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Dataset results
56 results for “Network biology”
Data for: Thermal vulnerability in a mountain stream network: Temporal, spatial, and biological data
Open the record for dataset details and reuse information.
Data from: Identification of migratory bird flyways in North America using community detection on biological networks
Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 years. However, previous flyway characterizations are not easily updated with current bird movement data and fail to provide assessments of the importance of specific geographical regions to the identification of flyways. Here, we developed a network model of migratory movement for four waterfowl species —mallard (Anas platyrhnchos), northern pintail (A. acuta), American green-winged teal (A. carolinensis), and Canada goose (Branta canadensis) — in North America using bird band and recovery data. We then identified migratory flyways using a community detection algorithm and characterize the importance of smaller geographic regions in identifying flyways using a novel metric, the consolidation factor. We identified four main flyways for mallards, northern pintails, and American green-winged teal with the flyway identification in Canada geese exhibiting higher complexity. For mallards, flyways were relatively consistent through time. However, consolidation factors revealed that for mallards and green-winged teal the presumptive Mississippi flyway was potentially a zone of high mixing between other flyways. Our results demonstrate that the network approach provides a robust method for flyway identification that is widely applicable given the relatively minimal data requirements and is easily updated with future movement data to reflect changes in flyway definitions and management goals.
Data used in "Biologically informed deep neural network for prostate cancer discovery" publication
<p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p>
Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus
<p>Dataset accompanying the manuscript "Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus". This dataset is used to re-create all figure panels with underlying data in the manuscript.</p>
Data for "Convergent temperature representations in artificial and biological neural networks"
<p>Data for "Convergent Temperature Representations in Artificial and Biological<br> neural networks" by Haesemeyer M, Schier AF and Engert F, 2019</p> <p>The corresponding python code is available at:<br> <a href="https://github.com/haesemeyer/GradientPrediction">https://github.com/haesemeyer/GradientPrediction</a></p> <p>All zip files should be extracted in the same folder as the python files. This<br> will create a sub-folder structure for the model data.<br> ZIP File Contents (Note: These are used by the code and not necessarily useful<br> by themselves):<br> model_data.zip<br> Contains tensorflow checkpoints on all naive and fully trained models, test<br> errors during training as well as evolution weights where applicable.<br> model_cluster_assignments.zip<br> For the trained models in model_data.zip the response cluster assignment<br> for each individual unit.<br> zebrafish_data.zip<br> The zebrafish brain and behavior data used in the paper comparisons. This<br> archive also contains the temperature stimulus file stimFile.hdf5<br> training_data.zip<br> The generated training data used during predictive network training<br> test_data.zip<br> The generated test data used to evaluate predictive network training</p>
(raw dataset) "Spatial biology of Ising-like synthetic genetic networks"
<p>This is the raw data for the manuscript Simpson et al "Spatial biology of Ising-like synthetic genetic<br> networks"</p>
Clinical and Biological Digestive Peritoneal Carcinomatosis Data Base From the French National Network of Peritoneal Surface Malignancies
ClinicalTrials.gov study NCT02823860. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Identification of migratory bird flyways in North America using community detection on biological networks
Open the record for dataset details and reuse information.
Data from: Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation
<p>Simulation data from Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation, doi: 10.3389/fsysb.2024.1291293</p> <p>There are 10 csv files per network type, each dataset contains the Timestep, Node, Strain, Inducer Concentration/Duration, and Node weights for the timecourse of the simulation. </p>
Implementation of cell-free biological networks at steady state
<p>Living cells maintain a steady state of biochemical reaction rates by exchanging energy and matter with the environment. These exchanges usually do not occur in in vitro systems, which consequently go to chemical equilibrium. This in turn has severely constrained the complexity of biological networks that can be implemented in vitro. We developed nanoliter-scale microfluidic reactors that exchange reagents at dilution rates matching those of dividing bacteria. In these reactors we achieved transcription and translation at steady state for 30 h and implemented diverse regulatory mechanisms on the transcriptional, translational, and posttranslational levels, including RNA polymerases, transcriptional repression, translational activation, and proteolysis. We constructed and implemented an in vitro genetic oscillator and mapped its phase diagram showing that steady-state conditions were necessary to produce oscillations. This reactor-based approach will allow testing of whether fundamental limits exist to in vitro network complexity.</p>
Multi_Level_Biological_Networks
