Pi-Chuan Chang
- Health Informatics top 5%
- Artificial Intelligence top 2%
- Natural Language Processing Techniques 13
- Topic Modeling 12
- Text Readability and Simplification 3
- Genetics top 5%
- Genomics and Rare Diseases 5
- Genetic Associations and Epidemiology 3
- Genomic variations and chromosomal abnormalities 3
- Cancer Research top 10%
- Cancer Genomics and Diagnostics 3
- Molecular Biology top 10%
- Genomics and Phylogenetic Studies 11
- Co-authors
- Christopher D. ManningCory Y. McLeanHuihsin TsengMichel GalleyMark A. DePristoDavid H. AlexanderSam GrossPegah Tootoonchi Afshar
- Partner nations
- United StatesTaiwanItaly
In The Last Decade
Pi-Chuan Chang
29 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 147
- Health Informatics 32
- Artificial Intelligence 711
- Genetics 534
- Cancer Research 229
- Molecular Biology 965
Countries citing papers authored by Pi-Chuan Chang
This map shows the geographic impact of Pi-Chuan 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 Pi-Chuan Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pi-Chuan Chang more than expected).
Fields of papers citing papers by Pi-Chuan Chang
This network shows the impact of papers produced by Pi-Chuan 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 Pi-Chuan Chang. The network helps show where Pi-Chuan Chang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pi-Chuan Chang, 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 | 2 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 9 | |
| 4 | 2024 | 5 | |
| 5 | 2023 | 3 | |
| 6 | 2023 | 10 | |
| 7 | 2022 | 5 | |
| 8 | 2022 | 21 | |
| 9 | 2022 | 89 | |
| 10 | 2022 | 5 | |
| 11 | 2021 | 165 | |
| 12 | 2021 | 133 | |
| 13 | 2020 | 94 | |
| 14 | 2019 | 0 | |
| 15 | Using Nucleus and TensorFlow for DNA Sequencing Error Correction | 2019 | 1 |
| 16 | A universal SNP and small-indel variant caller using deep neural networksbreakdown → | 2018 | 757 |
| 17 | Uptraining for Accurate Deterministic Question Parsing | 2010 | 47 |
| 18 | 2008 | 198 | |
| 19 | A Discriminative Syntactic Word Order Model for Machine Translation | 2007 | 25 |
| 20 | 2003 | 4 |
About Pi-Chuan Chang
Pi-Chuan Chang is a scholar working on Artificial Intelligence, Genetics and Cancer Research, having authored 30 papers that have together received 2.0k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (13 papers), Topic Modeling (12 papers), Genomics and Phylogenetic Studies (11 papers), Genomics and Rare Diseases (5 papers), Genetic Associations and Epidemiology (3 papers), Genomic variations and chromosomal abnormalities (3 papers), Text Readability and Simplification (3 papers) and Cancer Genomics and Diagnostics (3 papers). The work is most often cited by research in Health Informatics (32 citations), Artificial Intelligence (711 citations) and Genetics (534 citations). Pi-Chuan Chang has collaborated with scholars based in United States, Taiwan and Italy. Frequent co-authors include Christopher D. Manning, Cory Y. McLean, Huihsin Tseng, Michel Galley, Mark A. DePristo, David H. Alexander, Sam Gross, Pegah Tootoonchi Afshar, Ryan Poplin and Scott Schwartz. Their work appears in journals such as Science, Nature Communications and Nature Biotechnology.
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.