Lujia Chen

958 total citations
36 papers, 603 citations indexed

About

Lujia Chen is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Lujia Chen has authored 36 papers receiving a total of 603 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 11 papers in Cancer Research and 7 papers in Oncology. Recurrent topics in Lujia Chen's work include Bioinformatics and Genomic Networks (7 papers), Cancer Genomics and Diagnostics (6 papers) and Computational Drug Discovery Methods (5 papers). Lujia Chen is often cited by papers focused on Bioinformatics and Genomic Networks (7 papers), Cancer Genomics and Diagnostics (6 papers) and Computational Drug Discovery Methods (5 papers). Lujia Chen collaborates with scholars based in United States, China and Türkiye. Lujia Chen's co-authors include Xinghua Lu, Gregory F. Cooper, Chunhui Cai, Vicky Chen, Michael Q. Ding, Hao Shen, Yanfang Wang, Mingqiang Li, Zhiqing Hu and H. Wang and has published in prestigious journals such as Journal of Clinical Oncology, Bioinformatics and PLoS ONE.

In The Last Decade

Lujia Chen

34 papers receiving 592 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lujia Chen United States 12 359 112 85 72 70 36 603
Jesse Paquette United States 11 350 1.0× 147 1.3× 111 1.3× 36 0.5× 37 0.5× 13 719
Rand Arafeh Israel 8 330 0.9× 125 1.1× 89 1.0× 31 0.4× 50 0.7× 9 536
Matthew Ung United States 13 521 1.5× 237 2.1× 148 1.7× 64 0.9× 49 0.7× 33 786
Chuanwei Yang United States 15 336 0.9× 111 1.0× 171 2.0× 53 0.7× 30 0.4× 21 577
Verena Becker Germany 12 474 1.3× 95 0.8× 189 2.2× 34 0.5× 31 0.4× 20 789
Edwin E. Jeng United States 8 793 2.2× 81 0.7× 133 1.6× 83 1.2× 24 0.3× 17 973
Min Jin Ha United States 15 325 0.9× 121 1.1× 239 2.8× 51 0.7× 20 0.3× 51 655
Evan Paull United States 11 512 1.4× 188 1.7× 75 0.9× 47 0.7× 22 0.3× 17 664
Yafei Wang China 13 264 0.7× 157 1.4× 123 1.4× 27 0.4× 31 0.4× 32 586
Emmanouil Athanasiadis Greece 18 465 1.3× 87 0.8× 99 1.2× 27 0.4× 124 1.8× 42 963

Countries citing papers authored by Lujia Chen

Since Specialization
Citations

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

Fields of papers citing papers by Lujia Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lujia Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Lujia Chen. A scholar is included among the top collaborators of Lujia Chen 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 Lujia Chen. Lujia Chen 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.
Chen, Lujia, et al.. (2024). Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma. Scientific Reports. 14(1). 30328–30328. 1 indexed citations
2.
Cooper, Gregory F., et al.. (2024). An interpretable deep learning framework for genome-informed precision oncology. Nature Machine Intelligence. 6(8). 864–875. 3 indexed citations
3.
Chen, Jian, et al.. (2024). Cell-membrane targeting sonodynamic therapy combination with FSP1 inhibition for ferroptosis-boosted immunotherapy. Materials Today Bio. 30. 101407–101407. 4 indexed citations
4.
Wang, Jie, Pei Shang, Shan Wang, et al.. (2023). Sauchinone inhibits breast cancer cell proliferation through regulating microRNA‐148a‐3p/HER‐2 axis. Thoracic Cancer. 14(13). 1135–1144. 5 indexed citations
5.
Chen, Lujia, et al.. (2023). Revealing the Impact of Genomic Alterations on Cancer Cell Signaling with an Interpretable Deep Learning Model. Cancers. 15(15). 3857–3857. 2 indexed citations
6.
Sun, Runzi, Hongyu Zhao, Haochen Li, et al.. (2023). Amphiregulin couples IL1RL1 + regulatory T cells and cancer-associated fibroblasts to impede antitumor immunity. Science Advances. 9(34). eadd7399–eadd7399. 31 indexed citations
7.
Chen, Xueer, Lujia Chen, Cornelius Kürten, et al.. (2022). An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors. PLoS Computational Biology. 18(12). e1010761–e1010761. 3 indexed citations
8.
Leibowitz, Brian J., Lin Shen, Lujia Chen, et al.. (2022). Targeting Myc-driven stress vulnerability in mutant KRAS colorectal cancer. Molecular Biomedicine. 3(1). 10–10. 6 indexed citations
9.
Li, Xiangyun, Xiang Xu, Brian J. Leibowitz, et al.. (2020). eIF4E S209 phosphorylation licenses myc- and stress-driven oncogenesis. eLife. 9. 26 indexed citations
10.
Cai, Chunhui, Gregory F. Cooper, Xiaojun Ma, et al.. (2019). Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference. PLoS Computational Biology. 15(7). e1007088–e1007088. 17 indexed citations
11.
Lu, Songjian, Xiao-Nan Fan, Lujia Chen, & Xinghua Lu. (2018). A novel method of using Deep Belief Networks and genetic perturbation data to search for yeast signaling pathways. PLoS ONE. 13(9). e0203871–e0203871. 2 indexed citations
12.
Ding, Michael Q., et al.. (2017). Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics. Molecular Cancer Research. 16(2). 269–278. 112 indexed citations
13.
Yao, Guangyu, et al.. (2017). Pretreatment Hematocrit is Negatively Associated with Response to Neoadjuvant Chemotherapy in Breast Cancer. Biomarkers in Medicine. 11(9). 713–720. 3 indexed citations
14.
Chen, Lujia. (2017). Deep learning models for modeling cellular transcription systems. D-Scholarship@Pitt (University of Pittsburgh). 1 indexed citations
15.
Lu, Songjian, Chunhui Cai, Gonghong Yan, et al.. (2016). Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations. Cancer Research. 76(23). 6785–6794. 3 indexed citations
16.
Chen, Lujia, Chunhui Cai, Vicky Chen, & Xinghua Lu. (2016). Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. BMC Bioinformatics. 17(S1). 9–9. 70 indexed citations
17.
Chen, Lujia, Zoya Voronovich, Michael Walsh, et al.. (2014). Predicting the likelihood of an isocitrate dehydrogenase 1 or 2 mutation in diagnoses of infiltrative glioma. Neuro-Oncology. 16(11). 1478–1483. 62 indexed citations
18.
Chai, Lilong, Ji‐Qin Ni, Claude A. Diehl, et al.. (2012). Ventilation rates in large commercial layer hen houses with two-year continuous monitoring. British Poultry Science. 53(1). 19–31. 30 indexed citations
19.
Chen, An, Xiaoping Chen, Ruizheng Shi, et al.. (2009). [Association of genetic polymorphism in phenylethanolamine-N-methyl transferase with essential hypertension in Changsha Han people].. PubMed. 34(11). 1120–5. 4 indexed citations
20.
Liang, Jie, Peng Chen, Zhiqing Hu, et al.. (2008). Genetic variants in fibroblast growth factor receptor 2 (FGFR2) contribute to susceptibility of breast cancer in Chinese women. Carcinogenesis. 29(12). 2341–2346. 75 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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