3×	6 0.2×	26 1.0×	59	398
Fidelia Ibekwe-Sanjuan France	9	25 0.2×	117 1.0×	61 1.6×	5 0.2×	13 0.5×	40	216

Countries citing papers authored by Go Eun Heo

Since Specialization

Citations

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

Fields of papers citing papers by Go Eun Heo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Go Eun Heo. 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 Go Eun Heo. The network helps show where Go Eun Heo may publish in the future.

Co-authorship network of co-authors of Go Eun Heo

This figure shows the co-authorship network connecting the top 25 collaborators of Go Eun Heo. A scholar is included among the top collaborators of Go Eun Heo 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 Go Eun Heo. Go Eun Heo 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:

20 of 20 papers shown

Heo, Go Eun, Young Soo Ko, Qing Xie, & Min Song. (2023). High acknowledgement index: Characterizing research supporters with factors of acknowledgement affecting paper citation counts. Journal of Informetrics. 17(4). 101447–101447.

Lee, Eun Byul, Go Eun Heo, Chang Choi, & Min Song. (2022). MLM-based typographical error correction of unstructured medical texts for named entity recognition. BMC Bioinformatics. 23(1). 486–486. 7 indexed citations

Xie, Qing, et al.. (2020). Literature based discovery of alternative TCM medicine for adverse reactions to depression drugs. BMC Bioinformatics. 21(S5). 405–405. 8 indexed citations

Heo, Go Eun & Qing Xie. (2019). A Hybrid Semantic Relatedness Algorithm by Entity Co-Occurrence and Specialized Word Embeddings. 1–2. 1 indexed citations

Heo, Go Eun, Qing Xie, Min Song, & Jeong‐Hoon Lee. (2019). Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer’s disease. BMC Medical Informatics and Decision Making. 19(S5). 240–240. 6 indexed citations

Song, Min, Seung Han Baek, Go Eun Heo, & Jeong‐Hoon Lee. (2019). Inferring Drug-Protein–Side Effect Relationships from Biomedical Text. Genes. 10(2). 159–159. 6 indexed citations

Chen, Chaomei, Min Song, & Go Eun Heo. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics. 12(1). 158–180. 26 indexed citations

Heo, Go Eun, et al.. (2017). Analyzing the field of bioinformatics with the multi-faceted topic modeling technique. BMC Bioinformatics. 18(S7). 251–251. 22 indexed citations

Verspoor, Karin, et al.. (2016). Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts. BMC Medical Informatics and Decision Making. 16(S1). 68–68. 13 indexed citations

10.

Jeong, Yoo Kyung, et al.. (2016). Trajectory analysis of drug-research trends in pancreatic cancer on PubMed and ClinicalTrials.gov. Journal of Informetrics. 10(1). 273–285. 9 indexed citations

11.

Song, Min, et al.. (2015). PKDE4J: Entity and relation extraction for public knowledge discovery. Journal of Biomedical Informatics. 57. 320–332. 79 indexed citations

12.

Song, Min, Go Eun Heo, & Ying Ding. (2015). SemPathFinder: Semantic path analysis for discovering publicly unknown knowledge. Journal of Informetrics. 9(4). 686–703. 13 indexed citations

13.

Verspoor, Karin, et al.. (2015). Extraction of Fine-grained Semantic Relations for the Human Variome. 3–3.

14.

Heo, Go Eun, Keeheon Lee, & Min Song. (2014). Inferring Undiscovered Public Knowledge by Using Text Mining-driven Graph Model. 37–37. 1 indexed citations

15.

Song, Min, Go Eun Heo, & Su Yeon Kim. (2014). Analyzing topic evolution in bioinformatics: investigation of dynamics of the field with conference data in DBLP. Scientometrics. 101(1). 397–428. 34 indexed citations

16.

Song, Min, Go Eun Heo, & Da-Hee Lee. (2014). Identifying the landscape of Alzheimer’s disease research with network and content analysis. Scientometrics. 102(1). 905–927. 22 indexed citations

17.

Heo, Go Eun, et al.. (2014). Grounded Feature Selection for Biomedical Relation Extraction by the Combinative Approach. 29–32. 6 indexed citations

18.

Heo, Go Eun & Min Song. (2014). Inferring Undiscovered Public Knowledge by Using Text Mining-driven Graph Model. Journal of the Korean Society for information Management. 31(1). 231–250.

19.

Heo, Go Eun, et al.. (2013). Topic-Network based Topic Shift Detection on Twitter. Journal of the Korean Society for information Management. 30(1). 285–302. 13 indexed citations

20.

Heo, Go Eun & Min Song. (2013). Examining the Intellectual Structure of a Medical Informatics Journal with Author Co-citation Analysis and Co-word Analysis. Journal of the Korean Society for information Management. 30(2). 207–225. 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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