Journal of Targeted Oncology Research

Haizhou Liu

PhD

  • Department: Cancer Immunotherapy
  • University: Fujian Medical University
  • Country: China

Dr. Haizhou Liu is an Assistant Professor at the Fujian Provincial Key Laboratory of Cancer Immunotherapy, The First Affiliated Hospital of Fujian Medical University, China. He obtained his Ph.D. in Biomedical Engineering (Bioinformatics direction) from Nanjing University of Aeronautics and Astronautics (NUAA) in 2022, following a combined BD and MD degree in Basic Medicine (Bioinformatics direction) from Harbin Medical University (HMU). Dr. Liu has an extensive background in bioinformatics, pharmacogenomics, and network pharmacology, with a particular focus on cancer research. He has published multiple peer-reviewed articles in leading journals such as Genome Medicine, Journal of Molecular Biology, Molecular Therapy Nucleic Acids, and NPJ Precision Oncology. He actively contributes to academic communities as a member of several professional organizations, including the Chinese Association of Automation (CAA), the China Computer Federation (CCF), the Chinese Association for Artificial Intelligence (CAAI), and the Chinese Society of Biotechnology. Dr. Liu has also served as a reviewer for international conferences like BIBM and ICIC.

1. Editorial and Reviewing Work

  • Reviewer, IEEE International Conference on Bioinformatics and Biomedicine (BIBM) – 2022, 2021

  • Reviewer, International Conference on Intelligent Computing (ICIC) – 2019

2. Conference Presentations and Participation

  • 2023.2 – The Bioinformatics and Intelligent Information Processing Conference, Guangzhou

    • Oral Presentation: “CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research.”

  • 2019.9 – China Conference on Systems Biology, Shanghai

    • Poster Presentation (Second Prize for Excellent Poster)

    • Presented: “ncPEA: A novel method for pathway enrichment analysis of coding genes and non-coding RNAs through integrating pathway crosstalk.”

  • 2018.11 – The 8th Jiangsu Bioinformatics Academic Conference, Jiangsu

    • Oral Presentation: “Drug Resistance-Related Competing Interactions of lncRNA and mRNA across 19 Cancer Types.”

3. Professional Memberships and Affiliations

  • Member, Intelligent Health and Bioinformatics Committee, Chinese Association of Automation (CAA)

  • Member, BIO-3NEW Young Scholars Executive Committee (Bioinformatics), China Computer Federation (CCF)

  • Member, Bioinformatics and Artificial Life Committee, Chinese Association for Artificial Intelligence (CAAI)

  • Member, Computational Biology and Bioinformatics Committee, Chinese Society of Biotechnology

4. Research Collaboration and Leadership

  • Active participant in multi-institutional collaborative research in cancer bioinformatics and pharmacogenomics.

  • Contributor to the development of several computational tools and databases for cancer genomics (e.g., CTpathway, ncEP, scDR, DRdriver).

5. Awards and Honors Related to Scientific Work

  • National Scholarship, Ministry of Education of China (2021)

  • Advanced Individual for Scientific Research and Innovation, Nanjing University of Aeronautics and Astronautics (2021, 2020, 2019)

  • Special Scholarship, NUAA (2020)

  • Excellent Student Cadre, Harbin Medical University (2012)

  • Drug Resistance-Related Competing Interactions of lncRNA and mRNA across 19 Cancer Types
  • ncEP: A Manually Curated Database for Experimentally Validated ncRNA-encoded Proteins or Peptides
  • CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research
  • LncRNA-Encoded Peptide: Functions and Predicting Methods
  • Integrative Analysis of Regulatory Module Reveals Associations of Microgravity with Dysfunctions of Multi-body Systems and Tumorigenesis
  • ncFN: a comprehensive non-coding RNA function annotation framework based on a global and heterogeneous biomolecular network
  • Exploring molecular and modular insights into space ionizing radiation effects through heterogeneous gene regulatory networks
  • HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients