Linli Zheng
Biographic Data
| ID | 7792706 |
|---|---|
| NAME | Linli Zheng |
| GIVEN NAMES | Linli |
| FAMILY NAME | Zheng |
| SIGNATURE | ZHENG L |
| AFFILIATIONS | Sichuan University |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Support vector machine-based classification of bulimia nervosa using diffusion tensor imaging
This study demonstrated that DTI-based machine learning (ML) approaches could effectively differentiate individuals with bulimia nervosa (BN) from healthy controls (HCs), thereby providing insights into potential neurobiological markers associated with BN
Machine learning research based on diffusion tensor images to distinguish between anorexia nervosa and bulimia nervosa
Machine learning based on DTI could effectively distinguish between AN and BN, with MTG_L and STG_L potentially serving as neuroimaging biomarkers
Metabolic syndrome increases osteoarthritis risk: Findings from the UK Biobank prospective cohort study
MetS and its components have been found to be associated with an increased risk of OA, particularly in individuals with elevated levels of CRP. These findings highlight the significance of managing MetS as a preventive and intervention measure for OA
No prominent works on this page.
Machine learning research based on diffusion tensor images to distinguish between anorexia nervosa and bulimia nervosa
Machine learning based on DTI could effectively distinguish between AN and BN, with MTG_L and STG_L potentially serving as neuroimaging biomarkers
Metabolic syndrome increases osteoarthritis risk: Findings from the UK Biobank prospective cohort study
MetS and its components have been found to be associated with an increased risk of OA, particularly in individuals with elevated levels of CRP. These findings highlight the significance of managing MetS as a preventive and intervention measure for OA
Support vector machine-based classification of bulimia nervosa using diffusion tensor imaging
This study demonstrated that DTI-based machine learning (ML) approaches could effectively differentiate individuals with bulimia nervosa (BN) from healthy controls (HCs), thereby providing insights into potential neurobiological markers associated with BN
Advanced Neuroimaging Techniques and Applications (2 works) · Bulimia nervosa (2 works) · Diffusion MRI (2 works) · Eating Disorders and Behaviors (2 works) · Internal Medicine (2 works) · Medicine (2 works) · Anorexia (1 works) · Anorexia nervosa (1 works) · Artificial Intelligence (1 works) · Biobank (1 works)