Jamesjin63

The World Within Data

Jin-xin Zheng is a Ph.D. at The National Institute of Parasitic Diseases China CDC(2018-2021). He obtained his bachelor’s degree from Anhui Medical University in 2015, enrolled in a one-year master’s training program at Nanjing Medical University, and joined the Jiangsu Institute of Parasitic Diseases in 2016 to complete his master’s program. He has three years of study experience in Public Health, specializing in biostatistics and modeling methods within Infectious diseases. His research focuses on One Health issues globally, utilizing machine learning algorithms and ecological modeling to explore infectious diseases (COVID-19, malaria, dengue, schistosomiasis, liver fluke) and non-infectious diseases. He has also collaborated with clinicians on trial setup and data analysis by applying machine learning methods to clinical research. With a passion for R and Python statistical tools for medical data analysis and visualization.

  • Perform the the **International Development Research Centre (IDRC) **project about the research of disease modeling in South Asia areas.
  • Join the Control and elimination of Helminth Zoonoses in the Greater Mekong Subregion, mainly focus on data collecting and design models to test the risk areas of parasites diseases.

Education

July 2021 - Now
Post Doctoral Ruiin Hospital-Shanghai Jiao Tong University School of Medicine
September 2018 - July 2021
Ph.D. Candidate--Epidemiology and Biostatistics National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, P.R. China
July 2016 - July 2018
Graduate student School: Jiangsu Institute of Parasitic Diseases. Wuxi China.
September 2015 - July 2016
Graduate student School: Nanjing Medical University. Nanjing China.
September 2010-July 2015
Undergraduate student School: Anhui Medical University. Hefei China.

Experience

Machine Learning in Parasitology:

  • Using the system review methods to summarize the parasitic diseases, including schistosomiasis, liver fluke, and malaria. Using spatial approaches (Machine learning) to study the disease distribution patterns between environment and ecological features for disease transmission and dispersal, etc.

Machine Learning for Cancer:

  • Using clinical information, DNA methylation and gene expression variables to evaluate the prognosis of diseases. We provided a modeling solution (machine learning and statistical methods for biomarkers and its interaction terms) to predict the outcomes of lung cancer, Thyroid, and prognosis with CKD.

Global Burden of Diseases:

  • Most comprehensive global study—analyzing 286 causes of death, 369 diseases and injuries, and 87 risk factors in 204 countries and territories—reveals how well the world’s population were under those diseases’ patterns. Using data analysis and visualization to describe those trends around the world and found interesting results.

Modeling in Infectious disease:

  • We use modeling methods to study infectious diseases like COVID-19, Y. enterocolitis, and Dengue infection. Our approach involves data-driven machine learning models to analyze disease transmission patterns in different scenarios.

Publications

  1. Zheng JX, Lv S, Tian LG, Guo ZY, Zheng PY, Chen YL, Guan SY, Wang WM, Zhang SX. The rapid and efficient strategy for SARS-CoV-2 Omicron transmission control: analysis of outbreaks at the city level. Infect Dis Poverty. 2022 Nov 24;11(1):114.
  2. Zhang D, Zheng JX. The Burden of Childhood Asthma by Age Group, 1990-2019: A Systematic Analysis of Global Burden of Disease 2019 Data. Front Pediatr. 2022 Feb 16;10:823399.
  3. Liu Y, Zheng JX, Hao J, Wang RR, Liu X, Gu P, Yu H, Yu Y, Wu C, Ou B, Peng Z. Global burden of primary liver cancer by five etiologies and global prediction by 2035 based on global burden of disease study 2019. Cancer Med. 2022 Mar;11(5):1310-1323.
  4. Li Y, Zheng JX, Deng Y, Deng X, Lou W, Wei B, Xiang D, Hu J, Zheng Y, Xu P, Yao J, Zhai Z, Zhou L, Yang S, Wu Y, Kang H, Dai Z. Global Burden of Female Breast Cancer: Age-Period-Cohort Analysis of Incidence Trends From 1990 to 2019 and Forecasts for 2035. Front Oncol. 2022 Jun 9;12:891824.
  5. Liu TX, Zheng JX, Chen Z, Zhang ZC, Li D, Shi LP. An interpretable machine-learning model for predicting the efficacy of nonsteroidal anti-inflammatory drugs for closing hemodynamically significant patent ductus arteriosus in preterm infants. Front Pediatr. 2023 Apr
  6. Hui-Hui Zhu, Ji-lei Huang , Chang-Hai Zhou , Ting-Jun Zhu , Zheng JX , Mi-Zhen Zhang , Men-Bao Qian ,Ying-Dan Chen ,Shi-zhu Li “Soil-transmitted helminthiasis in mainland China from 2016 to 2020: a population-based study.” The Lancet Regional Health–Western Pacific (2023).
  7. Zheng JX, Xia S, Lv S, Zhang Y, Bergquist R, Zhou XN. Infestation risk of the intermediate snail host of Schistosoma japonicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach. Infect Dis Poverty. 2021 May 20;10(1):74.
  8. Zheng JX, Shi B, Xia S, Yang G, Zhou XN. Spatial patterns of Plasmodium vivax transmission explored by multivariate auto-regressive state-space modelling - A case study in Baoshan Prefecture in southern China. Geospat Health. 2021 Mar 12;16(1).
  9. Zhu J, Zheng JX, Li L, Huang R, Ren H, Wang D, Dai Z, Su X. Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma. Front Med (Lausanne). 2021 Mar 9;8: 635771.
  10. Shi B, Zheng JX, Xia S, Lin S, Wang X, Liu Y, Zhou XN, Liu J. Accessing the syndemic of COVID-19 and malaria intervention in Africa. Infect Dis Poverty. 2021 Jan 7;10(1):5.
  11. Tian N, Zheng JX, Guo ZY, Li LH, Xia S, Lv S, Zhou XN. Dengue Incidence Trends and Its Burden in Major Endemic Regions from 1990 to 2019. Trop Med Infect Dis. 2022 Aug 12;7(8):180.
  12. Yue Y, Zheng JX, Sheng M, Liu X, Hao Q, Zhang S, Xu S, Liu Z, Hou X, Jing H, Liu Y, Zhou X, Li Z. Public health implications of Yersinia enterocolitica investigation: an ecological modeling and molecular epidemiology study. Infect Dis Poverty. 2023 Apr 21;12(1):41.

Internships

  • WHO Western Pacific | World Health Organization**[2019/09-2019-12]**

As an intern in the NTD group, data manipulation, and collection for NTD modeling surveillance and mapping. Working on neglected tropical diseases include several parasitic and using WHO reported data to make risk map and predictions for schistosomiasis, liver fluke.

Expertise

  • Computational biology
  • machine learning & decisions interpretable
  • Data visualization
  • Geographic and mapping

Programs

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流行病, 临床统计,数据科学,机器学习, R语言

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