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ShareScore release 0.9.0
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7 results for “Network growth model”
A mathematical model to predict network growth in physarum polycephalum as a function of extracellular matrix viscosity, measured by a novel viscometer
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An ectopic network of transcription factors regulated by Hippo signaling drives growth and invasion of a malignant tumor model [larval wild type discs]
GEO Series GSE71353. Drosophila melanogaster. 18 samples. Type: Expression profiling by high throughput sequencing.
An ectopic network of transcription factors regulated by Hippo signaling drives growth and invasion of a malignant tumor model [larval imaginal discs]
GEO Series GSE71350. Drosophila melanogaster. 8 samples. Type: Expression profiling by high throughput sequencing.
An ectopic network of transcription factors regulated by Hippo signaling drives growth and invasion of a malignant tumor model [larval imaginal discs (eye, wing and leg)]
GEO Series GSE81272. Drosophila melanogaster. 27 samples. Type: Expression profiling by high throughput sequencing.
Proneurogenic ligands defined by modeling developing cortex growth factor communication networks
GEO Series GSE84482. Mus musculus. 9 samples. Type: Expression profiling by array.
An ectopic network of transcription factors regulated by Hippo signaling drives growth and invasion of a malignant tumor model
GEO Series GSE71354. Drosophila melanogaster. 53 samples. Type: Expression profiling by high throughput sequencing.
Macroscopic Models of Clique Tree Growth for Bayesian Networks
In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to characterizing clique tree growth as a function of increasing Bayesian network connectedness, specifically: (i) the expected number of moral edges in their moral graphs or (ii) the ratio of the number of non-root nodes to the number of root nodes. In experiments, we systematically increase the connectivity of bipartite Bayesian networks, and find that clique tree size growth is well-approximated by Gompertz growth curves. This research improves the understanding of the scaling behavior of clique tree clustering, provides a foundation for benchmarking and developing improved BN inference algorithms, and presents an aid for analytical trade-off studies of tree clustering using growth curves. **Reference:** O. J. Mengshoel, "Macroscopic Models of Clique Tree Growth for Bayesian Networks." In Proc. of the 22nd National Conference on Artificial Intelligence (AAAI-07). July 2007, Vancouver, Canada, pp. 1256-1262. **BibTex Reference:** @inproceedings{mengshoel07macroscopic, author = "Mengshoel, O. J.", title = "Macroscopic Models of Clique Tree Growth for {Bayesian} Networks", year = "2007", booktitle = {Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI-07)}, pages = "1256-1262", address = "Vancouver, British Columbia" }
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.