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217 results for “precision medicine”
Dataset for "Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia"
<p><strong>Article: Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia</strong></p> <p><em>Cancer Discovery</em>, <strong>DOI:</strong> 10.1158/2159-8290.CD-21-0410</p> <p> </p> <p>Data Types:</p> <p>1. Clinical summary</p> <p>2. Drug response data</p> <p>3. Exome-sequencing data</p> <p>4. RNA-sequencing data</p> <p> </p> <p><strong>Updates:</strong></p> <p>- <strong>FILE</strong>: File_3.2. <strong>DATE</strong>: 28.11.2022.</p> <p> </p> <p><strong>1. Clinical summary</strong></p> <p><strong>File_0: </strong>Common sample annotation including patient and sample IDs, stage of the disease, tissue type and availability of different data types.</p> <p><strong>File_1.1: </strong>Clinical data for 186 AML patients including clinical diagnosis, disease classification, gender, age at diagnosis, treatments, cytogenetic and molecular details. The description of the variables/column titles is given below the clinical data.</p> <p><strong>File_1.2</strong>: Description of the clinical variables in File_1.1.</p> <p> </p> <p><strong>2. Drug response data for 164 AML patient samples and 17 healthy samples</strong></p> <p><strong>File_2: </strong>Drug library details for 515 chemical compounds. The compound collection includes drugs names, drug class defined by molecular targets or mode of action, concentration range used for drug testing, supplier information, solvent information and vendor information.</p> <p><strong>File_3.1.: </strong>Drug response data including selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The DSS is modified area under the curve values and are calculated as shown in Yadav et al publication (1). The selective drug sensitivity scores (sDSS) is healthy control normalized DSS that gives estimated cancer-selective drug responses. The higher the sDSS values indicate drug sensitivities and negative sDSS values represent drug resistance.</p> <p><strong>File_3.2.: </strong>Drug response data including drug sensitivity scores (DSS) and selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The data is identical to the Supplementary Table 7 in the manuscript.</p> <p><em>Note: We recommend using selective DSS values instead of raw values (% inhibition, IC50, DSS). </em></p> <p><em>Note: If the value is missing, </em><em>the drug was not tested for </em><em>that</em><em> given sample</em><em>.</em></p> <p><strong>File_4: </strong>Drug sensitivity and resistance testing (DSRT) assay details for 181 samples (164 AML patient samples and 17 healthy control samples). The information includes medium (MCM or CM) used for the drug testing, % cell viability after 72 h without drug testing and blast cell percentage of each sample.</p> <p><em>Note: Column E is </em><em>the ratio of luminescence values at 72 h and 0 h. The fold change in the cell viability without drug treatment was calculated as % cell viability. That is why the value could be more than 100% e.g. 70% cell viability meaning that 30% cells died during 72 h and 300% cell viability meaning that cells grew 3 times in 72 h incubation period.</em></p> <p> </p> <p><strong>3. Exome-sequencing data for 225 AML patient samples</strong></p> <p><em>Note: The number of samples in the manuscript is 226. The correct number used in the analyses is 225.</em></p> <p>Mutation data. The cancer specific gene list was prepared by combining AML related genes from TCGA(2) (n=23), InToGen(3) (n=32), Papaemmanuil et al.(4) (n=111) and Census database(5) (n=616). Out of these genes, we found 340 genes as mutated across 225 AML patient samples. The mutation was called with P-values less than 0.05.</p> <p><strong>File_5: </strong>VAF (variant allele frequency) of 340 cancer-specific genes across 225 AML patient samples. The VAF was calculated using paired skin samples as a control from the same AML patient.</p> <p><strong>File_6:</strong> Binary data for 57 cancer specific genes frequently mutated (a given mutation detected in 5 or more samples) across 225 AML patient samples.</p> <p> </p> <p><strong>4. RNA-sequencing data for 163 AML patient samples and 4 healthy</strong></p> <p>CPM (count per million) data: The CPM values are batch corrected values used for direct comparison of gene expression.</p> <p><strong>File_7:</strong> Log2CPM values for 18,202 protein coding genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p><strong>File_8: </strong>Raw read count data RNA-seq library information for all 60,619 genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples). The raw read count data was used to calculate differential gene expression.</p> <p><strong>File_9: </strong>RNA-seq library information including RNA extraction method and sequencing library preparation information for 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p> </p> <p><strong>References</strong></p> <p>1. