About

Frederick R. Chen is an Assistant Professor in the Department of Political Science at The Ohio State University. His research explores the intersection of economics and security in international relations, with a particular focus on the domestic political mechanisms that shape these dynamics. He examines two interconnected themes: the political economy of international security, which investigates how domestic and global economic forces influence international conflict and foreign policy; and the role of domestic institutions in shaping international economic cooperation, including trade, foreign direct investment, and corporate behavior. His projects span multiple substantive areas, such as political economy, international security, foreign policy, and public opinion. Chen’s work has been published or is forthcoming in journals including International Organization, the Journal of Politics, World Politics, and the Review of International Organizations. He has received awards such as the David A. Lake Best Paper Award from the International Political Economy Society in 2020 and the Genevieve Gorst Herfurth Award for outstanding doctoral student research from the University of Wisconsin–Madison in 2019–20. His research has been supported by the Ministry of Education, Singapore. Chen holds a Ph.D. in Political Science from the University of Wisconsin–Madison, an M.A. in International Relations from Peking University, and a B.A. in International Politics from Tsinghua University. Prior to his current position, he was an assistant professor at the S. Rajaratnam School of International Studies at Nanyang Technological University, Singapore, and a pre-doctoral fellow at the Center for International Security and Cooperation at Stanford University.

Research topics

• Biology
• Cancer research
• Bioinformatics
• Genetics
• Computational biology
• Immunology

Selected publications

• Correction: MicroRNA-101 Targets CXCL12-Mediated Akt and Snail Signaling Pathways to Inhibit Cellular Proliferation and Invasion in Papillary Thyroid Carcinoma

  Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · 2025-01-01

  erratumOpen access1st authorCorresponding

  [This corrects the article DOI: 10.3727/096504018X15426763753594.].

  PublisherDOI

• 20-HETE: Its potential role in physiological and pathophysiological processes

  Biochemical Pharmacology · 2025-08-18 · 2 citations

  reviewOpen access

  20-Hydroxyeicosatetraenoic acid (20-HETE) is a critical regulator of multiorgan homeostasis. Physiologically, it maintains vascular tone by promoting vascular smooth muscle cell (VSMC) contraction and stabilizes blood pressure. Concurrently, 20-HETE facilitates vascular VSMC and endothelial cell proliferation and migration. This dual action drives vascular repair and remodeling. Through modulation of nitric oxide (NO) metabolism, it further regulates endothelial function. Pathologically, however, excessive 20-HETE synthesis associates with metabolic disorders. These include hypertension, obesity, diabetes, and non-alcoholic fatty liver disease. Importantly, 20-HETE exhibits organ-specific dual roles: (1) Renal - While maintaining sodium homeostasis, regulating renal blood flow, and modulating blood pressure under physiological conditions, it paradoxically promotes renal fibrosis and tubular injury in pathological states. (2) Brain - Physiologically preserves cerebrovascular homeostasis through vascular myogenic control of cerebral blood flow autoregulation and blood-brain barrier integrity; pathologically exacerbates cerebral injury via oxidative endothelial damage and neuroinflammatory pathways. (3) Lung - Sustains vascular function by stimulating NO-mediated pulmonary artery vasodilation and maintaining cellular viability through moderate reactive oxygen species modulation. Although drug development targeting 20-HETE has demonstrated therapeutic potential in animal models, using synthetic enzyme inhibitor HET0016 and receptor antagonist AAA, its clinical translation still faces challenges related to dual signaling pathways and precise targeting. Understanding the mechanism of action and regulatory pathways of 20-HETE may open new avenues for the diagnosis and treatment of these diseases.

