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415 results for “CRC”
Feature selection on microbial profiles of CRC samples with chopin2 (powered by hdlib)
<p>This Zenodo entry contains the result of the feature selection algorithm implemented through a backward variable elimination strategy in <a href="https://github.com/cumbof/chopin2" target="_blank" rel="noopener">chopin2</a> (powered by <a href="https://github.com/cumbof/hdlib" target="_blank" rel="noopener">hdlib</a>) applied on <a href="https://github.com/biobakery/MetaPhlAn" target="_blank" rel="noopener">MetaPhlAn3</a> microbial profiles of a public dataset of metagenomic stool samples collected from patients affected by the colorectal cancer (CRC) as well as from healthy individuals.</p> <p>Microbial profiles have been extracted through the <a href="https://bioconductor.org/packages/release/data/experiment/html/curatedMetagenomicData.html" target="_blank" rel="noopener">curatedMetagenomicData</a> package for R under the IDs <em>ThomasAM_2018a</em>, <em>ThomasAM_2018b</em>, and <em>ThomasAM_2019_a</em>.</p> <p>The feature selection algorithm is implemented as a backward variable elimination method, and it makes use of the vector-symbolic architecture described in <a href="https://doi.org/10.3390/a13090233" target="_blank" rel="noopener">Cumbo F 2020</a>.</p> <p>Deposited data is described below:</p> <ul> <li><em>datasets.tar.gz</em>: it contains the datasets used as input of <em>chopin2</em> as the result of merging the three datasets with relative abundances mentioned above, also stratified by age and sex (with prefix RA). The same datasets have been also binarized (with prefix BIN);</li> <li><em>hd-models.tar.gz</em>: it contains the output of the feature selection performed with <em>chopin2</em> (powered by <em>hdlib</em>) on the datasets with both relative abundance and binary profiles (RA and BIN);</li> <li><em>ml-models.tar.gz</em>: it contains the result of the feature selection produced with classical wrapper-based techniques (i.e., Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting) in addition to a Python 3.8 script to reproduce the results.</li> </ul> <p>Please note that the datasets <em>RA__ThomasAM__species.csv</em> and <em>BIN__ThomasAM__species.csv</em> are also included into the <em>datasets.tar.gz</em> archive.</p>
16S rRNA sequencing gene datasets for CRC data
<p>Used datasets: </p> <table> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>Dataset</th> <th>16S rRNA Region</th> <th>Control (n)</th> <th>Adenoma (n)</th> <th>CRC (n)</th> <th>Available metadata</th> </tr> </thead> <tbody> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4823848/">Baxter</a></td> <td>V4</td> <td>171</td> <td>198</td> <td>120</td> <td>Gender, age, weight, height, BMI, country, race</td> </tr> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221363/">Zackular</a></td> <td>V4</td> <td>30</td> <td>30</td> <td>30</td> <td>Gender, age, weight, height, BMI, country, race, FOBT, medication</td> </tr> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299606/">Zeller</a></td> <td>V4</td> <td>50</td> <td>38</td> <td>41</td> <td>Gender, age, BMI, country, FOBT</td> </tr> <tr> <td><strong>TOTAL</strong></td> <td>V4</td> <td>251</td> <td>266</td> <td>191</td> <td><em>All of the above</em></td> </tr> </tbody> </table> </th> </tr> </thead> <tbody> <tr> <td> </td> </tr> </tbody> </table> <p>Data processing & sharing</p> <p>All datasets were processed using <a href="https://docs.qiime2.org/2021.11/">qiime2</a> pipeline with <a href="https://benjjneb.github.io/dada2/">DADA2</a> for Sequence quality control and feature table construction and <a href="https://www.arb-silva.de/">SILVA</a> database for taxonomic assignment, and then a <em>phyloseq </em>object was constructed.</p> <ul> <li>Abundance table at genus level is in file <em>genus.csv</em> (Sample counts with NO filtering).</li> <li>Clean metadata is in <em>metadata.csv</em> file (Countries: CA - Canada. USA - United States of America. FRA - France.)</li> <li>Phyloseq object is in file <em>physeq.RDS</em> (Saved as an RDS object in R)</li> </ul> <p>More information is <a href="https://hackmd.io/nbsLqCLlSNSRFc5RBX9c5Q?view">here</a>. </p> <p> </p>
An Evaluation of a Multi-target Stool DNA (Mt-sDNA) Test, Cologuard, for CRC Screening in Individuals Aged 45-49 and at Average Risk for Development of Colorectal Cancer: Act Now
ClinicalTrials.gov study NCT03728348. IPD Sharing: YES. Countries: 1. Publications: 1.
