Assoc Prof Dr. mingguang shi | bioinformatics | Best Researcher Award | 2833

Assoc Prof Dr. mingguang shi – bioinformatics-Best Researcher Award

Assoc Prof Dr. mingguang shi, hefei university of technology, China

πŸ”— Professional Profiles

Dr. Mingguang Shi 🌟

🏫 Position: Associate Professor
🏒 Affiliation: Hefei University of Technology, China
πŸ”¬ Research Focus: Bioinformatics, Machine Learning, Deep Learning

πŸ“š Education and Career:

Dr. Shi earned his Doctor of Philosophy degree in [Field Name] from [University Name]. He then joined Hefei University of Technology, where he currently holds the position of Associate Professor.

πŸ” Research Interests:

His research interests primarily revolve around bioinformatics, with a specific focus on prognostic outcome prediction using semi-supervised least squares classification. Dr. Shi’s expertise also extends to the application of machine learning and deep learning algorithms in bioinformatics, where he has demonstrated innovative approaches and garnered recognition in the scientific community.

πŸ“‘ Publications:

Dr. Shi has authored numerous research papers, serving as both the first author and corresponding author. His publications cover a wide range of topics in bioinformatics, showcasing his expertise and dedication to advancing scientific knowledge in the field.

🌐 Impact and Recognition:

His work has had a significant impact on the bioinformatics community, contributing valuable insights and methodologies. Dr. Shi’s contributions are highly regarded, and he continues to be a driving force in the intersection of bioinformatics and computational methods.

Dr. Mingguang Shi’s commitment to excellence in research and his passion for leveraging advanced technologies in bioinformatics make him a valuable asset to Hefei University of Technology and the scientific community at large.

Publication Top Noted

Paper Title : Prognostic outcome prediction by semi-supervised least squares classification.

    • Authors: Mingguang Shi
    • Journal: Briefings in Bioinformatics
    • Volume: 22
    • Issue: 4
    • Pages: 2264-2273
    • Year: 2021

Paper Title : Development and validation of GMI signature based random survival forest prognosis model to predict clinical outcome in acute myeloid leukemia.

    • Authors: Mingguang Shi
    • Journal: BMC Medical Genomics
    • Volume: 12
    • Issue: 1
    • Pages: 102
    • Year: 2019