RT Journal Article SR Electronic T1 Deep Learning Behavioral Phenotyping System in the Diagnosis of Parkinson’s Disease with Drosophila melanogaster JF bioRxiv FD Cold Spring Harbor Laboratory SP 2024.02.23.581846 DO 10.1101/2024.02.23.581846 A1 Dong, Keyi A1 Burch, April A1 Huang, Kang YR 2024 UL http://biorxiv.org/content/early/2024/02/24/2024.02.23.581846.abstract AB Drosophila Melanogaster is widely used as animal models for Parkinson’s disease (PD) research. Because of the complexity of MoCap and quantitative assessment among Drosophila Melanogaster, however, there is a technical issue that identify PD symptoms within drosophila based on objective spontaneous behavioral characteristics. Here, we developed a deep learning framework generated from kinematic features of body posture and motion between wildtype and SNCAE46K mutant drosophila genetically modeled □-Syn, supporting clustering and classification of PD individuals. We record locomotor activity in a 3D-printed trap, and utilize the pre-analysis pose estimation software DeepLabCut (DLC) to calculate and generate numerical data representing the motion speed, tremor frequency, and limb motion of Drosophila Melanogaster. By plugging these data as the input, the diagnosis result (1/0) representing PD or WT as the output. Our result provides a toolbox which would be valuable in the investigation of PD progressing and pharmacotherapeutic drug development.Competing Interest StatementThe authors have declared no competing interest.