![]() In addition to working directly with experimental collaborators and rapidly sharing new research findings through preprint servers, has joined other researchers in committing to rapidly share all COVID-19 research data, and has joined forces with AWS and the Molecular Sciences Software Institute (MolSSI) to share datasets of unprecedented side through the AWS Open Data Registry, indexing these massive datasets via the MolSSI COVID-19 Molecular Structure and Therapeutics Hub. More information about COVID-19 research activities at the COVID-19 page. In the process, it created the world's first exascale distributed computing resource, enabling it to generate valuable scientific datasets of unprecedented size. During the COVID-19 epidemic, focused its resources on understanding the vulnerabilities in SARS-CoV-2, the virus that causes COVID-19 disease, and working closely with a number of experimental collaborators to accelerate progress toward effective therapies for treating COVID-19 and ending the pandemic. Run by the Consortium, a worldwide network of research laboratories focusing on a variety of different diseases, seeks to address problems in human health on a scale that is infeasible by another other means, sharing the results of these large-scale studies with the research community through peer-reviewed publications and publicly shared datasets. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcomeīy Jianfang Liu, Tara Lichtenberg, et al.Īlchemical free energy calculations biomolecular modeling coronavirus COVID-19 foldingathome health life sciences molecular dynamics protein SARS-CoV-2 simulations structural is a massively distributed computing project that uses biomolecular simulations to investigate the molecular origins of disease and accelerate the discovery of new therapies.Oncogenic Signaling Pathways in The Cancer Genome Atlas by Francisco Sanchez-Vega, Marco Mina, et al.The Immune Landscape of Cancer by Vésteinn Thorsson, David L.Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients by André Kahles, Kjong-Van Lehmann, et al.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 Li Q, Song JL, Li SH, Westover MB, Zhang R.Ĭancer genomic life sciences STRIDES whole genome sequencing Effects of cholinergic neuromodulation on thalamocortical rhythms during NREM sleep: a model study.by Paixao L, Sikka P, Sun H, Jain A, Hogan J, Thomas RJ, et al. Excess Brain Age Reflected in the Electroencephalogram of Sleep Predicts Reduced Life Expectancy.by Brink-Kjaer A, Leary EB, Sun H, Westover MB, Stone KL, Peppard PE, et al. Age estimation from sleep studies using deep learning predicts life expectancy.by Sun H, Ye E, Paixao L, Ganglberger W, Chu CJ, Zhang C, et al. The sleep and wake electroencephalogram over the lifespan.PMID: 36448766.* by Ye E*, Sun H*, Krishnamurthy PV, Adra N, Ganglberger W, Thomas RJ, et al. Dementia Detection from Brain Activity During Sleep.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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