Hao‐Teng Chang

1.8k total citations
58 papers, 1.4k citations indexed

About

Hao‐Teng Chang is a scholar working on Molecular Biology, Physiology and Oncology. According to data from OpenAlex, Hao‐Teng Chang has authored 58 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 10 papers in Physiology and 7 papers in Oncology. Recurrent topics in Hao‐Teng Chang's work include Genomics and Phylogenetic Studies (8 papers), Glycosylation and Glycoproteins Research (7 papers) and vaccines and immunoinformatics approaches (7 papers). Hao‐Teng Chang is often cited by papers focused on Genomics and Phylogenetic Studies (8 papers), Glycosylation and Glycoproteins Research (7 papers) and vaccines and immunoinformatics approaches (7 papers). Hao‐Teng Chang collaborates with scholars based in Taiwan, China and United States. Hao‐Teng Chang's co-authors include Tun‐Wen Pai, Chung‐Yu Lan, Pei-Wen Tsai, Cheng‐Yao Yang, Margaret Dah‐Tsyr Chang, Woei‐Cherng Shyu, Chi-Shin Hwang, Chon-Haw Tsai, Ya‐Chi Lin and Hsin‐Wei Wang and has published in prestigious journals such as PLoS ONE, Scientific Reports and Biochemical Journal.

In The Last Decade

Hao‐Teng Chang

57 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hao‐Teng Chang Taiwan 22 565 155 152 145 128 58 1.4k
Gi Jung Im South Korea 20 609 1.1× 66 0.4× 218 1.4× 163 1.1× 99 0.8× 110 1.6k
Alexander Lam United Kingdom 23 774 1.4× 96 0.6× 181 1.2× 125 0.9× 317 2.5× 63 2.2k
Peter Findeisen Germany 26 745 1.3× 127 0.8× 239 1.6× 51 0.4× 95 0.7× 88 1.9k
George Th. Tsangaris Greece 29 1.0k 1.8× 142 0.9× 270 1.8× 79 0.5× 43 0.3× 147 2.6k
Hiroshi Kondo Japan 24 482 0.9× 154 1.0× 141 0.9× 40 0.3× 267 2.1× 172 2.0k
Ping He China 25 975 1.7× 330 2.1× 248 1.6× 52 0.4× 105 0.8× 95 2.2k
Jack Rose Italy 8 588 1.0× 142 0.9× 164 1.1× 25 0.2× 123 1.0× 10 1.8k
Naoki Takahashi Japan 26 996 1.8× 440 2.8× 300 2.0× 69 0.5× 60 0.5× 96 2.5k
K. Morikawa Japan 15 918 1.6× 89 0.6× 213 1.4× 48 0.3× 73 0.6× 24 1.7k

