![]() ![]() The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantificati. TCGA has analyzed matched tumor and normal tissues from 11,000 patients, allowing for the comprehensive characterization of 33 cancer types and subtypes, including 10 rare cancers. The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer. by Romero K, Goparaju B, Russo K, Westover MB, Bianchi MT.Ĭancer genomic life sciences STRIDES whole genome sequencing Alternative remedies for insomnia: a proposed method for personalized therapeutic trials.by Nassi TE, Ganglberger W, Sun H, Bucklin AA, Biswal S, van Putten MJAM, et al. IEEE Transactions on Biomedical Engineering. Automated Scoring of Respiratory Events in Sleep with a Single Effort Belt and Deep Neural Networks.by Eiseman NA, Westover MB, Mietus JE, Thomas RJ, Bianchi MT Classification algorithms for predicting sleepiness and sleep apnea severity.by Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, et al. ![]() ![]() Artificial Intelligence in Sleep Medicine: An American Academy of Sleep Medicine Position Statement.by Adra N, Sun H, Ganglberger W, Ye EM, Dümmer LW, Tesh RA, et al. Optimal Spindle Detection Parameters for Predicting Cognitive Performance.This data is being used to develop CAISR (Complete AI Sleep Report), a collection of deep neural networks, rule-based algorithms, and signal processing approaches designed to provide better-than-human detection of conventional PSG. Beginning with PSG recordings from from ~15K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting. The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Bioinformatics deep learning life sciences machine learning medicine neurophysiology neuroscience ![]()
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