Steve Skiena

687 total citations
13 papers, 466 citations indexed

About

Steve Skiena is a scholar working on Artificial Intelligence, Molecular Biology and Genetics. According to data from OpenAlex, Steve Skiena has authored 13 papers receiving a total of 466 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Molecular Biology and 3 papers in Genetics. Recurrent topics in Steve Skiena's work include Topic Modeling (5 papers), Natural Language Processing Techniques (4 papers) and RNA modifications and cancer (3 papers). Steve Skiena is often cited by papers focused on Topic Modeling (5 papers), Natural Language Processing Techniques (4 papers) and RNA modifications and cancer (3 papers). Steve Skiena collaborates with scholars based in United States, Hong Kong and China. Steve Skiena's co-authors include Bruce Futcher, Alisa Yurovsky, Justin Gardin, Saumyadipta Pyne, Ying Cai, Adam P. Rosebrock, Francisco Ferrezuelo, Anna Maria Oliva, Janet Leatherwood and Haiying Chen and has published in prestigious journals such as Bioinformatics, PLoS ONE and PLoS Biology.

In The Last Decade

Steve Skiena

12 papers receiving 456 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Steve Skiena United States	9	323	79	38	38	32	13	466
Jérôme Waldispühl Canada	19	718 2.2×	51 0.6×	31 0.8×	67 1.8×	28 0.9×	64	962
Evangelos Karatzas Greece	12	298 0.9×	30 0.4×	14 0.4×	29 0.8×	28 0.9×	29	475
Michael Fry Australia	13	219 0.7×	48 0.6×	29 0.8×	55 1.4×	14 0.4×	52	490
Anamaria Crisan Canada	11	211 0.7×	66 0.8×	16 0.4×	143 3.8×	66 2.1×	26	500
Hiroshi Yamaguchi Japan	11	126 0.4×	42 0.5×	28 0.7×	60 1.6×	10 0.3×	42	338
Fritz Lekschas United States	10	348 1.1×	42 0.5×	7 0.2×	63 1.7×	87 2.7×	16	498
Sylvie Ranwez France	9	223 0.7×	212 2.7×	10 0.3×	63 1.7×	38 1.2×	21	450
Manal Kalkatawi Saudi Arabia	10	146 0.5×	176 2.2×	31 0.8×	8 0.2×	10 0.3×	18	391
Antonio Messina Italy	9	89 0.3×	120 1.5×	25 0.7×	38 1.0×	15 0.5×	28	281
Vuk Janjić United Kingdom	7	272 0.8×	53 0.7×	9 0.2×	26 0.7×	18 0.6×	10	383

Countries citing papers authored by Steve Skiena

Since Specialization

Citations

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

Fields of papers citing papers by Steve Skiena

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Steve Skiena

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

13 of 13 papers shown

1.

Allen, Kim & Steve Skiena. (2022). Chapter Ordering in Novels. 3838–3848.

2.

Allen, Kim, et al.. (2020). What time is it? Temporal Analysis of Novels. 9076–9086. 10 indexed citations

3.

Allen, Kim, et al.. (2020). Chapter Captor: Text Segmentation in Novels. 8373–8383. 10 indexed citations

4.

Skiena, Steve, et al.. (2019). The Trumpiest Trump? Identifying a Subject’s Most Characteristic Tweets. 1653–1663. 3 indexed citations

5.

Yurovsky, Alisa, et al.. (2018). Re-annotation of 12,495 prokaryotic 16S rRNA 3’ ends and analysis of Shine-Dalgarno and anti-Shine-Dalgarno sequences. PLoS ONE. 13(8). e0202767–e0202767. 21 indexed citations

6.

Kulkarni, Vivek, Junting Ye, Steve Skiena, & William Yang Wang. (2018). Multi-view Models for Political Ideology Detection of News Articles. 3518–3527. 41 indexed citations

7.

Yurovsky, Alisa, et al.. (2018). Prokaryotic coding regions have little if any specific depletion of Shine-Dalgarno motifs. PLoS ONE. 13(8). e0202768–e0202768. 4 indexed citations

8.

Gardin, Justin, et al.. (2014). Measurement of average decoding rates of the 61 sense codons in vivo. eLife. 3. 146 indexed citations

9.

Cui, Weiwei, Huamin Qu, Hong Zhou, Wenbin Zhang, & Steve Skiena. (2012). Watch the Story Unfold with TextWheel. ACM Transactions on Intelligent Systems and Technology. 3(2). 1–17. 18 indexed citations

10.

Huddy, Leonie, et al.. (2010). Large Scale Online Text Analysis Using Lydia. SSRN Electronic Journal. 1 indexed citations

11.

Mehler, Alexander, Yan Bao, Xin Li, Yuxi Wang, & Steve Skiena. (2006). Spatial Analysis of News Sources. IEEE Transactions on Visualization and Computer Graphics. 12(5). 765–772. 42 indexed citations

12.

Pyne, Saumyadipta, Bruce Futcher, & Steve Skiena. (2006). Meta-analysis based on control of false discovery rate: combining yeast ChIP-chip datasets. Bioinformatics. 22(20). 2516–2522. 14 indexed citations

13.

Oliva, Anna Maria, Adam P. Rosebrock, Francisco Ferrezuelo, et al.. (2005). The Cell Cycle–Regulated Genes of Schizosaccharomyces pombe. PLoS Biology. 3(7). e225–e225. 156 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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