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Dataset results
117 results for “network inference”
Data from: Using relatedness networks to infer contemporary dispersal: application to the endangered mammal Galemys pyrenaicus
Information about the degree of contemporary dispersal is important when trying to understand how populations interchange individuals and identify the specific barriers that prevent these movements. In the case of endangered species, this can represent crucial information when designing appropriate strategies that favor natural genetic exchange between populations. Here we analyze the parentage relationships between individuals from different localities and use these data to infer dispersal occurred in recent generations. We applied this approach to the Pyrenean desman (Galemys pyrenaicus), a semiaquatic and endangered species endemic to the Iberian Peninsula. We studied this species in four primary rivers in the Iberian Range, where two ancient mitochondrial lineages are separated by a strict contact zone but whose populations are more homogeneous at the genome level, suggesting the existence of complex dispersal patterns. Using next generation sequencing, we obtained 912 SNPs from each sample and estimated relatedness values between them. While relatedness networks were very dense within each river, we found surprisingly few relationships between individuals from different rivers despite their close proximity in some cases, indicating that dispersal between rivers is extremely low compared to dispersal within a single river. In agreement with this, the degree of inbreeding was exceedingly high in most individuals. These data show that relatedness information can be crucial to understand the contemporary dispersal patterns and conservation status of specific populations of endangered species.
Supplementary material 1 from: Barton DN (2023) Value 'generalisation' in ecosystem accounting - using Bayesian networks to infer the asset value of regulating services for urban trees in Oslo. One Ecosystem 8: e85021. https://doi.org/10.3897/oneeco.8.e85021
Bayesian Belief Network
Using the IBM analog in-memory hardware acceleration kit for neural network training and inference - Supplementary Material
<p>Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this Tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. Finally, we show examples of how users can expand and customize AIHWKit for their own needs. This Tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from <a href="https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial" target="_blank" rel="noopener">https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial</a>.</p> <p> </p>
Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)
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Data from: Using relatedness networks to infer contemporary dispersal: application to the endangered mammal Galemys pyrenaicus
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Unexpected high accuracy of landscape genetics inference with convolutional neural networks
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Data from: Bayesian inference of reticulate phylogenies under the multispecies network coalescent
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Data from: Statistical inference of allopolyploid species networks in the presence of incomplete lineage sorting
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Data from: Maximum parsimony inference of phylogenetic networks in the presence of polyploid complexes
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Data from: Spatial familial networks to infer demographic structure of wild populations
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Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
GEO Series GSE208620. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
LASSIM -a network inference toolbox for genome-wide mechanistic modeling [USF2, KLF6/COPEB, CBL and HIS1 siRNA]]
GEO Series GSE95508. Homo sapiens. 31 samples. Type: Expression profiling by array.
Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
GEO Series GSE125958. Homo sapiens. 192 samples. Type: Expression profiling by RT-PCR.
Network inference tools for the human transcriptome
GEO Series GSE27871. Homo sapiens. 424 samples. Type: Expression profiling by array.
Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells [RNA-seq]
GEO Series GSE113720. Mus musculus. 99 samples. Type: Expression profiling by high throughput sequencing.
LASSIM -a network inference toolbox for genome-wide mechanistic modeling [Time-series polarization: CD4+ T cells toward Th2]
GEO Series GSE60681. Homo sapiens. 15 samples. Type: Expression profiling by array.
LASSIM -a network inference toolbox for genome-wide mechanistic modeling [MAF, MYB & GATA3 siRNA]
GEO Series GSE65880. Homo sapiens. 24 samples. Type: Expression profiling by array.
Network Inference of Transcriptional Regulation in Germinating Low Phytic Acid Soybean Seeds
GEO Series GSE172018. Glycine max. 72 samples. Type: Expression profiling by high throughput sequencing.
Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses
GEO Series GSE144657. Saccharomyces cerevisiae. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Inferring gene networks for strains of Dehalococcoides highlights conserved relationships between genes encoding core catabolic and cell-wall structural proteins
GEO Series GSE42136. Dehalococcoides mccartyi. 36 samples. Type: Expression profiling by array.
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.