Jin-Ji Yang | Pulmonary oncology | Personalized Medicine Innovation

Personalized Medicine Innovation

Jin-Ji Yang
Guangdong Provincial People’s Hospital

Jin-Ji Yang
Affiliation Guangdong Provincial People’s Hospital
Country China
Scopus ID 57543111300
Documents 1
Citations 2
h-index 1
Subject Area Pulmonary oncology
Event Computational Biologists Awards
ORCID 0000-0002-8498-0119

Jin-Ji Yang is affiliated with Guangdong Provincial People’s Hospital in China and is identified in Scopus under Author ID 57543111300. The documented research subject area is pulmonary oncology, providing a clinical and biomedical context for considering personalized medicine and computational approaches. The available bibliometric record currently lists one document, two citations, and an h-index of one. [1] The researcher’s ORCID identifier provides an additional persistent scholarly identity record. [2]

Abstract

Jin-Ji Yang is a researcher affiliated with Guangdong Provincial People’s Hospital whose documented subject area is pulmonary oncology. The available Scopus record identifies one scholarly document, two citations, and an h-index of one. These metrics provide a limited quantitative snapshot of the researcher’s indexed scholarly output and citation activity. [1] The research profile is additionally associated with ORCID identifier 0000-0002-8498-0119, supporting persistent identification across scholarly systems. [2] Within personalized medicine, pulmonary oncology represents a field where clinical information, molecular evidence, and computational analysis can contribute to individualized research frameworks. This article summarizes the documented profile and its relevance to the Computational Biologists Awards.

Keywords

Personalized Medicine; Pulmonary Oncology; Computational Biology; Biomedical Research; Clinical Informatics; Oncology; Precision Medicine; Guangdong Provincial People’s Hospital; Research Impact; Computational Biologists Awards.

Introduction

Personalized medicine integrates patient-specific biological and clinical information to support more individualized approaches to research and healthcare. Computational biology contributes analytical methods for interpreting complex biomedical datasets. Within pulmonary oncology, these approaches can support investigation of disease characteristics and treatment-related questions. Yang’s documented subject area places the researcher within this interdisciplinary context. [1]

Research Profile

Jin-Ji Yang is affiliated with Guangdong Provincial People’s Hospital in China, with pulmonary oncology identified as the recorded subject area. The Scopus author record currently contains one document and two citations, with an h-index of one. [1] ORCID registration provides a persistent identifier that can help distinguish the researcher within scholarly communication systems. [2]

Research Contributions

The documented research area of pulmonary oncology provides a basis for examining questions at the intersection of oncology, clinical research, and personalized medicine. Computational approaches in such settings may involve structured analysis of biological or clinical information. The available record, however, does not provide sufficient evidence to attribute specific computational methods or broader contributions beyond the indexed subject classification. [1]

Publications

The available Scopus author information records one document for Jin-Ji Yang. [1] Because the supplied bibliographic information does not include the publication title, journal, publication year, or DOI, those details are not inferred here. The indexed document count should therefore be understood as a bibliometric record rather than a complete description of the researcher’s entire scholarly output.

Research Impact

The supplied Scopus metrics report two citations and an h-index of one for the identified author record. [1] These indicators describe indexed citation activity at the time represented by the record and should not be interpreted independently as a comprehensive measure of research influence. Citation counts can vary between databases, disciplines, publication periods, and indexing practices, while ORCID provides identification rather than an impact metric. [2]

Award Suitability

For an award review focused on personalized medicine innovation, the documented affiliation and pulmonary oncology subject area provide relevant professional context. [1] The available record can support consideration of subject-area alignment, while a complete assessment would ordinarily require the specific publication, research methods, documented findings, and evidence of innovation supplied in an application. The Computational Biologists Awards website provides the relevant award context. [3]

Conclusion

Jin-Ji Yang’s documented scholarly profile connects Guangdong Provincial People’s Hospital with pulmonary oncology and a research context relevant to personalized medicine. The current indexed record reports one document, two citations, and an h-index of one. [1] ORCID further establishes a persistent researcher identifier. [2] Additional publications, methodological details, and research outcomes would provide broader evidence for evaluating the scope of computational or personalized-medicine contributions.

References

  1. Elsevier. (n.d.). Scopus author details: Jin-Ji Yang, Author ID 57543111300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57543111300
  2. ORCID. (n.d.). Jin-Ji Yang — ORCID record. ORCID.
    https://orcid.org/0000-0002-8498-0119
  3. Computational Biologists Awards. (2026.)  Computational Biologists Awards.
    https://computationalbiologists.com/
  4. Research Article (2021. ) Clinicopathological features and resistance mechanisms in HIP1-ALK-rearranged lung cancer: A multicenter study.
    https://doi.org/10.1002/gcc.23005

Eyachew Misganew Tegaw | AI in Personalized Medicine | Best Researcher Award

Assist. Prof. Dr. Eyachew Misganew Tegaw | AI in Personalized Medicine | Best Researcher Award

Assistant Professor of Medical Physics at  Debre Tabor University, Ethiopia.

