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The Penn Integrated Neurodegenerative Disease Database (INDD) contains data from individuals with Alzheimer's disease, Parkinson's disease, frontotemporal dementia, and amyotrophic lateral sclerosis, who have been followed in research studies at the University of Pennsylvania. The database has been periodically described in publications (https://pubmed.ncbi.nlm.nih.gov/23978324/), with updates on the website. Researchers can request biosamples as well as clinical and biomarker data. Scientists work collaboratively to analyze the Integrative Neurodegenerative Disease Database (INDD) from the Center for Neurodegenerative Disease Research (CNDR) that tracks ~11,000 patients who attended one of four neurodegenerative disease centers at Penn.
The PRO-ACT platform houses the largest ALS clinical trials dataset ever created. It is a powerful tool for biomedical researchers, statisticians, clinicians, or anyone else interested in "Big Data." PRO-ACT merges data from existing public and private clinical trials, generating an invaluable resource for the design of future ALS clinical trials. The database will also contribute to the identification of unique observations, novel correlations, and patterns of ALS disease progression, as well as a variety of still unconsidered analyses. More than 600,000 people around them world are battling ALS. The disease strikes indiscriminately, and typically patients will die within 2-5 years following diagnosis. Currently, there are no effective treatments or a cure for ALS. Users of PRO-ACT are helping to accelerate the discovery, development, and delivery of ALS treatments, which will provide hope to patients and their families.
ALSoD is a freely available database that has been transformed from a single gene storage facility recording mutations in the SOD1 gene to a multigene ALS bioinformatics repository and analytical instrument combining genotype, phenotype, and geographical information with associated analysis tools. These include a comparison tool to evaluate genes side by side or jointly with user configurable features, a pathogenicity prediction tool using a combination of computational approaches to distinguish variants with nonfunctional characteristics from disease-associated mutations with more dangerous consequences, and a credibility tool to enable ALS researchers to objectively assess the evidence for gene causation in ALS. Furthermore, integration of external tools, systems for feedback, annotation by users, and two-way links to collaborators hosting complementary databases further enhance the functionality of ALSoD.