Ti‐Cheng Chang

2.5k total citations
31 papers, 851 citations indexed

About

Ti‐Cheng Chang is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Ti‐Cheng Chang has authored 31 papers receiving a total of 851 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 9 papers in Genetics and 7 papers in Cancer Research. Recurrent topics in Ti‐Cheng Chang's work include Cancer Genomics and Diagnostics (6 papers), Genomics and Rare Diseases (4 papers) and Gut microbiota and health (3 papers). Ti‐Cheng Chang is often cited by papers focused on Cancer Genomics and Diagnostics (6 papers), Genomics and Rare Diseases (4 papers) and Gut microbiota and health (3 papers). Ti‐Cheng Chang collaborates with scholars based in United States, China and Japan. Ti‐Cheng Chang's co-authors include Ioannis Stergiopoulos, Peter Vogel, Jason W. Rosch, Clifford S. Guy, Ankit Malik, Deepika Sharma, Thirumala‐Devi Kanneganti, Geoffrey Neale, Scott R. Olsen and R. K. Subbarao Malireddi and has published in prestigious journals such as Cell, Journal of Clinical Oncology and Blood.

In The Last Decade

Ti‐Cheng Chang

28 papers receiving 843 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ti‐Cheng Chang United States 14 454 169 152 131 96 31 851
Sunil K. Malonia United States 14 572 1.3× 198 1.2× 120 0.8× 119 0.9× 94 1.0× 25 1.0k
Chunmei Li China 20 607 1.3× 122 0.7× 266 1.8× 98 0.7× 105 1.1× 44 947
Xinlei Sheng United States 14 442 1.0× 77 0.5× 105 0.7× 75 0.6× 58 0.6× 23 801
Jeng‐Woei Lee Taiwan 19 337 0.7× 132 0.8× 103 0.7× 147 1.1× 126 1.3× 31 761
Yina Gao China 15 534 1.2× 200 1.2× 163 1.1× 73 0.6× 127 1.3× 30 836
Tsafi Pe’ery United States 20 779 1.7× 94 0.6× 148 1.0× 97 0.7× 115 1.2× 29 1.1k
Sadie L. Marjani United States 12 502 1.1× 115 0.7× 51 0.3× 131 1.0× 102 1.1× 20 798
Hiroyuki Horiuchi Japan 19 527 1.2× 121 0.7× 232 1.5× 246 1.9× 81 0.8× 111 1.2k
Shih-Queen Lee-Lin United States 9 283 0.6× 90 0.5× 110 0.7× 132 1.0× 34 0.4× 11 579
Matthew F. Barber United States 9 417 0.9× 116 0.7× 153 1.0× 75 0.6× 54 0.6× 15 961

Countries citing papers authored by Ti‐Cheng Chang

Since Specialization
Citations

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

Fields of papers citing papers by Ti‐Cheng Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ti‐Cheng Chang. 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 Ti‐Cheng Chang. The network helps show where Ti‐Cheng Chang may publish in the future.

