Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

Dataset 3 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution

Dataset Multi-GO Molecular dynamics simulation trajectories of TTR peptide and aggregation kinetics; Multi-eGO oligomer structures and trajectories: <ul> <li>multi-GO-XXmM: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration.</li> <li>TTR structures and trajectories of oligomers from dimers to decamers.</li> </ul>

opencc-by-4.0May 2022View details →

In silico investigation of Alsin RLD conformational dynamics and phosphoinositides binding mechanism

Raw data of the publication "In silico investigation of Alsin RLD conformational dynamics and phosphoinositides binding mechanism"

opencc-by-4.0Aug 2022View details →

Dataset for in silico and in vitro studies confirm Ondansetron as a novel AChE and BChE inhibitor.

Compressed data from IFD and MD simulations of Ondansetron, Tacrine and Rivastigmine binding to AChE and BChE. Structures of AChE and BChE after protein preparation.

opencc-by-4.0May 2024View details →

Data underlying the article: Molecular insights into disease-associated glutamate transporter (EAAT1 / SLC1A3) variants using in silico

This repository contains the scripts and data for the project published in Frontiers in Molecular Biosciences under the title: Molecular insights into disease-associated glutamate transporter (EAAT1 / SLC1A3) variants using in silico and in vitro approaches (DOI <a href="https://www.frontiersin.org/articles/10.3389/fmolb.2023.1286673/full">10.3389/fmolb.2023.1286673</a>).

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACoA)

This repository contains the dataset for the Missing ACoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database:Missing PCoA)

This repository contains the dataset for the Missing PCoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

in-silico EI-MS

FastEI里面的原始质谱库里面的前1000条记录

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoA and PCA P1)

This repository contains the dataset for the Missing PCoA and PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACA A1)

This repository contains the dataset for the Missing ACA A1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoAs)

This repository contains the dataset for the Missing PCoAs described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).

opencc-by-4.0Jul 2024View details →

In silico mock communities for evaluation of taxonomic profilers across eukaryotes in the human microbiome

In silico mock communities generated with CAMISIM for benchmarking the performance of taxonomic profilers across prokaryotic (50 communities), eukaryotic (30 communities), and viral communities (10 communities) of the human microbiome. Metagenomes were generated using CAMISIM (Fritz et al., 2019), which simulates 2.1 Gb of Illumina 2 ×150 bp paired end reads with the default HiSeq 2500 error profile and a mean insert size of 200 bp. Eukaryotic communities 30 eukaryotic in silico metagenomes comprising up to 200 randomly sampled genomes from a set of 113 eukaryotic species (See Supplementary Table 2 from the paper) corresponding to the eukaryotic species within both CHAMP and MetaPhlAn 4 (Blanco-Míguez et al., 2023) databasess. Prokaryotic and viral communities In silico data for prokaryotic and viral communities from the human microbiome can be found here: <a href="https://doi.org/10.5281/zenodo.10777404">doi: 10.5281/zenodo.10777404</a> References Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., et al. (2023). Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nature Biotechnology 2023 41:11 41, 1633–1644. doi: 10.1038/s41587-023-01688-w Fritz, A., Hofmann, P., Majda, S., Dahms, E., Dröge, J., Fiedler, J., et al. (2019). CAMISIM: Simulating metagenomes and microbial communities. Microbiome 7, 1–12. doi: 10.1186/S40168-019-0633-6/FIGURES/5

opencc-zeroJul 2024View details →

Galaxy Histories with in silico mass spectra of Mirex, Ethylene, Benzophenone and Enilconazole predicted via QCxMS

Galaxy histories containing spectra predicted using the QCxMS software. The molecules are Mirex, Ethylene, Benzophenone and Enilconazole. Calculations have been carried our using Galaxy and histories exported as ROCrates.

opencc-by-4.0Aug 2024View details →

Repurposing Major Metabolites of Lamiaceae Family as Potential Inhibitors of α-Synuclein Aggregation to Alleviate Neurodegenerative Diseases: An In Silico Approach