Countries citing papers authored by Hao‐Teng Chang

Since Specialization
Citations

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

Fields of papers citing papers by Hao‐Teng Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hao‐Teng Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Hao‐Teng Chang. A scholar is included among the top collaborators of Hao‐Teng 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 Hao‐Teng Chang. Hao‐Teng 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.
Lu, Chih‐Hao, Hao‐Teng Chang, Lee‐Fen Hsu, et al.. (2023). In Silico and In Vitro Screening of Serine Racemase Agonist and In Vivo Efficacy on Alzheimer’s Disease Drosophila melanogaster. Pharmaceuticals. 16(2). 280–280. 4 indexed citations
2.
Wang, Maofeng, et al.. (2018). pLG72 induces superoxide radicals via interaction and aggregation with SOD1. Free Radical Research. 52(9). 970–976. 5 indexed citations
3.
Lü, Tingting, et al.. (2017). Eosinophilic biomarkers for detection of acute exacerbation of chronic obstructive pulmonary disease with or without pulmonary embolism. Experimental and Therapeutic Medicine. 14(4). 3198–3206. 8 indexed citations
4.
Lee, Hsu-Tung, Hao‐Teng Chang, Sophie Lee, et al.. (2016). Role of IGF1R+ MSCs in modulating neuroplasticity via CXCR4 cross-interaction. Scientific Reports. 6(1). 32595–32595. 15 indexed citations
5.
Chen, Yu-Ching, Sarah Statt, Reen Wu, et al.. (2016). High mobility group box 1-induced epithelial mesenchymal transition in human airway epithelial cells. Scientific Reports. 6(1). 18815–18815. 63 indexed citations
6.
Chen, Wei-Ling, Chia‐Hung Hsieh, Hao‐Teng Chang, Chia‐Chun Hung, & Chin-Hong Chan. (2015). The epidemiology and progression time from transient to permanent psychiatric disorders of substance-induced psychosis in Taiwan. Addictive Behaviors. 47. 1–4. 12 indexed citations
7.
Chang, Hao‐Teng, et al.. (2014). Identification of Simple Sequence Repeat Biomarkers through Cross-Species Comparison in a Tag Cloud Representation. BioMed Research International. 2014. 1–11. 4 indexed citations
8.
Chang, Hao‐Teng, et al.. (2014). Cross-species identification of in silico microsatellite biomarkers for genetic disease. Biomedicine. 4(2). 14–14. 2 indexed citations
9.
Hwang, Chi-Shin, et al.. (2014). An Eye-Tracking Assistive Device Improves the Quality of Life for ALS Patients and Reduces the Caregivers’ Burden. Journal of Motor Behavior. 46(4). 233–238. 88 indexed citations
10.
Hwang, Chi‐Shin, et al.. (2013). Elevated serum autoantibody against high mobility group box 1 as a potent surrogate biomarker for amyotrophic lateral sclerosis. Neurobiology of Disease. 58. 13–18. 38 indexed citations
11.
Lee, Chen-Chen, Yu-Ting Lai, Hao‐Teng Chang, et al.. (2013). Inhibition of high-mobility group box 1 in lung reduced airway inflammation and remodeling in a mouse model of chronic asthma. Biochemical Pharmacology. 86(7). 940–949. 58 indexed citations
12.
Pai, Tun‐Wen, et al.. (2013). Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration. BMC Bioinformatics. 14(S4). S4–S4. 12 indexed citations
13.
Tsai, Pei-Wen, Yong Wang, Chia‐Hung Hsieh, et al.. (2012). Chemoattraction of macrophages by secretory molecules derived from cells expressing the signal peptide of eosinophil cationic protein. BMC Systems Biology. 6(1). 105–105. 13 indexed citations
14.
Wang, Hsin‐Wei, Ya‐Chi Lin, Tun‐Wen Pai, & Hao‐Teng Chang. (2011). Prediction of B‐cell Linear Epitopes with a Combination of Support Vector Machine Classification and Amino Acid Propensity Identification. BioMed Research International. 2011(1). 432830–432830. 70 indexed citations
15.
Huang, Hsiang-Ling, Yi–Wen Chen, Ping-Chiang Lyu, et al.. (2010). Trypsin-induced proteome alteration during cell subculture in mammalian cells. Journal of Biomedical Science. 17(1). 36–36. 226 indexed citations
16.
Chang, Hao‐Teng, et al.. (2010). Inhibition of the interactions between eosinophil cationic protein and airway epithelial cells by traditional Chinese herbs. BMC Systems Biology. 4(S2). S8–S8. 33 indexed citations
17.
18.
Chang, Hao‐Teng, Yu‐Lin Kao, Yiu‐Kay Lai, et al.. (2006). Signal peptide of eosinophil cationic protein upregulates transforming growth factor‐alpha expression in human cells. Journal of Cellular Biochemistry. 100(5). 1266–1275. 15 indexed citations
19.
Chang, Hao‐Teng, et al.. (2006). A reinforced merging methodology for mapping unique peptide motifs in members of protein families. BMC Bioinformatics. 7(1). 38–38. 12 indexed citations
20.
Chang, Hao‐Teng. (1974). THE VEGETATION OF THE HSI-SHA ISLANDS. 3 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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