Travers Ching

5.4k total citations
28 papers, 1.1k citations indexed

About

Travers Ching is a scholar working on Molecular Biology, Cancer Research and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Travers Ching has authored 28 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 16 papers in Cancer Research and 3 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Travers Ching's work include Cancer-related molecular mechanisms research (13 papers), RNA modifications and cancer (9 papers) and RNA Research and Splicing (5 papers). Travers Ching is often cited by papers focused on Cancer-related molecular mechanisms research (13 papers), RNA modifications and cancer (9 papers) and RNA Research and Splicing (5 papers). Travers Ching collaborates with scholars based in United States, China and United Kingdom. Travers Ching's co-authors include Lana X. Garmire, Xun Zhu, Sijia Huang, Lana X. Garmire, Olivier Poirion, Maarit Tiirikainen, János Molnár, Herbert Yu, Marla J. Berry and Dena Towner and has published in prestigious journals such as Nature Communications, Blood and PLoS ONE.

In The Last Decade

Travers Ching

27 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Travers Ching United States 18 736 379 137 127 89 28 1.1k
Lana X. Garmire United States 10 713 1.0× 348 0.9× 153 1.1× 163 1.3× 184 2.1× 22 1.1k
Yongjun Piao China 17 569 0.8× 206 0.5× 78 0.6× 106 0.8× 48 0.5× 59 1.1k
Cheryl Lee United States 17 302 0.4× 102 0.3× 121 0.9× 52 0.4× 61 0.7× 47 1.1k
Tao Meng China 17 376 0.5× 308 0.8× 65 0.5× 201 1.6× 257 2.9× 58 1.1k
Olivier Poirion United States 13 1.1k 1.6× 488 1.3× 222 1.6× 210 1.7× 240 2.7× 22 1.7k
Baoxia Cui China 20 513 0.7× 396 1.0× 71 0.5× 30 0.2× 61 0.7× 84 1.3k
Sonata Jarmalaitė Lithuania 20 854 1.2× 532 1.4× 371 2.7× 43 0.3× 61 0.7× 88 1.4k
Geoff Macintyre Australia 15 671 0.9× 313 0.8× 141 1.0× 22 0.2× 34 0.4× 38 1.0k
Lei Song United States 14 466 0.6× 288 0.8× 177 1.3× 31 0.2× 59 0.7× 41 849
Yu‐Chiao Chiu United States 23 1.1k 1.4× 540 1.4× 101 0.7× 169 1.3× 92 1.0× 65 1.5k

Countries citing papers authored by Travers Ching

Since Specialization
Citations

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

Fields of papers citing papers by Travers Ching

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Travers Ching

This figure shows the co-authorship network connecting the top 25 collaborators of Travers Ching. A scholar is included among the top collaborators of Travers Ching 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 Travers Ching. Travers Ching 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.
Olender, Jacqueline, Bi‐Dar Wang, Travers Ching, et al.. (2019). A Novel FGFR3 Splice Variant Preferentially Expressed in African American Prostate Cancer Drives Aggressive Phenotypes and Docetaxel Resistance. Molecular Cancer Research. 17(10). 2115–2125. 11 indexed citations
2.
Benny, Paula, Xun Zhu, Travers Ching, et al.. (2019). Maternal cardiovascular-related single nucleotide polymorphisms, genes, and pathways associated with early-onset preeclampsia. PLoS ONE. 14(9). e0222672–e0222672. 6 indexed citations
3.
4.
Poirion, Olivier, Xun Zhu, Travers Ching, & Lana X. Garmire. (2018). Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage. Nature Communications. 9(1). 4892–4892. 47 indexed citations
5.
Chaudhary, Kumardeep, Olivier Poirion, Liangqun Lu, et al.. (2018). Multimodal Meta-Analysis of 1,494 Hepatocellular Carcinoma Samples Reveals Significant Impact of Consensus Driver Genes on Phenotypes. Clinical Cancer Research. 25(2). 463–472. 40 indexed citations
6.
Ching, Travers, Xun Zhu, & Lana X. Garmire. (2018). Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Computational Biology. 14(4). e1006076–e1006076. 236 indexed citations
7.
8.
Zhu, Xun, Travers Ching, Xinghua Pan, Sherman M. Weissman, & Lana X. Garmire. (2017). Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization. PeerJ. 5. e2888–e2888. 54 indexed citations
9.
Wang, Bi‐Dar, SuJin Hwang, Ramez Andrawis, et al.. (2017). Alternative splicing promotes tumour aggressiveness and drug resistance in African American prostate cancer. Nature Communications. 8(1). 15921–15921. 78 indexed citations
10.
Ching, Travers & Lana X. Garmire. (2017). Pan-cancer analysis of expressed somatic nucleotide variants in long intergenic non-coding RNA. PubMed. 23. 512–523. 1 indexed citations
11.
Feng, Nannan, Travers Ching, Yu Wang, et al.. (2016). Analysis of Microarray Data on Gene Expression and Methylation to Identify Long Non-coding RNAs in Non-small Cell Lung Cancer. Scientific Reports. 6(1). 37233–37233. 37 indexed citations
12.
Poirion, Olivier, Xun Zhu, Travers Ching, & Lana X. Garmire. (2016). Single-Cell Transcriptomics Bioinformatics and Computational Challenges. Frontiers in Genetics. 7. 163–163. 78 indexed citations
13.
Ching, Travers, Karolina Peplowska, Sijia Huang, et al.. (2016). Pan-Cancer Analyses Reveal Long Intergenic Non-Coding RNAs Relevant to Tumor Diagnosis, Subtyping and Prognosis. EBioMedicine. 7. 62–72. 31 indexed citations
14.
Ching, Travers, et al.. (2015). Non-coding yet non-trivial: a review on the computational genomics of lincRNAs. BioData Mining. 8(1). 44–44. 18 indexed citations
15.
Ching, Travers, James C. Ha, Min‐Ae Song, et al.. (2015). Genome-scale hypomethylation in the cord blood DNAs associated with early onset preeclampsia. Clinical Epigenetics. 7(1). 21–21. 45 indexed citations
16.
Huang, Sijia, et al.. (2014). A Novel Model to Combine Clinical and Pathway-Based Transcriptomic Information for the Prognosis Prediction of Breast Cancer. PLoS Computational Biology. 10(9). e1003851–e1003851. 54 indexed citations
17.
Menor, Mark, Travers Ching, Xun Zhu, David Garmire, & Lana X. Garmire. (2014). mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome biology. 15(10). 500–500. 40 indexed citations
18.
Ching, Travers, Min‐Ae Song, Maarit Tiirikainen, et al.. (2014). Genome-wide hypermethylation coupled with promoter hypomethylation in the chorioamniotic membranes of early onset pre-eclampsia. Molecular Human Reproduction. 20(9). 885–904. 52 indexed citations
19.
Ching, Travers, Sijia Huang, & Lana X. Garmire. (2014). Power analysis and sample size estimation for RNA-Seq differential expression. RNA. 20(11). 1684–1696. 166 indexed citations
20.
Ching, Travers, Brandon A. Yoza, & Qing X. Li. (2013). Quartet Analysis of Putative Horizontal Gene Transfer in Crenarchaeota. Journal of Molecular Evolution. 78(2). 163–170. 1 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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