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R<em>, et al.</em> Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports <strong>2014</strong>;4:5193.</p> <p>2. Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A<em>, et al.</em> Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med <strong>2013</strong>;368(22):2059-74.</p> <p>3. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A<em>, et al.</em> IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods <strong>2013</strong>;10(11):1081-2.</p> <p>4. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND<em>, et al.</em> Genomic classification and prognosis in acute myeloid leukemia. New England Journal of Medicine <strong>2016</strong>;374(23):2209-21.</p> <p>5. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N<em>, et al.</em> COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research <strong>2019</strong>;47(D1):D941-D7.</p> <p> </p>
Measuring platelet function: new strategies for precision medicine to prevent thrombosis - Prof Jon Gibbins (University of Reading)
<p>This video is the third talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Measuring platelet function: new strategies for precision medicine to prevent thrombosis - Prof Jon Gibbins (University of Reading).</p> <p>Bio: Jon Gibbins is Professor of Cell Biology within the School of Biological Sciences at the University and is Director of the Institute for Cardiovascular and Metabolic Research. He is a graduate of the University, obtaining a degree in Pathobiology with Chemistry in 1991 and a PhD in Molecular Endocrinology in 1995. Following a period of postdoctoral research at the Oxford University, he returned to Reading in 1998 as a lecturer. Jon has established an internationally leading research group that studies blood clotting, with a particular focus on the development of more effective clinical strategies for the prevention and treatment of heart attacks and strokes, and thrombosis associated with infection. Jon values greatly working in an active, engaging and successful school, in which all aspects of biology are represented, and he champions cross-disciplinary working to approach today’s most challenging and pressing questions in new ways. He believes strongly in widening participation and improving levels of equity, diversity and inclusion across our institution.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/8vJZ_WO-dvk</p>
Dataset: Praxis Precision Medicines, Inc. (PRAX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data and materials for "The Consequences of Data Dispersion in Genomics: A Comparative Analysis of Data Sources for Precision Medicine" manuscript"
<p>Data and sripts for the "The Consequences of Data Dispersion in Genomics: A Comparative Analysis of Data Sources for Precision Medicine" manuscript" manuscript, sent to BMC Bioinformatics</p>
Perinatal Precision Medicine
ClinicalTrials.gov study NCT03211039. IPD Sharing: YES. Countries: 1. Publications: 11.
Extended data for Manuscript: Precision medicine implementation and research-practice partnerships: implications of measurement scale differential item functioning (DIF)
<p>This is the extended data for the manuscript submitted to F1000 Research titled:</p> <p>Precision medicine implementation and research-practice partnerships: implications of measurement scale differential item functioning (DIF).</p> <p>File 1 is the redacted survey responses from a card sort exercise carried out to find out how the study participants sorted and ranked various measures of factors thought to influence precision medicine implementation at health systems level.</p> <p>File 2 consists of the study package as described in the article</p>
PRIME Care (PRecision Medicine In MEntal Health Care)
ClinicalTrials.gov study NCT03170362. IPD Sharing: YES. Countries: 1. Publications: 55.
Characterization of Type 2 Diabetes Subgroups at Diagnosis: a Necessary Step Towards Precision Medicine in Diabetes.
ClinicalTrials.gov study NCT05333718. IPD Sharing: NO. Countries: 1. Publications: 1.
Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Precision Medicine Phase 2 Option 1
ClinicalTrials.gov study NCT04602806. IPD Sharing: YES. Countries: 1. Publications: 12.
Head and Neck Cancer Omics/Integrated Precision Medicine (HOPE)
ClinicalTrials.gov study NCT07190573. IPD Sharing: NO. Countries: 1. Publications: 7.
Optimizing Immunosuppression Drug Dosing Via Phenotypic Precision Medicine
ClinicalTrials.gov study NCT03527238. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Kidney Precision Medicine Project
ClinicalTrials.gov study NCT04334707. IPD Sharing: Not stated. Countries: 1. Publications: 20.
Data from: Creation of de novo cryptic splicing for ALS/FTD precision medicine
Open the record for dataset details and reuse information.
