Kanix Wang

468 total citations
8 papers, 256 citations indexed

About

Kanix Wang is a scholar working on Molecular Biology, Genetics and Epidemiology. According to data from OpenAlex, Kanix Wang has authored 8 papers receiving a total of 256 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Genetics and 2 papers in Epidemiology. Recurrent topics in Kanix Wang's work include Genetic Associations and Epidemiology (2 papers), Bioinformatics and Genomic Networks (2 papers) and Genetic Mapping and Diversity in Plants and Animals (2 papers). Kanix Wang is often cited by papers focused on Genetic Associations and Epidemiology (2 papers), Bioinformatics and Genomic Networks (2 papers) and Genetic Mapping and Diversity in Plants and Animals (2 papers). Kanix Wang collaborates with scholars based in United States, Japan and United Kingdom. Kanix Wang's co-authors include Andrey Rzhetsky, Nancy J. Cox, Hallie Gaitsch, Hoifung Poon, Christopher S. Lyttle, Steven C. Bagley, Robert D. Gibbons, Russ B. Altman, Edwin H. Cook and Joseph Lykins and has published in prestigious journals such as Nature Genetics, Clinical Infectious Diseases and PLoS Computational Biology.

In The Last Decade

Kanix Wang

7 papers receiving 250 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kanix Wang United States	5	80	67	45	34	29	8	256
Moshe Herskovitz Israel	9	33 0.4×	11 0.2×	64 1.4×	24 0.7×	43 1.5×	28	286
Krishna Chinthapalli United Kingdom	9	80 1.0×	40 0.6×	19 0.4×	18 0.5×	10 0.3×	46	290
Ying Han China	11	51 0.6×	9 0.1×	7 0.2×	55 1.6×	18 0.6×	33	269
R.C. Beach United Kingdom	11	49 0.6×	48 0.7×	5 0.1×	28 0.8×	19 0.7×	20	362
Réka Nagy United Kingdom	9	29 0.4×	85 1.3×	9 0.2×	15 0.4×	11 0.4×	18	214
Olaf Junge Germany	11	123 1.5×	80 1.2×	103 2.3×	3 0.1×	110 3.8×	18	424
Mallika Prem Senthil Australia	11	66 0.8×	23 0.3×	207 4.6×	24 0.7×	157 5.4×	47	371
Aravind Lathika Rajendrakumar United States	7	28 0.3×	37 0.6×	8 0.2×	15 0.4×	10 0.3×	10	211
Tania Torbati United States	8	76 0.9×	6 0.1×	105 2.3×	10 0.3×	92 3.2×	9	293
Jeanine May United States	12	102 1.3×	18 0.3×	75 1.7×	21 0.6×	94 3.2×	17	454

Countries citing papers authored by Kanix Wang

Since Specialization

Citations

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

Fields of papers citing papers by Kanix Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kanix Wang

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

8 of 8 papers shown

1.

Adelman, Daniel & Kanix Wang. (2024). Frontiers in Operations: Optimal Genetic Testing of Families. Manufacturing & Service Operations Management. 26(4). 1338–1357.

2.

Li, Yu, Xue Zhong, Kanix Wang, et al.. (2023). The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci. Nature Computational Science. 3(5). 403–417. 4 indexed citations

3.

Wang, Kanix, et al.. (2021). A High-Fidelity Model to Predict Length of Stay in the Neonatal Intensive Care Unit. INFORMS journal on computing. 34(1). 183–195. 3 indexed citations

4.

Wang, Kanix, Hallie Gaitsch, Hoifung Poon, Nancy J. Cox, & Andrey Rzhetsky. (2017). Classification of common human diseases derived from shared genetic and environmental determinants. Nature Genetics. 49(9). 1319–1325. 154 indexed citations

5.

Lykins, Joseph, Kanix Wang, Kelsey M. Wheeler, et al.. (2016). Understanding Toxoplasmosis in the United States Through “Large Data” Analyses. Clinical Infectious Diseases. 63(4). 468–475. 28 indexed citations

6.

Blair, David, Kanix Wang, Svetlozar Nestorov, James A. Evans, & Andrey Rzhetsky. (2014). Quantifying the Impact and Extent of Undocumented Biomedical Synonymy. PLoS Computational Biology. 10(9). e1003799–e1003799. 10 indexed citations

7.

Rzhetsky, Andrey, Steven C. Bagley, Kanix Wang, et al.. (2014). Environmental and State-Level Regulatory Factors Affect the Incidence of Autism and Intellectual Disability. PLoS Computational Biology. 10(3). e1003518–e1003518. 41 indexed citations

8.

Regan, Kelly, Kanix Wang, Haiquan Li, et al.. (2012). Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants. Journal of the American Medical Informatics Association. 19(2). 306–316. 16 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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