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Dataset results
189 results for “feature model”
pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
<p>pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.</p>
DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction
<p>This is an open-source repository for hosting codes and data files from research, "DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction". We also provided a web service based on our methods, please go to http://114.117.165.134:8501/.</p><p>(1)raw_dataset.zip: </p><ol><li>1,282 neuron reconstructions from SEU-Allen dataset;</li><li>1,002 neuron reconstructions from Janelia dataset;</li><li>1,100 neuron reconstructions from ION dataset.</li></ol><p>(2)Supplementary.zip: Supplementary information, including tables and figures;</p><p>(3)DSM-tools.zip: A python package for converting neuron morphology into sequences and implementing DSM models.</p><ol><li>NeuronSequenceDataset class: to transform SWC files to sequence dataframes by binary tree traversals, and prepare for the input of DSM networks.</li><li>DSMDataConverter class: a helper to convert the sequence dataframes for classification and clustering.</li><li>DSMHierarchicalAttentionNetwork class: classification model, giving a pre-trained DSM-HAN model by default.</li><li>DSMAutoencoder class: clustering model, giving a pre-trained DSM-AE model by default.</li></ol><p>(4)neuron2seq_for_developer.zip: A repository for further development of the models, including source codes and all data files.</p>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
scRNA-seq dataset "A novel in vitro tubular model to recapitulate features of distal airways: the bronchioid"
<p>We provide a .Rds file of an annotated Seurat Object of scRNA-seq data of two bronchioid models derived from distinct donors after 21days of culture using 10x genomics 3' v3 chemistry. Raw data was processed using CellRanger v7.1.0. Cells were filtered based on detected UMIs (>2000) and fraction of mitochondrial counts (<10%).<br>Metadata annotations contain:<br>- Patient -> patient information for every cell (patient1 or patient2)<br>- nCount_RNA -> UMI counts per cell<br>- nFeature_RNA -> genes detected per cell<br>- percent.mt -> mitochondrial count fraction per cell<br>- seurat_clusters -> unsupervised clustering results using Louvain algorithm with resolution = 0.5<br>- Manual.Annotation -> Cell types annotated based on marker gene expression<br>- Celltypist.prediction -> Cell types predicted with CellTypist Python package<br>- Celltypist.prediction.ari -> Cell types predicted with CellTypist Python package, with harmonized names for comparison with manual annotation</p>
Predictor complexity and feature selection affect Maxent model transferability: evidence from global freshwater invasive species
<p>This dataset contains the following:</p> <ol> <li>Occurrence datasets of five global freshwater invasive species (African sharptooth catfish <i>Clarias gariepinus</i>, Mozambique tilapia <i>Oreochromis mossambicus</i>, American bullfrog <i>Lithobates catesbeianus</i>, red swamp crayfish <i>Procambarus clarkii</i>, and Australian redclaw crayfish <i>Cherax quadricarinatus</i>)</li> <li>Background points for presence-only ecological niche modelling (e.g., Maxent)</li> <li>Example R script (with annotations inline) to conduct model tuning and transferability assessments using Maxent</li> </ol>
Dataset of "Hybrid modeling on 3D hydraulic features of a step-pool unit"
<p>In this repository you can find the data for the submission "Hybrid modeling on 3D hydraulic features of a step-pool unit" by Zhang et al. to Earth Surface Dynamics.</p> <p>The topographic models of the step-pool unit made of natural stones after FAVORization in FLOW3D for the six flow rates are kept in .stl files which were named after the flow rates (L/s). The mesh size for the step-pool feature is 2.5 mm for X, Y and Z directions. The locations, water level and flow velocity for the inlet boundary in all the numerical models are presented in the excel file. </p>
Modelling the genetic aetiology of complex disease: human-mouse conservation of noncoding features and disease-associated loci
<p>Understanding the genetic aetiology of loci associated with disease is crucial for developing preventative measures and effective treatments. Mouse models are used extensively to understand human pathobiology and mechanistic functions of disease-associated loci. However, the utility of mouse models is limited by evolutionary divergence in transcription regulation for pathways of interest. Here, we summarise the conservation of genomic (exonic and multi-cell regulatory) features and complex disease associated variant sites between humans and mice. Our results highlight the importance of understanding evolutionary divergence in transcription regulation when interpreting functional studies using mice as models for human disease variants.</p>
Data for Floor heating pre-on/off parameters based on Model Predictive Control feature extrapolation Paper in CLIMA2022 conference proceedings