  PublisherDOI

• Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis

  Frontiers in Oncology · 2025-08-25 · 2 citations

  articleOpen access

  Objectives Lymph node metastasis (LNM) is an important factor affecting the stage and prognosis of patients with lung adenocarcinoma. The purpose of this study is to explore the predictive value of the stacking ensemble learning model based on 18 F-FDG PET/CT radiomic features and clinical risk factors for LNM in lung adenocarcinoma, and elucidate the biological basis of predictive features through pathological analysis. Methods Ninety patients diagnosed with lung adenocarcinoma who underwent PET/CT were retrospectively analyzed and randomly divided into the training and testing sets in a 7:3 ratio. Stacking ensemble learning models were developed based on radiomic features combined with clinical risk factors. The predictive performance of each model was assessed through area under the curve (AUC). Additionally, Spearman’s correlation analysis was employed to investigate the association between features predicting LNM and pathological features. Results Multifactorial logistic regression identified the bronchial cut-off sign and serum carcinoembryonic antigen (CEA) as clinical risk factors. The Stacking-combined model demonstrated superior diagnostic efficacy compared with logistic regression, random forest, and naive Bayes-combined models, with AUC values of 0.971 and 0.901 in the training and testing sets, respectively. Despite the absence of FDR-significant radiomic-pathomic correlations (all q > 0.05), exploratory analysis revealed nominal associations (uncorrected P < 0.05) for partial feature pairs. Crucially, radiomic features demonstrated strong associations with Ki-67 expression: PET_GLRLM_LongRunHigh GreyLevelEmphasis (r = 0.610, q < 0.001) and CT_INTENSITY-BASED_Intensity BasedEnergy (r = 0.332, q = 0.004). Conclusions The stacking ensemble learning model based on 18 F-FDG PET/CT radiomics demonstrates potential for predicting LNM in lung adenocarcinoma, and the quantitative analysis of radiomic features holds significant biological significance.

  PublisherOA PDFDOI

• Learning‐Based Classification of B‐ and T‐Cell Lymphoma on Histopathological Images: A Multicenter Study

  European Journal Of Haematology · 2025-05-13

  articleOpen access

  Lymphoma, a clonal malignancy from lymphocytes, includes diverse subtypes requiring distinct immunohistochemical stains for accurate diagnosis. Limited biopsy specimens often restrict the use of multiple stains, complicating diagnostic workflows. Lymphomas are typically classified into B-cell and T-cell types, each with specific markers. This study represents the first feasibility study in deploying deep learning models for B- and T-cell lymphoma classification on histopathological images. We analyzed 1510 H&E-stained sections (750 B-cell, 760 T-cell) with CNN models (Xception, NASNetL, ResNet50, EfficientNet), enhanced by Convolutional Block Attention Modules (CBAMs). All models demonstrated strong classification capabilities, with EfficientNet achieving the highest accuracy at 91.5% and the best precision at 91.9%, while Xception performed the best recall at 91.5%. Furthermore, the deep learning models significantly outperformed human pathologists in classification accuracy and inference speed, processing images in milliseconds compared to the several seconds required for manual diagnosis. These findings underscore the effectiveness of advanced CNN models in improving diagnostic precision while reducing dependency on manual staining and interpretation, and the integration of AI-driven classification can provide valuable support for pathologists.

  PublisherOA PDFDOI

• Exploring adolescents' mental health, cyberbullying perpetration, and interpersonal relationships: A latent profile and moderation approach

  Journal of Affective Disorders · 2025-07-02 · 2 citations

  article
  PublisherDOI

• Melatonin Alleviates Retina Angiogenesis by Targeting Fibronectin and the <scp>VEGF</scp> Pathway

  The FASEB Journal · 2025-09-30 · 1 citations

  article1st author

  Diabetic retinopathy (DR) and retinopathy of prematurity (ROP) continue to be significant causes of vision impairment despite the well-established role of vascular endothelial growth factor (VEGF) in pathological angiogenesis. We still need to deeply understand retinal angiogenesis's molecular mechanisms and identify potential alternate therapeutic targets. We used RNA sequencing (RNA-seq) and found fibronectin (FN1), an extracellular matrix protein, was significantly upregulated during retinal angiogenesis in the oxygen-induced retinopathy (OIR) model. Employing a deep learning model (BioNet) to identify potential FN1 inhibitors among FDA-approved drugs, we discovered that melatonin effectively reduced FN1 expression and inhibited VEGF-induced angiogenesis by decreasing VEGFR2 phosphorylation. In vivo, melatonin administration significantly reduced preretinal tufts in the OIR model while suppressing FN1 expression and VEGFR2 activation. This study highlights the power of computer-driven drug discovery, with BioNet successfully identifying melatonin as a potential therapeutic agent for retinal angiogenesis. The ability of melatonin to inhibit both FN1 and VEGF signaling highlights the potential of integrating advanced computational methods with rigorous experimental validation to uncover novel therapies for complex diseases.

