Peicong Lin

430 total citations
7 papers, 118 citations indexed

About

Peicong Lin is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Peicong Lin has authored 7 papers receiving a total of 118 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 3 papers in Materials Chemistry. Recurrent topics in Peicong Lin's work include Protein Structure and Dynamics (4 papers), Computational Drug Discovery Methods (3 papers) and Chemical Synthesis and Analysis (2 papers). Peicong Lin is often cited by papers focused on Protein Structure and Dynamics (4 papers), Computational Drug Discovery Methods (3 papers) and Chemical Synthesis and Analysis (2 papers). Peicong Lin collaborates with scholars based in China. Peicong Lin's co-authors include Sheng‐You Huang, Jiahua He, Yumeng Yan, Ji Chen, Huanyu Tao, Xuejun Zhao and Qilong Wu and has published in prestigious journals such as Nature Communications, Bioinformatics and Proteins Structure Function and Bioinformatics.

In The Last Decade

Peicong Lin

7 papers receiving 118 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Peicong Lin China	7	92	36	26	20	7	7	118
Tunde Aderinwale United States	8	159 1.7×	70 1.9×	26 1.0×	22 1.1×	10 1.4×	10	198
P. Reinke Germany	5	83 0.9×	70 1.9×	24 0.9×	11 0.6×	4 0.6×	9	154
Raj S. Roy United States	8	149 1.6×	70 1.9×	24 0.9×	36 1.8×	10 1.4×	14	191
Anna Shiriaeva United States	7	134 1.5×	48 1.3×	26 1.0×	11 0.6×	9 1.3×	9	181
Yasuyo Ikegawa Japan	4	129 1.4×	59 1.6×	6 0.2×	29 1.4×	4 0.6×	6	156
Takahiro KUDOU Japan	4	123 1.3×	60 1.7×	6 0.2×	16 0.8×	3 0.4×	6	145
John Vant United States	5	62 0.7×	33 0.9×	31 1.2×	8 0.4×	9 1.3×	12	87
Naozumi Hiranuma United States	4	167 1.8×	62 1.7×	5 0.2×	28 1.4×	2 0.3×	5	211
L. Launer France	3	146 1.6×	163 4.5×	25 1.0×	12 0.6×	2 0.3×	3	216
Ivan Vulovic United States	5	119 1.3×	42 1.2×	15 0.6×	4 0.2×	3 0.4×	5	163

Countries citing papers authored by Peicong Lin

Since Specialization

Citations

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

Fields of papers citing papers by Peicong Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peicong Lin. 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 Peicong Lin. The network helps show where Peicong Lin may publish in the future.

Co-authorship network of co-authors of Peicong Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Peicong Lin. A scholar is included among the top collaborators of Peicong Lin 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 Peicong Lin. Peicong Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

1.

Lin, Peicong, et al.. (2024). Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches. Current Opinion in Structural Biology. 85. 102789–102789. 7 indexed citations

2.

Lin, Peicong, Yumeng Yan, Huanyu Tao, & Sheng‐You Huang. (2023). Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes. Nature Communications. 14(1). 4935–4935. 12 indexed citations

3.

Tao, Huanyu, Qilong Wu, Xuejun Zhao, Peicong Lin, & Sheng‐You Huang. (2022). Efficient 3D conformer generation of cyclic peptides formed by a disulfide bond. Journal of Cheminformatics. 14(1). 26–26. 12 indexed citations

4.

He, Jiahua, et al.. (2022). Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly. Nature Communications. 13(1). 4066–4066. 48 indexed citations

5.

Tao, Huanyu, et al.. (2022). Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy. Bioinformatics. 38(17). 4109–4116. 11 indexed citations

6.

Lin, Peicong, Yumeng Yan, & Sheng‐You Huang. (2022). DeepHomo2.0: improved protein–protein contact prediction of homodimers by transformer-enhanced deep learning. Briefings in Bioinformatics. 24(1). 17 indexed citations

7.

Yan, Yumeng, et al.. (2020). Challenges and opportunities of automated protein‐protein docking: HDOCK server vs human predictions in CAPRI Rounds 38‐46. Proteins Structure Function and Bioinformatics. 88(8). 1055–1069. 11 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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