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →

Salmonella In Silico Typing Resource (SISTR) commandline tool database version 1.1 used by SISTR tool up to release version 1.1.2 inclusive

<h3>Context</h3> Salmonella In Silico Typing Resource (<a href="https://github.com/phac-nml/sistr_cmd/tree/master">SISTR</a>) commandline tool enables the identification of the Salmonella serovar and cgMLST types of Salmonella from whole genome sequencing (WGS) data tby using a large database (10,000+) of Salmonella genomes and cgMLST profiles based on the 330 alleles. This database is the central part of the SISTR tool and contains both metadata on 2672 serovars and the corresponding antigenic formula, 84464 genomes to serovar mappings, sequences of the O, H1 and H2 antigens, 139729 cgMLST sequences and  38240 profiles with pairwise distances, MASH sketch of the 15465 genomes used for species and serovar identification. For more information and citation please refer to the following publication and official repository at <a href="https://github.com/phac-nml/sistr_cmd/tree/master">https://github.com/phac-nml/sistr_cmd/tree/master </a> Note: This database was used by SISTR tool up to version 1.1.2 inclusive. From SISTR release 1.1.3 onwards the slightly modified version of this database will be used onwards with changes detailed in <a href="https://github.com/phac-nml/sistr_cmd/blob/master/CHANGELOG.md">https://github.com/phac-nml/sistr_cmd/blob/master/CHANGELOG.md</a> <h3>Citation</h3> The Salmonella In Silico Typing Resource (SISTR): an open web-accessible tool for rapidly typing and subtyping draft Salmonella genome assemblies. Catherine Yoshida, Peter Kruczkiewicz, Chad R. Laing, Erika J. Lingohr, Victor P.J. Gannon, John H.E. Nash, Eduardo N. Taboada. PLoS ONE 11(1): e0147101. doi: 10.1371/journal.pone.0147101. <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101</a>

In-Silico Mass Spectra for Compounds in the Metabolon's Annotation List (Open-Access)

<div> In-silico spectra (+ve mode, M+H) were generated for Metabolon's data dictionary ( https://zenodo.org/records/10974865 ) using the ICEBERG model, re-trained using MONA/GNPS/NIST2020 spectra.  Disclaimer: These are simulated spectra so they should be used with cautions to annotate compounds in LC-HRMS datasets.    </div>

In-silico Mass Spectra for PFAS in the Blood Exposome Database

In-silico spectra were generated for the PFAS compounds in the Blood Exposome Database using the ICEBERG model, re-trained using MONA/GNPS/NIST2020 spectra.

In-silico Mass Spectra for FAO_WHO_JECFA Chemical List

In-silico spectra were generated for the FAO_WHO_JECFA Chemical List using the ICEBERG model, re-trained using MONA/GNPS/NIST2020 spectra.

Studying Therapy Effects and Disease Outcomes in Silico using Artificial Counterfactual Tissue Samples

Counterfactual samples created by our generative method the CF-HistoGAN introduced in https://doi.org/10.48550/arXiv.2302.03120. This model was trained on and transformed data from 2 different datasets: the colorectal cancer (CRC) dataset by Schürch et al. https://doi.org/10.1016/j.cell. 2020.07.005 and the cutaneous T cell lymphoma (CTCL) by (Phillips et al https://doi.org/10.1038/s41467-021-26974-6.

In-silico Mass Spectra for HMDB 65k metabolites

In-silico spectra were generated for HMDB 65k metabolites using the ICEBERG model, re-trained using MONA/GNPS/NIST2020 spectra.

In-silico Mass Spectra for PFAS compounds in Negative mode

In-silico spectra were generated for PFAS compounds in negative mode, using the ICEBERG model, re-trained using MONA/GNPS/NIST2020 spectra. Disclaimer: These are simulated spectra so they should be used with cautions to annotate compounds in LC-HRMS datasets.