Jason Y. Liu

881 total citations
9 papers, 683 citations indexed

About

Jason Y. Liu is a scholar working on Molecular Biology, Genetics and Organic Chemistry. According to data from OpenAlex, Jason Y. Liu has authored 9 papers receiving a total of 683 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Genetics and 1 paper in Organic Chemistry. Recurrent topics in Jason Y. Liu's work include Genetic Associations and Epidemiology (5 papers), Bioinformatics and Genomic Networks (5 papers) and Genomics and Rare Diseases (2 papers). Jason Y. Liu is often cited by papers focused on Genetic Associations and Epidemiology (5 papers), Bioinformatics and Genomic Networks (5 papers) and Genomics and Rare Diseases (2 papers). Jason Y. Liu collaborates with scholars based in Australia, Netherlands and United States. Jason Y. Liu's co-authors include Rik Derynck, Robert L. Judson, Samy Lamouille, Nathan Bucay, Robert Blelloch, Deepa Subramanyam, Merridee A. Wouters, Diane Fatkin, Lina Feng and Richard A. George and has published in prestigious journals such as Nucleic Acids Research, Nature Biotechnology and BMC Bioinformatics.

In The Last Decade

Jason Y. Liu

9 papers receiving 673 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jason Y. Liu Australia	7	613	221	97	49	39	9	683
Nikiana Simigdala United Kingdom	11	234 0.4×	136 0.6×	81 0.8×	50 1.0×	19 0.5×	17	455
Carlie Delaine Australia	12	420 0.7×	80 0.4×	60 0.6×	91 1.9×	9 0.2×	18	570
Filipe V. Jacinto Spain	11	799 1.3×	158 0.7×	139 1.4×	15 0.3×	15 0.4×	12	915
Olivia Lanvin France	12	246 0.4×	68 0.3×	116 1.2×	20 0.4×	20 0.5×	14	420
Yuanjie Hu China	15	325 0.5×	162 0.7×	55 0.6×	18 0.4×	29 0.7×	23	557
Naser Monsefi Ireland	10	331 0.5×	133 0.6×	30 0.3×	17 0.3×	25 0.6×	13	483
Sarah Greenlee United States	12	667 1.1×	53 0.2×	72 0.7×	21 0.4×	14 0.4×	13	827
Guotai Xu United States	10	482 0.8×	129 0.6×	80 0.8×	17 0.3×	17 0.4×	13	646
Carlos Stahlhut United States	9	781 1.3×	680 3.1×	30 0.3×	29 0.6×	10 0.3×	15	951
Chiara Zucal Italy	13	435 0.7×	212 1.0×	27 0.3×	17 0.3×	19 0.5×	18	592

Countries citing papers authored by Jason Y. Liu

Since Specialization

Citations

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

Fields of papers citing papers by Jason Y. Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jason Y. Liu. 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 Jason Y. Liu. The network helps show where Jason Y. Liu may publish in the future.

Co-authorship network of co-authors of Jason Y. Liu

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

9 of 9 papers shown

1.

Liu, Jason Y.. (2015). PE‐Swab Direct STR Amplification of Forensic Touch DNA Samples. Journal of Forensic Sciences. 60(3). 693–701. 6 indexed citations

2.

Ballouz, Sara, Jason Y. Liu, Richard A. George, et al.. (2013). GentrepidV2.0: a web server for candidate disease gene prediction. BMC Bioinformatics. 14(1). 249–249. 4 indexed citations

3.

Ballouz, Sara, Jason Y. Liu, Martin Oti, et al.. (2013). Candidate disease gene prediction using Gentrepid: application to a genome‐wide association study on coronary artery disease. Molecular Genetics & Genomic Medicine. 2(1). 44–57. 8 indexed citations

4.

Ballouz, Sara, Jason Y. Liu, Martin Oti, et al.. (2011). Analysis of genome-wide association study data using the protein knowledge base. BMC Genetics. 12(1). 98–98. 8 indexed citations

5.

Subramanyam, Deepa, Samy Lamouille, Robert L. Judson, et al.. (2011). Multiple targets of miR-302 and miR-372 promote reprogramming of human fibroblasts to induced pluripotent stem cells. Nature Biotechnology. 29(5). 443–448. 461 indexed citations

6.

Haworth, Naomi L., et al.. (2010). Estimating Relative Disulfide Energies: An Accurate Ab Initio Potential Energy Surface. Australian Journal of Chemistry. 63(3). 379–387. 11 indexed citations

7.

George, Richard A., et al.. (2009). Conformational changes in redox pairs of protein structures. Protein Science. 18(8). 1745–1765. 38 indexed citations

8.

Teber, Erdahl, Jason Y. Liu, Sara Ballouz, Diane Fatkin, & Merridee A. Wouters. (2009). Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies. BMC Bioinformatics. 10(S1). S69–S69. 29 indexed citations

9.

George, Richard A., Jason Y. Liu, Lina Feng, et al.. (2006). Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Research. 34(19). e130–e130. 118 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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