  PublisherDOI

• Utility of peak myocardial work index: a nonproprietary method for myocardial work evaluation in patients with cardiomyopathy

  The International Journal of Cardiovascular Imaging · 2025-11-13

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Ints7 deficiency activates DNA damage response to elicit resurgence of endogenous retrovirus <i>MERVL</i> and anastasis of embryonic stem cells

  Nucleic Acids Research · 2025-08-11

  articleOpen access

  The murine endogenous retrovirus MERVL is dynamically activated in a small population of in vitro cultured mouse embryonic stem cells (mESCs) exhibiting totipotent-like features. Yet, the relationship between MERVL activation and cell fate decisions of mESCs is incompletely understood. Through a genome-wide knockout screen, we discovered that MERVL activity is intrinsically linked to DNA damage response pathways. Loss of Ints7, a backbone subunit of the Integrator complex, increased DNA damage and triggered MERVL expression. Mechanistically, Ints7 depletion induced phosphorylation of Kap1, increased chromatin accessibility at MERVL loci, and activated the p53-Dux axis to drive MERVL transcription. Intriguingly, DNA damage-induced MERVL resurgence followed the cleavage of caspase-3, often accompanying a process known as anastasis-cell survival after transient apoptotic signaling. Collectively, our study uncovered that MERVL activation in mESCs is integrated into the cellular circuit for decision-making in response to DNA damage, suggesting that sublethal caspase activation can influence the developmental potential of stem cells.

  PublisherDOI

• School Bullying Victimization and Depressive Symptoms Among College Students: The Role of Rumination and Different Social Support

  Psychiatric Quarterly · 2025-07-12 · 2 citations

  article
  PublisherDOI

• SelfMedHPM: Self Pre-training With Hard Patches Mining Masked Autoencoders For Medical Image Segmentation

  ArXiv.org · 2025-04-03

  preprintOpen accessSenior author

  In recent years, deep learning methods such as convolutional neural network (CNN) and transformers have made significant progress in CT multi-organ segmentation. However, CT multi-organ segmentation methods based on masked image modeling (MIM) are very limited. There are already methods using MAE for CT multi-organ segmentation task, we believe that the existing methods do not identify the most difficult areas to reconstruct. To this end, we propose a MIM self-training framework with hard patches mining masked autoencoders for CT multi-organ segmentation tasks (selfMedHPM). The method performs ViT self-pretraining on the training set of the target data and introduces an auxiliary loss predictor, which first predicts the patch loss and determines the location of the next mask. SelfMedHPM implementation is better than various competitive methods in abdominal CT multi-organ segmentation and body CT multi-organ segmentation. We have validated the performance of our method on the Multi Atlas Labeling Beyond The Cranial Vault (BTCV) dataset for abdomen mult-organ segmentation and the SinoMed Whole Body (SMWB) dataset for body multi-organ segmentation tasks.

  PublisherOA PDFDOI

Frequent coauthors

• David Myung

  Smith-Kettlewell Eye Research Institute

  88 shared

• Peter Le

  Stanford University

  53 shared

• Miqin Zhang

  Lanzhou Jiaotong University

  52 shared

• Bin Li

  Dali University

  36 shared

• Li Wang

  Shandong University

  36 shared

• Wei Chen

  36 shared

• Weibin Bai

  Jinan University

  36 shared

• Gabriella Maria Fernandes-Cunha

  33 shared

Education

• PhD, Program of Materials Science and Engineering, Department of Nanoengineering

  University of California San Diego

  2019

• Master's

  Shanghai Institute of Ceramics Chinese Academy of Sciences

  2011

• Bachelor's, Materials Science and Engineering

  Southeast University

  2008

Awards & honors

• David A. Lake Best Paper Award from the International Politi…
• Genevieve Gorst Herfurth Award for outstanding doctoral stud…