ND-AMD: A Web-Based Database for Animal Models of Neurological Disease With Analysis Tools.

BACKGROUND: Research on animal models of neurological diseases has primarily focused on understanding pathogenic mechanisms, advacing diagnostic strateggies, developing pharmacotherapies, and exploring preventive interventions. To facilitate comprehensive and systematic studies in this filed, we have developed the Neurological Disease Animal Model Database (ND-AMD), accessible at https://www.uc-med.net/NDAMD. This database is signed around the central theme of "Big Data - Neurological Diseases - Animal Models - Mechanism Research," integrating large-scale, multi-dimensional, and multi-scale data to facilitate in-depth analyses. ND-AMD serves as a resource for panoramic studies, enabling comparative and mechanistic research across diverse experimental conditions, species, and disease models.

METHOD: Data were systematically retrieved from PubMed, Web of Science, and other relevant databases using Boolean search strategies with standardized MeSH terms and keywords. The collected data were curated and integrated into a structured SQL-based framework, ensuring consistency through automated validation checks and manual verification. Heterogeneity and sensitivity analyses were conducted using Cochran's Q test and the I2 statistic to assess variability across studies. Statistical workflows were implemented in Python (SciPy, Pandas, NumPy) to support multi-scale data integration, trend analysis, and model validation. Additionally, a text co-occurrence network analysis was performed using Natural Language Processing (TF-IDF and word embeddings) to identify key conceptual linkages and semantic structures across studies.

RESULTS: ND-AMD integrates data from 483 animal models of neurological diseases, covering eight disease categories, 21 specific diseases, 13 species, and 152 strains. The database provides a comprehensive repository of experimental and phenotypic data, covering behavioral, physiological, biochemical, molecular pathology, immunological, and imaging characteristics. Additionally, it incorporates application-oriented data, such as drug evaluation outcomes. To enhance data accessibility and facilitate in-depth analysis, ND-AMD features three custom-developed online tools: Model Frequency Analysis, Comparative Phenotypic Analysis, and Bibliometric Analysis, enabling systematic comparison and trend identification across models and experimental conditions.

CONCLUSIONS: The centralized feature of ND-AMD enables comparative analysis across different animal models, strains, and experimental conditions. It helps capture intricate interactions between biological systems at different levels, ranging from molecular mechanisms to cellular processes, neural networks, and behavioral outcomes. These models play a vital role as tools in replicating pathological conditions of neurological diseases. By offering users convenient, efficient, and intuitive access to data, ND-AMD enables researchers to identify patterns, trends, and potential therapeutic targets that may not be apparent in individual studies.