Dr. Eyachew Misganew Tegaw is an Assistant Professor of Medical Physics at Debre Tabor University, Ethiopia. He earned his Ph.D. from Tehran University of Medical Sciences, specializing in intraoperative radiotherapy using nanoparticles for breast cancer treatment. His research spans medical imaging, radiation therapy, nanomedicine, and AI applications in oncology. Dr. Eyachew Misganew Tegaw has published extensively in peer-reviewed journals and actively contributes to scientific advancement through research, mentorship, and international collaboration.

👨‍🎓 Educational Background:

Eyachew Misganew Tegaw holds a Ph.D. in Medical Physics from the Tehran University of Medical Sciences (TUMS), Iran, where he specialized in intraoperative radiotherapy (IORT) using nanoparticles for breast cancer treatment. His academic journey began with a B.Sc. in Applied Physics from the University of Gondar, followed by an M.Sc. in Condensed Matter Physics from Mekelle University. Across these programs, he built a strong foundation in quantum mechanics, computational physics, and radiation therapy physics, gaining multidisciplinary expertise in both theoretical and applied domains.

Profile:

👨‍🏫 Professional Experience:

With over a decade of academic service, Dr. Eyachew Misganew Tegaw currently serves as an Assistant Professor in the Department of Physics at Debre Tabor University, Ethiopia. He previously held the position of Lecturer at the same institution, actively contributing to curriculum development and student mentorship. His leadership experience includes a two-year term as Department Head and three years as Chair of the Ethiopian Space Science Society (Debre Tabor Branch), showcasing his commitment to both academic and community development.

🧪 Research Interests:

Dr. Eyachew Misganew Tegaw is a highly driven researcher with diverse interests spanning medical physics, radiotherapy technologies, medical imaging, radiation dosimetry, nanomedicine, Monte Carlo simulations, and artificial intelligence. His work integrates cutting-edge approaches like deep learning and machine learning with traditional medical physics techniques, aiming to enhance cancer treatment planning and medical imaging. He is especially passionate about nanoparticle-enhanced therapies and computational modeling for improved patient outcomes in oncology.

🔍 Areas for Improvement:

While Dr. Eyachew Misganew Tegaw has made significant strides in academic research, increased visibility through invited talks, expanded international partnerships, and higher engagement in translational clinical projects could further enhance his research impact. Diversifying funding sources and mentoring emerging researchers may also solidify his standing in the global scientific community.

📚 Scientific Contributions

Dr. Eyachew has authored and co-authored numerous peer-reviewed journal articles in high-impact scientific journals. His publications cover a wide range of topics such as Monte Carlo simulations for radiotherapy, nanoparticle-enhanced imaging and treatment, and explainable AI in oncology. Notable among his recent works is a 2025 article in Scientific Reports comparing survival outcomes in breast cancer surgery, and another accepted paper focusing on AI for lung cancer survival prediction. His systematic reviews and meta-analyses have also significantly contributed to evidence-based practice in cancer diagnosis and therapy.

Publications:

  1. Diagnostic performance of mammography and ultrasound in breast cancer: A systematic review and meta-analysis
    Journal of Ultrasound, 2023

  2. Dosimetric characteristics of the INTRABEAM® system with spherical applicators in the presence of air gaps and tissue heterogeneities
    Radiation and Environmental Biophysics, 2020

  3. Techniques for generating attenuation map using cardiac SPECT emission data only: A systematic review
    Annals of Nuclear Medicine, 2019

  4. Molecular imaging approaches in the diagnosis of breast cancer: A systematic review and meta-analysis
    Iranian Journal of Nuclear Medicine, 2020

  5. Comparison of organs at risk doses between deep inspiration breath-hold and free-breathing techniques during radiotherapy of left-sided breast cancer: A meta-analysis
    Polish Journal of Medical Physics and Engineering, 2022

  6. Gold-nanoparticle-enriched breast tissue in breast cancer treatment using the INTRABEAM® system: A Monte Carlo study
    Radiation and Environmental Biophysics, 2022

  7. Dosimetric effect of nanoparticles in the breast cancer treatment using INTRABEAM® system with spherical applicators in the presence of tissue heterogeneities: A Monte Carlo study
    Biomedical Physics & Engineering Express, 2021

  8. A comparison between EGSnrc/Epp and MCNP in simulation of dosimetric parameters for radiotherapy applications
    Journal of Biomedical Physics & Engineering, 2021

  9. Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries
    Scientific Reports, 2025

  10. Attenuation correction for dedicated cardiac SPECT imaging without using transmission data
    Molecular Imaging and Radionuclide Therapy, 2023

  11. Comparative study between EPID and CBCT for radiation treatment verifications
    Iranian Journal of Medical Physics, 2018