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414
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ShareScore release 0.9.0
Dataset results
414 results for “Generative Model”
Generation of expandable multipotential distal lung progenitors from human pluripotent stem cells that model idiopathic pulmonary fibrosis [bulk RNA-Seq]
GEO Series GSE245721. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Trained Models for Sliced Iterative Generator (NeurIPS submission)
<p>Trained SIG Models on MNIST, Fashion-MNIST, CIFAR-10 and CelebA, as well as trained autoencoders on CelebA</p>
Dataset and model weights for paper "Multi-Referenced Training for Dialogue Response Generation"
<p>dataset.txt: JSON file of dataset which has multiple references in each training sample</p> <p>gpt2_medium.floor_rel.seed_42.20200407-135521.model.pt: model weights of the finetuned GPT-2 used as a seq-level teacher model</p> <p>gpt2_small.floor_none.seed_42.20200407-133531.model.pt: model weights of the finetuned GPT-2 used as a token-level teacher model (because medium GPT-2 is too large and too slow for token-level KD)</p> <p>roberta_large.floor_none.seed_42.2020-04-01-12_28_50.semi.supervised_by_overall.model.pt: model weights of Roberta-eval for evaluating</p> <p>mturk_results.json: JSON file of Amazon MTurk human evaluation results</p>
Data from: A methylation-to-expression feature model for generating accurate prognostic risk scores and identifying disease targets in clear cell kidney cancer
Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration itself captures relationships in the data that represent highly disease-relevant functions.
Data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al.
<p>The data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al. The datasets are dictionaries and are saved with .npy extensions. The datasets can be loaded in Python with expressions like: 'scenario_base = np.load(f"{filepath}scenario_base_hierarchical.npy",allow_pickle="TRUE").item()'.</p>
Models generated by Alphafold2
<p>All the AlphaFold models predicted for interacting and non-interacting proteins pairs used in the article :</p><p>"AlphaFold2 predicts interactions amidst confounding structural compatibility".</p><p>The archive contains two directories:</p><ul><li>INPUT_SEQUENCES : the input sequences</li><li>AF2_OUTPUT: the resulting models generated by AlphaFold2. This directory contains 3 directories:<ul><li>MSA_DEFAULT : models generated with the MSA default pairing option "pair_mode": "unpaired_paired", with recycling</li><li>MSA_PAIRED : models generated with the MSA pairing option "pair_mode": "paired" and no recycling</li><li>MSA_UNPAIRED : models generated with the MSA pairing option "pair_mode": "unpaired" and no recycling</li></ul></li></ul><p> </p>
A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems - Agent classification table
<p>This table classifies the agents used in the work to those which use models in their decision-making, which are model-based (MB), and those which do not, which are model-free (MF).</p>
Modelling to Generate Alterantives at Ruhr-University Bochum Backbone dataset
<p>Datasets for the Backbone energy model of the Ruhr-University Bochum:</p> <ul> <li>2019 data for validation, demand time series are not published</li> <li>2030 and 2045 data used for Modelling to generate alternatives, demand time series are aggregated</li> </ul> <p>Backbone version 1.4. was used.</p>
DeepStruc: Towards structure solution from pair distribution function data using deep generative models
<p>XYZ files, PDF dataset and XGB model to use MetalFinder which is one of the baseline models in the paper.</p>
Auto-resolving atomic structure at van der Waal interfaces using a generative model
<p>Dataset and model parameter for paper 'Auto-resolving atomic structure at van der Waal interfaces using a generative model'</p> <p>The dataset will be made public after the papers are accepted.</p> <p>Modified DRIT training data: xxx.tar.gz</p> <p>Modified DRIT Model parameter:modified_dirt_xxx.pth</p> <p>Stacking Pattern Analysing Model training data: xxx_als.tar.gz</p> <p>Bilayer ReS2 Stacking Pattern Analysing Model's parameter : res2_bs.pth</p> <p>MoS2 stacking pattern training data is subvolume compressed, please download all subvolumes to use this data set</p>
Mechanistic Exploration and Kinetic Modeling through In-Silico Data Generation and Probabilistic Machine Learning Analysis
<p>This zip file includes the dataset 'two_reactions_022624.csv,' which is used for training and testing ML/DL models in the paper 'Mechanistic Exploration and Kinetic Modeling through In-Silico Data Generation and Probabilistic Machine Learning Analysis,' as well as trained models and some files used for training the model. When running the model downloaded from GitHub, copy and paste the files downloaded from here into the subfolder with the same name and path as the one downloaded from GitHub.</p>
AlphaFold 2 generated models of SurA homologues
<p>Models of SurA homologues which are present in the <span>InterPro family </span><span>IPRO15391<span> but are not in the EBI AlphaFold database (2024)</span></span></p>
Data generated by our developed theoretical asperity contact creep model