<p>In our work, we collected biological entities and their relations, and put them into these networks.</p> <p>The below description is in our manuscript.</p> <p>As soon as the manuscript is published, we will present the access information in this page.</p> <p>---</p> <p>The molecule type consisted of genes, and the function type consisted of GO terms. The phenotypic type consisted of diseases that are MeSH terms in disease categories, and the concepts in some UMLS concept types: <em>Congenital Abnormality, Acquired Abnormality, Finding, Pathologic Function, Disease or Syndrome, Mental or Behavioral Dysfunction, Cell or Molecular Dysfunction, Sign or Symptom, Anatomical Abnormality, </em>and<em> Neoplastic Process.</em> Those UMLS concept types contained a considerable number of the MeSH terms in disease categories.</p> <p>18 types of biological relations were defined with their pre-defined relation types and their original resources. The 18 types are presented in the Supplementary Material (Supplementary Table S2).</p> <p>From the CODA, 13 types of relations for this study were defined by combining the six original resources and the eleven relation types that were pre-defined in the CODA. The six original resources were BioGRID<sup>41</sup>, RegNetwork, TRANSFAC<sup>42</sup>, EndoNet<sup>43</sup>, KEGG, GO, and PhenoGO<sup>44</sup>, which were released until 2016. The six pre-defined types in CODA were: <em>Undirected Link, Directed Link, Positive Increase, Positive Decrease, Negative Increase, </em>and<em> Negative Decrease</em>. Moreover, their reversal types were also made. The <em>Undirected Link</em> means a non-directional association, and the <em>Positive Decrease</em> means that the activity or the amount of a receiver decreases as the activity or the amount of an actor increases<sup>19</sup>. </p> <p>From the UMLS (version 2016AA), four types of relations were defined for the present study. They were defined only by their original resources: MedlinePlus<sup>45</sup>, MTHMST<sup>46</sup>, NCI<sup>47</sup>, and OMIM. Although the UMLS has more than 200 resources, only the four resources provided a considerable number of relations among the nodes that we considered. The pre-defined types of relations in the four resources were too many for us to properly categorize them. Therefore, we ignored them when defining the types of relations for this study.</p> <p>The MEDLINE (version 2017) provides the co-occurrence frequency of two MeSH terms. It says how many times two MeSH terms have been attached to the same biomedical literature. Only one type of relations was defined for our research. Among functions or phenotypes, if two nodes had any co-occurrence frequency, we determined there is a co-occurrence relation between them. We tried to separate them according to their frequency. However, the performance, AUROC values, in the application was best when the co-occurrence relations were not separated.</p>
PAGER: constructing PAGs and new PAG–PAG relationships for network biology
<p>Pathway and Annotated-list Gene-signature Electronic Repository (PAGER) is an online systems biology tool for constructing and visualizing gene and PAG networks from multiple PAG genesets.</p> <p>PAG stands for Pathway, Annotated-list and Gene-signature. A PAG geneset represents a set of genes that share a common feature, whether they belong within the same pathway, share common biological processes, are co-differentially expressed under a particular condition of an experiment or clinical sample, or share a common downstream target or function.</p> <p><strong>Available at:</strong> <a href="http://discovery.informatics.uab.edu/PAGER">http://discovery.informatics.uab.edu/PAGER</a></p> <p><strong>Acknowledgements:</strong></p> <ul> <li> <p>Zongliang Yue, Qi Zheng, Michael T Neylon, Minjae Yoo, Jimin Shin, Zhiying Zhao, Aik Choon Tan, Jake Y Chen, PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology, Nucleic Acids Research, Volume 46, Issue D1, 4 January 2018, Pages D668–D676,<a href="https://doi.org/10.1093/nar/gkx1040">https://doi.org/10.1093/nar/gkx1040</a></p> </li> </ul> <p> </p> <p> </p> <p> </p> <p><strong>File description</strong></p> <p>The GMT file format is a tab delimited file format that describes gene sets. In the GMT format, each row represents a PAG. The first column is PAG identifier, the second column is PAG link and the third and left columns are PAG genes. </p>
An integrative network biology analysis identifies miR-508-3p as the determinant and a prognosis biomarker of the mesenchymal subtype ovarian cancer
GEO Series GSE108863. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Omics multi-layers networks and identification of the key genes and miRNAs involved in fat storage and metabolism: systems biology approach
GEO Series GSE122224. Gallus gallus. 6 samples. Type: Expression profiling by high throughput sequencing.
Global ovary gene interaction network structure informs biological effects of Chemical stressors
GEO Series GSE70936. Pimephales promelas. 1470 samples. Type: Expression profiling by array.
Effects of Long-term Culture on the Biological Characteristics and ceRNA Network-associated RNA Profiles of Human Bone Marrow-derived Mesenchymal Stem Cells
GEO Series GSE178514. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network
<p>Data published along with paper: <em>Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically-inspired neural network</em></p> <p>In this data is shared: models (before & after training), stimuli used to train and test, and activity of the model in test.</p>
Language Acquisition in the Brain and Algorithms: Towards Systematic Monitoring of the Evolution of Semantic Representations in Biological and Artificial Neural Networks
ClinicalTrials.gov study NCT05217043. IPD Sharing: NO. Countries: 1. Publications: 0.
The Radiation Oncology-Biology Integration Network (ROBIN) Molecular Characterization Trial (MCT) of Standard Short Course Radiotherapy for Rectal Cancer
ClinicalTrials.gov study NCT05943210. IPD Sharing: NO. Countries: 1. Publications: 0.
Prospective Pilot Study of Bone Marrow and Peripheral Blood Samples From AML Patients to Characterize the Biologic Heterogeneity of the Disease Using Single Cell Network Profiling (SCNP)
ClinicalTrials.gov study NCT01203033. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.