Pi-Chuan Chang

9.3k total citations · 1 hit paper
30 papers, 2.0k citations indexed

About

Pi-Chuan Chang is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Pi-Chuan Chang has authored 30 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 13 papers in Artificial Intelligence and 8 papers in Genetics. Recurrent topics in Pi-Chuan Chang's work include Natural Language Processing Techniques (13 papers), Topic Modeling (12 papers) and Genomics and Phylogenetic Studies (11 papers). Pi-Chuan Chang is often cited by papers focused on Natural Language Processing Techniques (13 papers), Topic Modeling (12 papers) and Genomics and Phylogenetic Studies (11 papers). Pi-Chuan Chang collaborates with scholars based in United States, Taiwan and Italy. Pi-Chuan Chang's 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 and has published in prestigious journals such as Science, Nature Communications and Nature Biotechnology.

In The Last Decade

Pi-Chuan Chang

29 papers receiving 1.9k citations

Hit Papers

A universal SNP and small-indel variant caller using deep... 2018 2026 2020 2023 2018 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A universal SNP and small-indel variant caller using deep neural networks
    2018 757 citations Ryan Poplin, Pi-Chuan Chang et al. Nature Biotechnology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pi-Chuan Chang United States 14 965 711 534 233 229 30 2.0k
Marc Fiume Canada 9 1.5k 1.6× 303 0.4× 410 0.8× 130 0.6× 426 1.9× 11 2.2k
Erel Levine United States 20 1.5k 1.5× 284 0.4× 384 0.7× 106 0.5× 181 0.8× 35 2.1k
Xinghua Shi United States 22 745 0.8× 290 0.4× 224 0.4× 109 0.5× 225 1.0× 78 1.7k
Semyon Kruglyak United States 13 2.0k 2.0× 233 0.3× 1.1k 2.0× 437 1.9× 452 2.0× 20 3.1k
Louxin Zhang Singapore 24 1.4k 1.4× 416 0.6× 657 1.2× 314 1.3× 55 0.2× 113 2.2k
Amelia Ireland United Kingdom 5 2.4k 2.5× 955 1.3× 360 0.7× 246 1.1× 162 0.7× 5 3.1k
Yaniv Erlich United States 27 2.7k 2.7× 667 0.9× 1.4k 2.7× 284 1.2× 687 3.0× 45 4.5k
Karen Eilbeck United States 21 2.8k 2.9× 1.1k 1.6× 742 1.4× 135 0.6× 302 1.3× 65 3.6k
Michael M. Hoffman Canada 17 1.5k 1.6× 169 0.2× 311 0.6× 133 0.6× 228 1.0× 37 2.0k
Kei‐Hoi Cheung United States 24 1.6k 1.6× 344 0.5× 655 1.2× 108 0.5× 79 0.3× 77 2.5k

Countries citing papers authored by Pi-Chuan Chang

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Pi-Chuan Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Pi-Chuan Chang. A scholar is included among the top collaborators of Pi-Chuan 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 Pi-Chuan Chang. Pi-Chuan 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.
Asri, Mobin, Prajna Hebbar, Alexey Kolesnikov, et al.. (2025). Highly accurate assembly polishing with DeepPolisher. Genome Research. 35(7). 1595–1608. 2 indexed citations
2.
Carroll, Andrew, Alexey Kolesnikov, Daniel E. Cook, et al.. (2025). Accurate human genome analysis with element avidity sequencing. BMC Bioinformatics. 26(1). 194–194. 1 indexed citations
3.
Sirén, Jouni, Glenn Hickey, Jordan M. Eizenga, et al.. (2024). Personalized pangenome references. Nature Methods. 21(11). 2017–2023. 9 indexed citations
4.
Kolesnikov, Alexey, Daniel E. Cook, Maria Nattestad, et al.. (2024). Local read haplotagging enables accurate long-read small variant calling. Nature Communications. 15(1). 5907–5907. 5 indexed citations
5.
Cook, Daniel E., Aarti Venkat, Yannick Pouliot, et al.. (2023). A deep-learning-based RNA-seq germline variant caller. Bioinformatics Advances. 3(1). vbad062–vbad062. 3 indexed citations
6.
Chen, Nae-Chyun, Alexey Kolesnikov, Sidharth Goel, et al.. (2023). Improving variant calling using population data and deep learning. BMC Bioinformatics. 24(1). 197–197. 10 indexed citations
7.
Chang, Pi-Chuan & I‐Chen Liao. (2022). Hydration, barrier of skin and uremic pruritus in patients undergoing hemodialysis: A pilot investigation. Néphrologie & Thérapeutique. 18(6). 498–505. 5 indexed citations
8.
Lin, Yilin, Pi-Chuan Chang, Ching Hsu, et al.. (2022). Comparison of GATK and DeepVariant by trio sequencing. Scientific Reports. 12(1). 1809–1809. 21 indexed citations
9.
Baid, Gunjan, Daniel E. Cook, Kishwar Shafin, et al.. (2022). DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer. Nature Biotechnology. 41(2). 232–238. 89 indexed citations
10.
Markello, Charles, Charles Huang, Álex Rodríguez, et al.. (2022). A complete pedigree-based graph workflow for rare candidate variant analysis. Genome Research. 32(5). 893–903. 5 indexed citations
11.
Sirén, Jouni, Jean Monlong, Xian Chang, et al.. (2021). Pangenomics enables genotyping of known structural variants in 5202 diverse genomes. Science. 374(6574). abg8871–abg8871. 165 indexed citations
12.
Shafin, Kishwar, Trevor Pesout, Pi-Chuan Chang, et al.. (2021). Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads. Nature Methods. 18(11). 1322–1332. 133 indexed citations
13.
Yun, Taedong, Helen Li, Pi-Chuan Chang, et al.. (2020). Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics. 36(24). 5582–5589. 94 indexed citations
14.
Hsieh, Tsung‐Cheng, et al.. (2019). Enteral Access Potentially Endangers Esophageal Carcinoma Patients Under Multi-modality Therapy: A Population-based Study. Anticancer Research. 39(4). 2227–2232.
15.
Baid, Gunjan, Helen Li, & Pi-Chuan Chang. (2019). Using Nucleus and TensorFlow for DNA Sequencing Error Correction. 1 indexed citations
16.
Poplin, Ryan, Pi-Chuan Chang, David H. Alexander, et al.. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology. 36(10). 983–987. 757 indexed citations breakdown →
17.
Petrov, Slav, et al.. (2010). Uptraining for Accurate Deterministic Question Parsing. Empirical Methods in Natural Language Processing. 705–713. 47 indexed citations
18.
Chang, Pi-Chuan, Michel Galley, & Christopher D. Manning. (2008). Optimizing Chinese word segmentation for machine translation performance. 224–232. 198 indexed citations
19.
Chang, Pi-Chuan & Kristina Toutanova. (2007). A Discriminative Syntactic Word Order Model for Machine Translation. Meeting of the Association for Computational Linguistics. 9–16. 25 indexed citations
20.

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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