Fusong Ju

445 total citations · 1 hit paper
17 papers, 191 citations indexed

About

Fusong Ju is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Fusong Ju has authored 17 papers receiving a total of 191 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 6 papers in Materials Chemistry and 3 papers in Computational Theory and Mathematics. Recurrent topics in Fusong Ju's work include Protein Structure and Dynamics (10 papers), Machine Learning in Bioinformatics (8 papers) and RNA and protein synthesis mechanisms (5 papers). Fusong Ju is often cited by papers focused on Protein Structure and Dynamics (10 papers), Machine Learning in Bioinformatics (8 papers) and RNA and protein synthesis mechanisms (5 papers). Fusong Ju collaborates with scholars based in China, United States and Australia. Fusong Ju's co-authors include Dongbo Bu, Jianwei Zhu, Wei‐Mou Zheng, Tie‐Yan Liu, Bin Shao, Shiwei Sun, Haicang Zhang, Haiguang Liu, He Zhang and Ziheng Lu and has published in prestigious journals such as Nature Communications, Bioinformatics and Genome biology.

In The Last Decade

Fusong Ju

15 papers receiving 186 citations

Hit Papers

align trajectories

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.

Predicting equilibrium distributions for molecular systems with deep learning

2024 59 citations Shuxin Zheng, Yu Shi et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fusong Ju China	7	153	55	39	10	9	17	191
Jeffrey M. Lotthammer United States	8	199 1.3×	36 0.7×	57 1.5×	14 1.4×	4 0.4×	11	243
Marc Vaisset France	9	191 1.2×	66 1.2×	35 0.9×	29 2.9×	15 1.7×	15	232
Paweł Rubach Poland	6	140 0.9×	51 0.9×	17 0.4×	12 1.2×	6 0.7×	18	200
Yasser Mohseni Behbahani France	5	155 1.0×	34 0.6×	15 0.4×	14 1.4×	6 0.7×	8	176
Anup Kumar Germany	8	114 0.7×	23 0.4×	20 0.5×	9 0.9×	4 0.4×	15	168
David Menéndez Hurtado Sweden	7	325 2.1×	133 2.4×	100 2.6×	11 1.1×	7 0.8×	11	357
Sian Xiao United States	10	179 1.2×	39 0.7×	108 2.8×	17 1.7×	12 1.3×	19	236
Yaosen Min China	5	94 0.6×	67 1.2×	79 2.0×	4 0.4×	6 0.7×	8	155
Mirco Michel Sweden	7	312 2.0×	110 2.0×	74 1.9×	6 0.6×	4 0.4×	8	330
Alejandro Varela‐Rial Spain	6	114 0.7×	76 1.4×	109 2.8×	11 1.1×	7 0.8×	8	183

Countries citing papers authored by Fusong Ju

Since Specialization

Citations

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

Fields of papers citing papers by Fusong Ju

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fusong Ju

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

All Works

Sort: Min cites: Since: Top N: Style:

17 of 17 papers shown

Ju, Fusong, et al.. (2025). Matrix Is All You Need: Rearchitecting Quantum Chemistry to Scale on AI Accelerators. 2126–2142.

He, Liang, Pan Deng, Z. G. Zhao, et al.. (2025). Accelerating protein engineering with fitness landscape modelling and reinforcement learning. Nature Machine Intelligence. 7(9). 1446–1460. 3 indexed citations

Zheng, Shuxin, Yu Shi, Ziheng Lu, et al.. (2024). Predicting equilibrium distributions for molecular systems with deep learning. Nature Machine Intelligence. 6(5). 558–567. 59 indexed citations breakdown →

Ju, Fusong, et al.. (2024). Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials. Communications Biology. 7(1). 297–297. 5 indexed citations

Ju, Fusong, Andrew J. Jenkins, Jia Zhang, et al.. (2024). Acceleration without Disruption: DFT Software as a Service. Journal of Chemical Theory and Computation. 20(24). 10838–10851. 6 indexed citations

Ju, Fusong, Zhiyuan Chen, Liang He, et al.. (2023). CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms. Genome biology. 24(1). 266–266. 5 indexed citations

Ju, Fusong, et al.. (2023). SASA-Net: A Spatial-Aware Self-Attention Mechanism for Building Protein 3D Structure Directly From Inter- Residue Distances. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(6). 3482–3488. 1 indexed citations

Huang, Bin, Fusong Ju, Jianwei Zhu, et al.. (2023). Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms. Genomics Proteomics & Bioinformatics. 21(5). 913–925. 23 indexed citations

Ju, Fusong, Wei‐Mou Zheng, Jianwei Zhu, et al.. (2022). ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs. Journal of Computational Biology. 29(2). 92–105. 7 indexed citations

10.

Zhang, He, et al.. (2021). Co-evolution Transformer for Protein Contact Prediction. Neural Information Processing Systems. 34. 5 indexed citations

11.

Huang, Bin, Bing Wang, Fusong Ju, et al.. (2021). Filling gaps of genome scaffolds via probabilistic searching optical maps against assembly graph. BMC Bioinformatics. 22(1). 533–533.

12.

Ju, Fusong, et al.. (2021). FALCON2: a web server for high-quality prediction of protein tertiary structures. BMC Bioinformatics. 22(1). 439–439. 5 indexed citations

13.

Ju, Fusong, Jianwei Zhu, Bin Shao, et al.. (2021). CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction. Nature Communications. 12(1). 2535–2535. 52 indexed citations

14.

Ju, Fusong, et al.. (2021). Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction. Bioinformatics. 38(4). 990–996. 3 indexed citations

15.

Zhu, Jianwei, et al.. (2020). ISSEC: inferring contacts among protein secondary structure elements using deep object detection. BMC Bioinformatics. 21(1). 503–503. 3 indexed citations

16.

Ju, Fusong, Jingwei Zhang, Dongbo Bu, et al.. (2019). De novo glycan structural identification from mass spectra using tree merging strategy. Computational Biology and Chemistry. 80. 217–224. 7 indexed citations

17.

Zhang, Haicang, Fusong Ju, Jianwei Zhu, et al.. (2019). Predicting protein inter-residue contacts using composite likelihood maximization and deep learning. BMC Bioinformatics. 20(1). 537–537. 7 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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