<p>This is a collection of time series results used to obtain all the results shown in the paper. The tags of the .csv or .mat files are self explanatory and easy to use.</p>
Modelling migraine-related features in the nitroglycerin animal model: trigeminal hyperalgesia is associated with affective status and motor behaviour
<p>This dataset comprises the findings obtained in the study aimed at exploring the correlation between trigeminal hyperalgesia and affective status or behavioral components in a migraine-specific animal model based on nitroglycerin administration (see article <em>Modelling migraine-related features in the nitroglycerin animal model: trigeminal hyperalgesia is associated with affective status and motor behaviour).</em></p> <p>In vivo assessments performed in male Sprague-Dawley rats four hours after treatment with nitroglycerin (10mg/kg, i.p.) or its vehicle:</p> <p>- in the open field test were evaluated: time spent (expressed in seconds) in the center of the apparatus as a measure of anxiety; distance (expressed in meters) travelled in the apparatus as a measure of motor behaviour; number of rearings as a measure of exploratory behaviour; time spent in grooming behavior (expressed in seconds) used as nociception index;</p> <p>- evaluation of trigeminal hyperalgesia in the orofacial formalin test: the face rubbing was measured counting the seconds the animal spent grooming the injected area (upper lip, lateral to the nose) with the ipsilateral forepaw or hindpaw 0–3 min (Phase I) or 12–45 min (Phase II) after formalin injection (50 µl, s.c.). The observation time was divided into 15 blocks of 3 min each.</p> <p><strong>RESULTS</strong></p> <p>The data analysis shows an inverse correlation between trigeminal hyperalgesia and motor or exploratory behavior, and a positive association with anxiety-like behavior of spontaneous grooming.</p> <p>These findings further expand on the translational value of the migraine-specific model based on nitroglycerin administration and prompt additional parameters that can be investigated to explore the complexity of the disease.</p>
Code and Data Supplement for Using feature importance as exploratory data analysis tool on earth system models
<p>This contains:</p> <ul> <li>Code for all analyses in</li> <li>E3SM data</li> </ul> <p>For the paper Using <em>feature importance as exploratory data analysis tool on earth system models.</em></p>
Ranger models for predicting isoform abundance from UTR sequence features
<p>Each RDS file contains a ranger object, trained on transcripts after removing those associated with the held-out genes in one of the five cross-validation folds. The day and replicate number in the file name corresponds to the neuronal differentiation sample on which the model was trained. The file gene_folds.txt indicates the fold from which each gene was excluded during model training. The file transcript_gene_associations.txt contains transcript-gene associations. The file predictors.RDS contains the matrix of predictor variables.</p>
Dataset for "Who is behind the Model? Classifying Modelers based on Pragmatic Model Features"
<p>This dataset contains pragmatic features computed after each interaction with the modeling tool.</p> <p>The dataset has been collected in several modeling sessions in 2010. Student data was collected using students from the Technical University of Eindhoven. Practitioner data, in turn, was collected as part of a Dutch BPM roundtable event in Eindhoven as well as in Berlin.</p>
Summary table outlining key features of different OA business model classifications
<p>A summary table created as a quick reference for the paper published at https://doi.org/10.1629/uksg.667</p>
Data and Code from: Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients
<div> <div> <div> <p>Abstract</p> <p>Spinocerebellar ataxia type 7 (SCA7) is a genetic neurodegenerative disorder caused by a CAG- polyglutamine repeat expansion. Purkinje cells (PCs) are central to the pathology of ataxias, but their low abundance in the cerebellum underrepresents their transcriptomes in sequencing assays. To address this issue, we developed a PC enrichment protocol and sequenced individual nuclei from mice and patients with SCA7. Single-nucleus RNA sequencing in SCA7-266Q mice revealed dysregulation of cell identity genes affecting glia and PCs. Specifically, genes marking zebrin-II PC subtypes accounted for the highest proportion of DEGs in symptomatic SCA7-266Q mice. These transcriptomic changes in SCA7-266Q mice were associated with increased numbers of inhibitory synapses as quantified by immunohistochemistry and reduced spiking of PCs in acute brain slices. Dysregulation of zebrin-II cell subtypes was the predominant signal in PCs of SCA7-266Q mice and was associated with the loss of zebrin-II striping in the cerebellum at motor symptom onset. We furthermore demonstrated zebrin-II stripe degradation in additional mouse models of polyglutamine ataxia and observed decreased zebrin-II expression in cerebellum of patients with SCA7. Our results suggest that a breakdown of zebrin subtype regulation is a shared pathological feature of polyglutamine ataxias.