Dr. Yulian Ding | Molecular Basis of Genetic Disease | Best Researcher Award

Shenzhen Institute of Advanced Technology | China

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🧬 YULIAN DING, PH.D. – PIONEER IN BIOMEDICAL ENGINEERING AND MACHINE LEARNING

EARLY ACADEMIC PURSUITS🎓

Yulian Ding embarked on her academic journey with a Bachelor of Science in Computer Science from Luoyang Normal University (2010-2014). Her passion for data science and bioinformatics was further solidified during her Master's degree in Computer Science at Shaanxi Normal University (2014-2017), where she focused on dynamic protein complex identification and essential protein identification through swarm intelligence optimization. Her research, under the guidance of Dr. Xiujuan Lei, shaped her understanding of computational biology and bioinformatics. In 2018, Yulian took her academic prowess to the next level by pursuing a Ph.D. in Biomedical Engineering at the University of Saskatchewan (2018-2022). Under the supervision of Dr. Fang-Xiang Wu, she developed advanced machine learning models to predict biomolecule-disease associations, working with miRNAs, lncRNAs, and circRNA.

PROFESSIONAL ENDEAVORS🧑‍🔬

Upon completing her Ph.D., Yulian Ding transitioned into various research roles that leveraged her expertise in both machine learning and biomedical engineering. From March 2022 to February 2023, she served as an Assistant Researcher at the Center for High Performance Computing, Shenzhen Institute of Advanced Technology, under the prestigious Chinese Academy of Sciences. Her work there focused on drug design and disease biomarker identification using machine learning techniques, contributing to major advancements in protein-ligand interaction prediction. Concurrently, Yulian completed a Postdoctoral Fellowship at the University of Saskatchewan’s College of Medicine and Division of Biomedical Engineering. She specialized in bio-data analytics, working on clinical data, particularly in identifying cirRNA biomarkers for cancer.

CONTRIBUTIONS AND RESEARCH FOCUS ON MOLECULAR BASIS OF GENETIC DISEASE🔬

Yulian Ding’s research focuses on biomedical informatics, particularly at the intersection of machine learning and biomolecular interaction. Her key contributions lie in:

• Developing novel machine learning models for predicting disease-associated biomolecules, including miRNAs, lncRNAs, and circRNA.
• Drug design and disease biomarker discovery, particularly through the integration of machine learning in protein-ligand interactions.
• Enhancing the understanding of bio-data analytics in oncology, utilizing vast datasets to identify crucial molecular targets for treatment.

IMPACT AND INFLUENCE🌍

Ding's work has had significant implications in precision medicine, especially in the early detection of diseases through biomarker identification. Her research on biomolecule-disease associations and the use of machine learning for drug discovery has positioned her as a rising leader in the biomedical engineering field. Her collaboration with leading scientists and institutions, including her postdoctoral work, has influenced both academic research and practical applications in biotechnology.

ACADEMIC CITES📚

Yulian Ding has been recognized for her academic excellence through numerous prestigious awards:

• Dr. Victor A. Pollak and Mirka B. Pollak Scholarship (2021, University of Saskatchewan)
• Russell (Russ) William Haid Memorial Award (2020, University of Saskatchewan)
• China’s National Scholarship for Graduate Students (2017)
• China’s National Second Prize of Lanqiao Cup Software Design Competition (2014)
• ACM Programming Competition Silver Medal (2014)

Her work has been published in leading academic journals and recognized by international peers in the fields of bioinformatics and computational biology. As a journal reviewer for notable journals like Neurocomputing, Knowledge-Based Systems, and PLOS Computational Biology, she has contributed to shaping future research trends.

LEGACY AND FUTURE CONTRIBUTIONS🔮

Yulian Ding's legacy is deeply intertwined with her pioneering work in biomedical engineering. By harnessing the power of machine learning, she has opened new avenues for disease diagnosis and drug development. Her future endeavors are poised to push the boundaries of biomedical innovation, with a continued focus on applying computational intelligence to solve biomedical challenges. Through ongoing research and collaboration, Yulian will undoubtedly remain at the forefront of biomedical science, contributing to innovations that bridge the gap between computation and medicine.

 FINAL THOUGHTS🏅

Yulian Ding's academic journey, professional achievements, and innovative research establish her as a significant figure in biomedical engineering. Her work continues to make impactful contributions to precision medicine and biomedical informatics, shaping the future of healthcare through cutting-edge machine learning applications.

NOTABLE PUBLICATIONS📑

"MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations" 

• Authors: Liu, Y. , Zhang, F. , Ding, Y. , Li, J. , Wu, F.-X.
• Journal: Cellular and Molecular Medicine
• Year: 2024

"P4PC: A Portal for Bioinformatics Resources of piRNAs and circRNAs" 

• Authors: Liu, Y., Li, R., Ding, Y., Hei, X., Wu, F.-X.
• Journal: Bioinformatics
• Year: 2024

"Negative sample selection for miRNA-disease association prediction models" 

• Authors: Ding, Y. , Wang, F. , Zhang, Y. , Wu, F.-X.
• Journal: Machine Learning Methods for Multi-Omics Data Integration
• Year: 2023

"Biomarker Identification via a Factorization Machine-Based Neural Network With Binary Pairwise Encoding" 

• Authors: Ding, Y. , Lei, X. , Liao, B. , Wu, F.-X
• Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
• Year: 2023

"MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations" 

• Authors: Ding, Y. , Lei, X. , Liao, B. , Wu, F.-X
• Journal: Briefings in Bioinformatics
• Year: 2022