Co-authorship network of co-authors of Ti‐Cheng Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Ti‐Cheng Chang. A scholar is included among the top collaborators of Ti‐Cheng Chang 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 Ti‐Cheng Chang. Ti‐Cheng Chang 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.
Pölönen, Petri, Shaohua Lei, Qingsong Gao, et al.. (2025). Tallforest: Multi-omic classifier for T-lineage acute lymphoblastic leukemia. Blood. 146(Supplement 1). 336–336.
2.
Chang, Ti‐Cheng, Jing Yu, Zhaoming Wang, et al.. (2024). Machine learning to optimize automated RH genotyping using whole-exome sequencing data. Blood Advances. 8(11). 2651–2659. 4 indexed citations
3.
Chang, Ti‐Cheng, Wenan Chen, Stanley Pounds, et al.. (2023). Genomic determinants of outcome in acute lymphoblastic leukemia: A Children’s Oncology Group study.. Journal of Clinical Oncology. 41(16_suppl). 10015–10015. 1 indexed citations
4.
Lei, Shaohua, Sujuan Jia, Sunitha Takalkar, et al.. (2023). Cancer Genomic Profiling and Minimal Residual Disease Monitoring By Cell-Free DNA Sequencing in Pediatric Leukemia. Blood. 142(Supplement 1). 1604–1604. 1 indexed citations
5.
Davenport, Christopher M., Brett J.W. Teubner, Seung Baek Han, et al.. (2022). Innate frequency-discrimination hyperacuity in Williams-Beuren syndrome mice. Cell. 185(21). 3877–3895.e21. 7 indexed citations
6.
Chang, Ti‐Cheng, Ke Xu, Zhongshan Cheng, & Gang Wu. (2022). Somatic and Germline Variant Calling from Next-Generation Sequencing Data. Advances in experimental medicine and biology. 1361. 37–54. 3 indexed citations
7.
Clay, Michael R., Anand G. Patel, Quynh T. Tran, et al.. (2021). Methylation profiling reveals novel molecular classes of rhabdomyosarcoma. Scientific Reports. 11(1). 22213–22213. 16 indexed citations
8.
Margolis, Elisa, Hana Hakim, Ronald H. Dallas, et al.. (2021). Antibiotic prophylaxis and the gastrointestinal resistome in paediatric patients with acute lymphoblastic leukaemia: a cohort study with metagenomic sequencing analysis. The Lancet Microbe. 2(4). e159–e167. 13 indexed citations
9.
Zamora, Anthony E., Jeremy Chase Crawford, E. Kaitlynn Allen, et al.. (2019). Pediatric patients with acute lymphoblastic leukemia generate abundant and functional neoantigen-specific CD8 + T cell responses. Science Translational Medicine. 11(498). 59 indexed citations
10.
Rowe, Hannah M., Erik A. Karlsson, Ti‐Cheng Chang, et al.. (2019). Bacterial Factors Required for Transmission of Streptococcus pneumoniae in Mammalian Hosts. Cell Host & Microbe. 25(6). 884–891.e6. 44 indexed citations
11.
Chen, Li‐Hung, B. J. Aegerter, E.M. Miyao, et al.. (2019). Cloning of the Cytochrome b Gene From the Tomato Powdery Mildew Fungus Leveillula taurica Reveals High Levels of Allelic Variation and Heteroplasmy for the G143A Mutation. Frontiers in Microbiology. 10. 663–663. 12 indexed citations
12.
LeMessurier, Kim S., Amy Iverson, Ti‐Cheng Chang, et al.. (2019). Allergic inflammation alters the lung microbiome and hinders synergistic co-infection with H1N1 influenza virus and Streptococcus pneumoniae in C57BL/6 mice. Scientific Reports. 9(1). 19360–19360. 24 indexed citations
13.
Malik, Ankit, Deepika Sharma, R. K. Subbarao Malireddi, et al.. (2018). SYK-CARD9 Signaling Axis Promotes Gut Fungi-Mediated Inflammasome Activation to Restrict Colitis and Colon Cancer. Immunity. 49(3). 515–530.e5. 157 indexed citations
14.
Xu, Ke, Liang Ding, Ti‐Cheng Chang, et al.. (2018). Structure and evolution of double minutes in diagnosis and relapse brain tumors. Acta Neuropathologica. 137(1). 123–137. 59 indexed citations
15.
Cortez, Valerie, Ti‐Cheng Chang, Gabriela Salmón-Mulanovich, et al.. (2018). Identification of Leptospira and Bartonella among rodents collected across a habitat disturbance gradient along the Inter-Oceanic Highway in the southern Amazon Basin of Peru. PLoS ONE. 13(10). e0205068–e0205068. 12 indexed citations
16.
Chang, Ti‐Cheng, Robert Carter, Yongjin Li, et al.. (2017). The neoepitope landscape in pediatric cancers. Genome Medicine. 9(1). 78–78. 59 indexed citations
17.
18.
Chang, Ti‐Cheng & Ioannis Stergiopoulos. (2015). Evolutionary analysis of the global landscape of protein domain types and domain architectures associated with family 14 carbohydrate‐binding modules. FEBS Letters. 589(15). 1813–1818. 4 indexed citations
19.
20.
Natale, Darren A., Cecilia N. Arighi, Winona C. Barker, et al.. (2007). Framework for a Protein Ontology. BMC Bioinformatics. 8(S9). S1–S1. 69 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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