Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression
Bibliographic Data
| ID | 15520028 |
|---|---|
| Authors | Jing Gao (0000-0002-0020-0622, Wenzhou Medical University), Jinli Gao, Qinglang Wang (Nanjing Medical University), Jie Liu (0000-0001-7976-502X, Wenzhou Medical University), Siqian Zheng (0000-0002-8436-4367, Jiaxing University), Jiahong Liu (0000-0003-2611-6100, First Affiliated Hospital of Wenzhou Medical University), Zhiyong Gao (0000-0002-2181-7402, First Affiliated Hospital of Wenzhou Medical University), Cheng Zhu (0000-0001-7913-2572, First Affiliated Hospital of Wenzhou Medical University, corresponding author) |
| Year | 2025 |
| Volume | 16 |
| Pages | 1564095-1564095 |
| Publication date | 2025-06-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2025.1564095 |
| PMID | 40656047 |
| OpenAlex | W4411706467 |
| Language | EN |
| References cited | 42 |
The combined application of machine learning and transcriptomic analysis effectively identified core neurobiological genes in a depression model. Genes including Oprm1 and BDNF demonstrated functional relevance in modulating neural activity and behavior, offering promising candidates for early diagnosis and individualized treatment of depression
Biology · Computational biology · Depression (economics · Feature selection · Gene · Gene expression · Machine learning · Random forest · Adipose Tissue and Metabolism · Computer Science · Neuroscience · Psychology · Stress Responses and Cortisol · Tryptophan and brain disorders · Artificial Intelligence · Genetics
| Citation velocity | historical |
|---|---|
| Highly cited | No |