Guo Ci Teo
- Spectroscopy top 1%
- Advanced Proteomics Techniques and Applications 10
- Mass Spectrometry Techniques and Applications 10
- Molecular Biology top 10%
- Metabolomics and Mass Spectrometry Studies 7
- Machine Learning in Bioinformatics 2
- vaccines and immunoinformatics approaches 1
- Glycosylation and Glycoproteins Research 1
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- Artificial Intelligence in Healthcare 1
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- Advanced Electrical Measurement Techniques 1
- Co-authors
- Alexey I. NesvizhskiiFengchao YuDaniel A. PolaskyDmitry M. AvtonomovSarah E. HaynesVadim DemichevAndy T. KongMarkus Ralser
- Journals
- Nature Communications (5 papers)Nature Biotechnology (1 paper)Analytical Chemistry (1 paper)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Guo Ci Teo
13 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 106
- Spectroscopy 594
- Molecular Biology 926
- Immunology 111
- Oncology 143
- Cell Biology 72
Countries citing papers authored by Guo Ci Teo
This map shows the geographic impact of Guo Ci Teo'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 Guo Ci Teo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guo Ci Teo more than expected).
Fields of papers citing papers by Guo Ci Teo
This network shows the impact of papers produced by Guo Ci Teo. 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 Guo Ci Teo. The network helps show where Guo Ci Teo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Guo Ci Teo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 6 | |
| 2 | Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platformbreakdown → | 2023 | 119 |
| 3 | 2023 | 91 | |
| 4 | 2023 | 18 | |
| 5 | 2023 | 15 | |
| 6 | dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amountsbreakdown → | 2022 | 202 |
| 7 | 2022 | 67 | |
| 8 | 2020 | 183 | |
| 9 | 2020 | 191 | |
| 10 | 2020 | 161 | |
| 11 | 2020 | 21 | |
| 12 | 2020 | 103 | |
| 13 | 2016 | 55 |
About Guo Ci Teo
Guo Ci Teo is a scholar working on Spectroscopy, Health Information Management and Molecular Biology, having authored 13 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Proteomics Techniques and Applications (10 papers), Mass Spectrometry Techniques and Applications (10 papers), Metabolomics and Mass Spectrometry Studies (7 papers), Machine Learning in Bioinformatics (2 papers), vaccines and immunoinformatics approaches (1 paper), Glycosylation and Glycoproteins Research (1 paper), Artificial Intelligence in Healthcare (1 paper) and Advanced Electrical Measurement Techniques (1 paper). The work is most often cited by research in Spectroscopy (594 citations), Molecular Biology (926 citations) and Immunology (111 citations). Guo Ci Teo has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Alexey I. Nesvizhskii, Fengchao Yu, Daniel A. Polasky, Dmitry M. Avtonomov, Sarah E. Haynes, Vadim Demichev, Andy T. Kong, Markus Ralser, Daniel Geiszler and Klemens Fröhlich. Their work appears in journals such as Nature Communications, Nature Biotechnology and Analytical Chemistry.
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.