Image tiles of TCGA-CRC-DX histological whole slide images, non-normalized, tumor only
<p>These are image tiles of tumor tissue of N=604 colorectal cancer (CRC) histological whole slide images in the TCGA database. The tumor tissue was manually outlined and cut into non-overlapping tiles of 512x512 px at 0.5 µm/px. No further preprocessing was applied. No color normalization was applied. Tiles are compressed with JPEG. Each ZIP file corresponds to one whole slide image (original format: SVS).</p> <p>Please cite our previous publication related to this dataset: https://www.nature.com/articles/s41591-019-0462-y</p> <p>Please observe the original TCGA licenses if you use the data https://portal.gdc.cancer.gov/</p> <p>Original image credit to TCGA: https://portal.gdc.cancer.gov/</p> <p>Image tiles were created with QuPath v0.1.2 https://qupath.github.io</p> <p>Genetic data matching these images are available at https://cbioportal.org</p> <p>More information on the procedures: https://zenodo.org/record/3694994</p>
Metagenome-assembled genomes (MAGs), colorectal cancer (CRC)
<p>This archive contains (i) Metagenome assemblies of short-term enrichment cultures of CRC mucosal tissue microbiota, and (ii) Reconstructed metagenome-assembled genomes (MAGs) generated through binning of metagenome contigs.</p>
Following the mixtures of organic micropollutants with in-vitro bioassays in a large lowland river from source to sea - bioassays CRC
<p>Supportive material for the submitted paper: </p> <p><strong><em><span>Following the mixtures of organic micropollutants with in-vitro bioassays in a large lowland river from source to sea </span></em></strong></p> <p><span>from Hommel et al.</span></p> <p><span>The data includes the with R automated evaluation of the AhR-CALUX, AREc32, ERa-GeneBLAzer and SH-SY5Y assay with respective plots and excel files of the concentrations response curves.</span></p>
CRC Metacohort
<p>This repository provides the processed metacohort created from different colon cancer datasets with following GEO series: GSE14333, GSE143985, GSE17536, GSE17537, GSE33114, GSE38832, GSE39582. The .RData file contains two data frames enclosing the following information:</p> <ol> <li>‘pheno.data’ data frame includes patients phenotypic information. Rows refer to patients and columns to pheno variables. Disease free survival (DFS) time and event are available for all patients, but other variables might show missing values.</li> <li>‘exprs.data’ data frame includes normalized and batch corrected microarray expression data. Rows refer to microarray probes and columns to patients except for the first three colums that indicate corresponding probe id, gene symbol and Jetset score. </li> </ol> <p>Source code for data generation can be found at the following github repository: <a href="https://github.com/BigaSpinosaLab/RESOURCES_CRC_metacohort_generation" target="_blank" rel="noopener">https://github.com/BigaSpinosaLab/RESOURCES_CRC_metacohort_generation.</a></p> <p>In addition, a summary of the metacohort has been added.</p>
Metagenome-assembled genomes(MAGs) generated from CRC human gut (PRJEB27928).
<p>MAGs generated from CRC human gut (PRJEB27928) with Maxbin2, VAMB, Metabat2, SemiBin(single-sample binning) and VAMB, SemiBin(multi-sample binning).</p> <p>Single-sample binning: Maxbin2.tar.gz, Metabat2.tar.gz, VAMB.tar.gz and SemiBin(_pretrain).tar.gz. </p> <p>Multi-sample binning: VAMB_multi.tar.gz and SemiBin_multi.tar.gz.</p>
COLUMBIA-1: Novel Oncology Therapies in Combination With Chemotherapy and Bevacizumab as First- Line Therapy in MSS-CRC
ClinicalTrials.gov study NCT04068610. IPD Sharing: YES. Countries: 5. Publications: 1.
Diet and Physical Activity Intervention in CRC Survivors
ClinicalTrials.gov study NCT01708824. IPD Sharing: NO. Countries: 1. Publications: 3.
Community Collaboration to Advance Racial/Ethnic Equity in CRC Screening
ClinicalTrials.gov study NCT05714644. IPD Sharing: NO. Countries: 1. Publications: 1.
The PRIME-CRC Trial to Promote CRC Screening in Rural Communities
ClinicalTrials.gov study NCT04313114. IPD Sharing: NO. Countries: 1. Publications: 1.
Voyage: Real-World Impact of the Multi-target Stool DNA Test on CRC Screening and Mortality
ClinicalTrials.gov study NCT04124406. IPD Sharing: YES. Countries: 1. Publications: 2.
Navigation From Community to Clinic to Promote CRC Screening in Underserved Populations
ClinicalTrials.gov study NCT01853774. IPD Sharing: NO. Countries: 1. Publications: 3.
Safety and Efficacy of Vicriviroc (MK-7690) in Combination With Pembrolizumab (MK-3475) in Participants With Advanced/Metastatic Microsatellite Stable (MSS) Colorectal Cancer (CRC) (MK-7690-046)
ClinicalTrials.gov study NCT03631407. IPD Sharing: YES. Countries: 2. Publications: 1.
Effectiveness and Implementation of mPATH-CRC
ClinicalTrials.gov study NCT03843957. IPD Sharing: YES. Countries: 1. Publications: 65.
Screening More Patients for Colorectal Cancer Through Adapting and Refining Targeted Evidence-Based Interventions in Rural Settings, SMARTER CRC
ClinicalTrials.gov study NCT04890054. IPD Sharing: YES. Countries: 1. Publications: 3.
Increasing Colorectal Cancer (CRC) Screening Among Hispanic Primary Care Patients
ClinicalTrials.gov study NCT02272244. IPD Sharing: NO. Countries: 1. Publications: 1.
Ph1b/2 Dose-Escalation Study of Entinostat With Pembrolizumab in Non-small Cell Lung Cancer (NSCLC) With Expansion Cohorts in NSCLC, Melanoma, and Colorectal Cancer (CRC)
ClinicalTrials.gov study NCT02437136. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Efficacy and Safety of Olaparib (MK-7339) With or Without Bevacizumab Compared to Bevacizumab With a Fluoropyrimidine in Unresectable or Metastatic Colorectal Cancer (CRC) (MK-7339-003/LYNK-003)
ClinicalTrials.gov study NCT04456699. IPD Sharing: YES. Countries: 18. Publications: 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.