Bioinformatics Research

KEY FINDING: The machine learning method showed high accuracy in detecting the type of wheelchair propulsion, with an F1 score of 0.886 when using both frame and wheel sensors.

KEY FINDING: SVM_linear algorithm showed superior predictive ability (AUC = 0.904, accuracy = 0.944).

KEY FINDING: Serpina3n protein expression significantly increased in the injured spinal cord segment after SCI, with higher levels in severe SCI cases.

KEY FINDING: A total of 129 autophagy-related DEGs were identified, including 126 up-regulated and 3 down-regulated genes.

KEY FINDING: The developed neural network model exhibits an average time error of less than 11.51 ms across all walking conditions, indicating its suitability for real-time applications.

KEY FINDING: Novel nanomaterial-based sensor designs improve skin conformality, reduce noise, and enhance breathability.

KEY FINDING: The recurrent neural network (RNN) model significantly outperformed linear regression, Ridge, and Lasso methods in predicting gait recovery after SCI.

KEY FINDING: Identification of 54 differentially expressed microRNAs and 1656 differentially expressed genes in SCI patients compared to healthy controls.

KEY FINDING: The United States leads in the number of published articles on AI research in spinal cord neural injury and restoration.

KEY FINDING: SCIseg, an open-source automatic method, was trained and evaluated on a dataset of 191 spinal cord injury patients from three sites for segmenting spinal cord and T2-weighted lesions.