<p>The uploaded data was generated by our developed theoretical asperity contact creep model of interfacial friction for Geomaterials, including excel-style data and origin-style data.</p>
Bambu: a simple tool to generate QSAR models from bioassays data
<p>Quantitative Structure Activity Relationship (QSAR) is a computational method that allows the estimation of the properties of a molecule, including its biological activity, based on its structure. QSAR models have been widely employed in the search for potential drug candidates, but also for agrochemicals and other molecules with applications on different branches of the industry. Here we present Bambu, a simple command line tool to generate QSAR models from high-throughput screening bioassays datasets. </p> <p>The tool was developed using the Python programming language and relies mainly on RDKit for molecule data manipulation, FLAML for automated machine learning and the PubChem REST API for data retrieval. As a proof-of-concept we have employed the tool to generate QSAR models for melanoma cell growth inhibition based on HTS data and used them to screen libraries of FDA-approved drugs and natural compounds.</p> <p>Based on the developed tool we were able to produce QSAR models and identify a wide variety of molecules with potential melanoma cell growth inhibitors, many of which with anti-tumoral activity already described. The tool is available through the URL <a href="http://caramel.ufpel.edu.br">http://caramel.ufpel.edu.br</a>.</p> <p> Bambu is an free and open source tool which facilitates the creation of QSAR models and can be futurely applied in a wide variety of drug discovery projects.</p>
Dataset and Software for High-resolution mantle flow models reveal importance of plate boundary geometry and slab pull forces on generating tectonic plate motions
<p>This repository contains the plugin and dataset used to setup models in the manuscript: "High-resolution mantle flow models reveal importance of plate boundary geometry and slab pull forces on generating tectonic plate motions". It also includes the corresponding versions of the geodynamics software ASPECT and Worldbuilder which were used to develop the models in the paper.</p> <p>The material model plugin used in our models is in the "plugins" folder. The "models" folder contains the reference input parameter file described in the paper. All other model configurations shown in the paper can be obtained by modifying this parameter file. The input files used to set up the models are in the respective folder. Additionally, the Jupyter notebook used to compute residuals of our models is provided in the "scripts" folder.</p>
Results of differential expression analysis for the manuscript "N-of-one differential gene expression without control samples using a deep generative model"
<p>Output files for the differential gene expression analysis in the manuscript "N-of-one differential gene expression without control samples using a deep generative model". The zip file contains three folders with the results for DESeq2 and the DGD (named NB-GMM in the folders).</p><p>- The folder False_positives_Fig4B contains the results presented in figure 4B of the manuscript. The file False_positive_single_vs_all.csv contains the results for the one-vs-all experiments. The file False_positive_5_vs_all.csv contains the results for the 5-vs-all experiments.</p><p>- The folder enrichment_plots contains the results presented in figure 4C of the manuscript. Each subfolder (e.g. Basal, HER2E, LumA, LumB) contain the differential expression analysis results for each breast cancer subtype.</p><p>- The folder fig5 contains the results presented in figure 5 of the manuscript. The results for each cancer are presented in a folder.</p>
Dataset and Checkpoints of MolEdit: In-silico 3D Molecular Editing through Physics-Informed and Peference-Aligned Generative Foundation Models
<p>This repository hosts the pre-trained checkpoints and data utilized for the development of MolEdit. The corresponding research paper, titled "<em>In-silico</em> 3D Molecular Editing through Physics-Informed and Peference-Aligned Generative Foundation Models" (an early version is preprinted at <a href="doi.org/10.26434/chemrxiv-2023-j2n6l-v2">doi:10.26434/chemrxiv-2023-j2n6l-v2</a>) and the corresponding GitHub repository of <a href="https://github.com/issacAzazel/MolEdit">MolEdit</a> details the application and validation of these checkpoints and data. For further information, please refer to the README.md file contained within this repository.</p>
A Generative Model-based System for Predicting Survival and Guiding Treatment Decisions in Patients With Unresectable Hepatocellularcarcinoma Undergoing Transcatheter Arterial Chemoembolization in Com
ClinicalTrials.gov study NCT07065786. IPD Sharing: NO. Countries: 1. Publications: 0.
Predictive Performance of a Generative Model for Corneal Tomography After ICL Implantation
ClinicalTrials.gov study NCT07146737. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Thrombin Generation Numerical Models Validation in Haemophilic Case
ClinicalTrials.gov study NCT02300519. IPD Sharing: Not stated. 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.