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281
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Dataset results
281 results for “network integrity”
Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19
<p>The biological determinants underlying the range of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multivariate model classifying COVID-19 severity (multi-class AUC<sub>training</sub> = 0.799, p-value = 4.2e-6; multi-class AUC<sub>validation</sub> = 0.773, p-value = 7.7e-6). Examination of informative model features reveals novel biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.</p>
CancerEpiSys – Integrative analysis of epigenetic networks that determine the chronic lymphocytic leukemia disease state
<p>A public repository of data processed for Mallm, Iskar, Ishaque et. al. 2018 Linking aberrant chromatin features in chronic lymphocytic leukemia to deregulated transcription factor networks</p> <p><strong>Website:</strong> http://www.cancerepisys.org/cancerepisys/index.html</p> <p><strong>Github:</strong> https://github.com/CancerEpiSys/Mallm-et-al-processing-scripts</p> <p><strong>Research mission</strong></p> <p><em>CancerEpiSys</em> is a BMBF funded research project with the <a href="http://www.bmbf.de/foerderungen/15063.php">CancerSys program</a> that dissects the epigenetic networks associated with chronic lymphocytic leukemia (CLL) to develop novel diagnostic and therapeutic approaches for the disease. It has been initiated based on the emerging view that signals encoded in the DNA sequence, epigenetic modifications (e.g. DNA methylation, post-translational histone modifications), non-coding RNAs and nucleosome positioning are not independently regulated properties. Rather, these chromatin features are governed by an interconnected network of molecular processes that determine the cellular gene expression program. Any errors that occur in the interplay of these factors can lead to aberrant gene regulation associated with cancer. To rationalize the mode of action of novel ‘epigenetic’ drugs in cancer therapy that change properties of this network, we will dissect experimentally and mathematically the interdependence of these processes, focusing on CLL.</p> <p>The main objectives of <em>CancerEpiSys</em> are: (i) deciphering the relation of DNA sequence, epigenetic modifications, nucleosome positioning and aberrant gene expression in CLL, (ii) the identification of epigenetic network morphologies that describe the response to the epigenetic drugs panobinostat and 3-deazaneplanocin (DZNep) that inhibit histone deacetylases and methyltransferases, (iii) the characterization of epigenetic markers and chromatin features of CLL patient subgroups, and (iv) the integration of results into an analysis scheme for the epigenetic aberrations most relevant for prognostication, prediction of treatment relapse and stratification of patients with respect to therapeutic options. Moreover, our results will inform novel therapeutic approaches that target gene-expression programs at the epigenetic level to sensitize cancer cells towards apoptosis and anti-growth signals.</p>
OTU table: Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA)
<p>This dataset contains the OTU table and associated metadata from the ENIGMA study which was used in "A Practical Guide to Methods Controlling False Discoveries in Computational Biology" (Korthauer, K. and Kimes, P., et al. 2018; associated github: https://github.com/pkimes/benchmark-fdr/).</p> <p>The original raw data is available on MG-RAST (project mgp8190): https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp8190</p> <p>These data were processed as described in Korthauer & Kimes et al, 2018. The OTU table was provided by Renmao Tian and generated with the following pipeline: <a href="http://zhoulab5.rccc.ou.edu/pipelines/ASAP_web/pipeline_asap.php">http://zhoulab5.rccc.ou.edu/pipelines/ASAP_web/pipeline_asap.php</a>. </p> <p>More information about the ENIGMA project can be found at http://enigma.lbl.gov/</p>
Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks
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A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""
<p>Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em></p> <p>1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a></p> <p>2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared)</p> <p>3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> </a>on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.</p> <p> </p>
Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
<p class="MsoNormal"><span>Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies.</span></p>
Data from: Integrating variation in bacterial-fungal co-occurrence network with soil carbon dynamics
<p>Bacteria and fungi are core microorganisms in diverse ecosystems, and their cross-kingdom interactions are considered key determinants of microbiome structure and ecosystem functioning. However, how bacterial-fungal interactions mediate soil organic carbon (SOC) dynamics remains largely unexplored in the context of artificial forest ecosystems. Here, we characterized soil bacterial and fungal communities in four successive planting of Eucalyptus and compared them to a neighboring evergreen broadleaf forest. Carbon (C) mineralization combined with five C-degrading enzymatic activities was investigated to determine the effects of successive planting of Eucalyptus on SOC dynamics. Our results indicated that successive planting of Eucalyptus significantly altered the diversity and structure of soil bacterial and fungal communities and increased the negative bacterial-fungal associations. The bacterial diversity significantly decreased in all Eucalyptus plantations compared to the evergreen forest, while the fungal diversity showed the opposite trend. The ratio of negative bacterial-fungal associations increased with successive planting of Eucalyptus due to the decrease in SOC, ammonia nitrogen (NH4+−N), nitrate nitrogen (NO3−−N), and available phosphorus (AP). Structural equation modeling indicated that the potential cross-kingdom competition, based on the ratio of negative bacterial-fungal correlations, was significantly negatively associated with the diversity of total bacteria and keystone bacteria, thereby increasing C-degrading enzymatic activities and C mineralization.</p> <p>Synthesis and applications: Our results highlight the regulatory role of the negative bacterial-fungal association in enhancing the correlation between bacterial diversity and C mineralization. This suggests that promoting short-term successive planting in the management of Eucalyptus plantations can mitigate the impact of this association on SOC decomposition. Taken together, our study advances the understanding of bacterial-fungal negative associations to mediate carbon mineralization in Eucalyptus plantations, giving us a new insight into SOC cycling dynamics in artificial forests.</p>
Canadian Biomarker Integration Network for Depression (CAN-BIND) - Validation Study
ClinicalTrials.gov study NCT04162522. IPD Sharing: YES. Countries: 1. Publications: 3.
Veterans Integrated Service Network (VISN) 23 Lung Disease Self Management/Case Management Program
ClinicalTrials.gov study NCT00126776. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Maintaining Independence and Sobriety Through Systems Integration, Outreach, and Networking
ClinicalTrials.gov study NCT02942979. IPD Sharing: NO. Countries: 1. Publications: 3.
Canadian Biomarker Integration Network for Depression Study
ClinicalTrials.gov study NCT01655706. IPD Sharing: YES. Countries: 1. Publications: 18.
COVID-CDR: An integrative resource for network-based investigation of COVID-19 combinatorial drug repositioning and mechanism of action
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Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19
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Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events
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Data for: Integration of an invasive plant in hummingbird and flower mite networks is driven by ecological fitting and generalization
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Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
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Integrating nocturnal and diurnal interactions in a Neotropical pollination network
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Data from: Integrating variation in bacterial-fungal co-occurrence network with soil carbon dynamics
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Data cleaning and enrichment through data integration: networking the Italian academia
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.