</p> <p>Data and Code Availability</p> <p>Here you will find data and code associated with our manuscript "Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients", Bartelt et al., <em>Sci. Trans. Med. </em>16, eadn5449 (2024).</p> <p>The data file labeled "HuCb_filtered.rds" is a processed and annotated single-nucleus RNA-seq Seurat object, containing the gene-level count data for the multiplexed snRNA-seq experiment performed on post-mortem human cerebellar tissues from patients with SCA7 and unaffected controls. Data obtained from WT and SCA7-266Q mice as described in our paper can be accessed in the NIH Gene Expression Omnibus under accession number GSE269430.</p> <p>There are three code files numbered 00 through 02 which contain analysis code for snRNA-seq data applied to both the mouse and human datasets. These files are sequential and will take the user from CellRanger output, to filtered and annotated Seurat objects, and include details for subclustering analysis as well as our pseudobulk DEseq2 differential expression approach. There are places where the user may need to modify the code based on their computer system, version of R or Seurat, and whether they are processing the 5 week, 8 week, or human data sets; these locations in the code are marked with comments.</p> <ul> <li>The first file, 00_Preprocessing_MULTIseq, begins with CellRanger filtered_feature_barcode_matrix output, extracts cell barcodes, utilizes the MULTIseq deMULTIplex software to match cell barcodes to oligo barcodes from MULTIseq fastq files, and annotates the Seurat file with metadata. Cell type identification and annotation also takes place in this file. Note: the deMULTIplex step will likely need to be run on a high performance compute cluster.</li> <li>The second file, 01_Seurat_Analysis, uses the filtered and annotated Seurat file to calculate useful QC metrics, investigate disease signals, and perform cell type subclustering analyses.</li> <li>The third file, 02_Pseudobulk_DEseq2, contains custom analysis code to extract raw counts for each cell type and each animal from the Seurat file, and uses the DEseq2 package to calculate DEGs, taking into account biological replicates, and raw read count differences between control and SCA7 animals.</li> </ul> </div> </div> </div>
GCN-TFiLM: Modelling Black-box Audio Effects with Time-varying Feature Modulation
<p>Dataset, checkpoints, evaluation, and supplementary material associated with the following publication:</p> <p><a href="https://arxiv.org/abs/2211.00497">Modelling Black-box Audio Effects with Time-varying Feature Modulation</a></p> <p><strong>Citation:</strong></p> <pre><code>@inproceedings{comunita2023modelling, title={Modelling black-box audio effects with time-varying feature modulation}, author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D}, booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} }</code></pre> <p> </p>
Resource-Centric Goal Model Slicing for Detecting Feature Interactions
<p>Supplementary materials of the paper entitled:</p> <p>“Resource-Centric Goal Model Slicing for Detecting Feature Interactions”</p> <p>file01 - The features related to the concept of Decline-Mutual-Exclusion.<br> file02 - The features related to the concept of Decline-Produce-and-Use<br> file03 - The features related to the concept of Decline-State-Changing<br> file04 - The features related to the concept of Enhanced-State-Changing<br> file05 - Zoom-features-2022</p>
Nemo2 (Numbers, fEatures, MOdels, version 2) Testing Files
<p>The publications and research associated with this software is currently under review in the "<em>Journal</em> of <em>Systems</em> and <em>Software</em>".</p> <p>You can find the official Github repository of this dataset in: <a href="https://github.com/danieljmg/Nemo2_models">https://github.com/danieljmg/Nemo2_models</a></p>
FunMap feature data file used for model training
<p>The provided file is a TSV (Tab-Separated Values, gzipped) file that serves as input for training a machine learning model. The file structure consists of rows representing gene pairs and columns containing 16 Spearman correlation coefficients and 16 mutual ranks. The Spearman correlation coefficients capture the strength and direction of the relationship between gene pairs. Additionally, the mutual ranks are derived from the correlation coefficients and are utilized in the training process of the model.</p>
Predicting Pathological Complete Response in Esophageal Squamous Cell Carcinoma Using a Multimodal Model Integrating Clinical, Radiomics, and Deep Learning Features
ClinicalTrials.gov study NCT07181850. IPD Sharing: NO. Countries: 1. Publications: 4.
Modelling the genetic aetiology of complex disease: human-mouse conservation of noncoding features and disease-associated loci
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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.