Experimental Data for "What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way" at SIGIR2020
<p>This deposit contains data used for the experiments reported in the paper "<a href="https://doi.org/10.1145/3397271.3401048">What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way</a>", most notably the ElasticSearch 5.4 indices used for the reported experiments.</p> <p>To load the indices into an ElasticSearch cluster of your own, use the restore function described in the <a href="https://www.elastic.co/guide/en/elasticsearch/reference/5.4/modules-snapshots.html">ElasticSearch documentation</a>.</p> <p>The names of the index snapshots contained here are</p> <ul> <li>ct1718 for the indexed ClinicalTrials data used in the TREC-PM challenges in <a href="http://www.trec-cds.org/2017.html">2017</a> and <a href="http://www.trec-cds.org/2018.html">2018</a>.</li> <li>ct19 for the indexed ClinicalTrials data used in the TREC-PM challenge in <a href="http://www.trec-cds.org/2019.html">2019</a>.</li> <li>ba1718 for the indexed PubMed data used in the TREC-PM challenges in <a href="http://www.trec-cds.org/2017.html">2017</a> and <a href="http://www.trec-cds.org/2018.html">2018</a>.</li> <li>ba19 for the indexed PubMed data used in the TREC-PM challenge in <a href="http://www.trec-cds.org/2019.html">2019</a>.</li> </ul> <p>The other file contains the original output that <a href="https://www.automl.org/automated-algorithm-design/algorithm-configuration/smac/">SMAC</a> wrote to disc during the parameter optimization process. There are directories for the biomedical abstracts (BA) and clinical trials (ct) and for each respective 10 fold cross validation split. Those file contain the exact parameter configurations and their evalation score (the infNDCG metric was used) in live-runXX.json files.</p> <p>The code to these files is located in <a href="https://zenodo.org/record/3856403">this Zenodo deposit</a>.</p>
Results from simulation of Dynamic Precision Medicine 2-Window Trial
<p>Results from the simulation adapted from Beckman et al. PNAS 2012. For details see the preprint "A Generalized Evolutionary Classifier for Evolutionary Guided Precision Medicine" https://www.medrxiv.org/content/10.1101/2020.09.24.20201111v2</p> <p> </p> <p>The simualtion code that generated these results can be found at: https://github.com/GU-DPM/EvolutionaryClassifier</p> <p>------------------------------------------------------------------</p> <p>Annotations of simulation outcome files.</p> <p>Simulation outcomes are stored in multiple files.</p> <p>param_ALLDRUG_xxx.txt: parameter configuration values.<br>stopt_ALLDRUG_xxx.txt: survival times for each parameter configuration and each strategy.<br>dosage_ALLDRUG_xxx.txt: dosage combination sequences for each parameter configuration and each strategy.<br>pop_ALLDRUG_xxx.txt: population composition dynamics for each parameter configuration and each strategy.</p> <p>xxx denotes the index of the job when I ran simulations in my cluster. You can simply concatenate the files over all indices.</p> <p><br>param_ALLDRUG_xxx.txt format:</p> <p>Each line denotes a parameter configuration with the following entries:</p> <p>(1)parameter configuration index (one entry).<br>(2)initial population compositions x0 (four entries, x0(S), x0(R1), x0(R2), x0(R12)).<br>(3)growth rate g0 (one entry).<br>(4)drug sensitivity matrix Sa (eight entries, Sa(S,drug1), Sa(S,drug2), Sa(R1,drug1), Sa(R1,drug2), ...).<br>(5)transition rate matrix T (sixteen entries, T(S->S), T(S->R1), T(S->R2), T(S->R12), T(R1->S), T(R1->R1), T(R1->R2), T(R1->R12), ....</p> <p><br>stopt_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the survival times (stopping times) of 10 strategies for each parameter configuration.</p> <p>strategy 0: strategy 0 in the PNAS paper.<br>strategy 1: strategy 2.2 in the PNAS paper.<br>strategy 2: strategy 2.2 in the PNAS paper for first two treatment decisions, followed by strategy 0 in the PNAS paper</p> <p>Each line has 11 entries: parameter configuration index and the survival times of the 10 strategies.</p> <p>The time horizon of the therapy is 1800 days. Each period is 45 days. If the patient is cured (the total tumor population = 0), then the survival time is reported as 1845 days. If the survival time is 1800 days, then the tumor population size is greater than 0 and smaller than the mortal level (1e13).</p> <p><br>dosage_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the dosage combination sequence of each strategy for each parameter configuration.</p> <p>(1)parameter configuration index.<br>(2)strategy index (0-2).<br>(3)(drug1 dosage,drug2 dosage) at t=0.<br>(4)(drug1 dosage,drug2 dosage) at t=45.<br>...<br>(42)(drug1 dosage,drug2 dosage) at t=1755.</p> <p>If t exceeds the survival time of the strategy, then the drug dosage is set to -1.</p> <p><br>pop_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the population composition dynamics of each strategy for each parameter configuration.</p> <p>(1)parameter configuration index.<br>(2)strategy index (0-9).<br>(3)(S,R1,R1,R12) population size at t=45.<br>(4)(S,R1,R1,R12) at t=90.<br>...<br>(42)(S,R1,R1,R12) at t=1800.</p> <p>If t exceeds the survival time of the strategy, then the population size is set to -1.</p> <p>Notice the dosage combination is reported at the beginning of each period, and the population composition is reported at the end of each period.</p> <p>When the best strategy over strategies 0-8 cures the patient and yields survival time 1800 days, I did not bother running strategy 9 (since it's time consuming). Thus the dosage combination and population composition should be ignored.</p> <p> </p>
PRecisiOn MEdicine to Target Frailty of Endocrine-metabolic Origin
ClinicalTrials.gov study NCT04856683. IPD Sharing: Not stated. Countries: 1. Publications: 55.
PRospective REgistry of Advanced Stage CancER (PREFER) Patients to Assess Prevalence of Actionable Biomarkers and Driver Mutations to Address Disparities in Precision Medicine
ClinicalTrials.gov study NCT05697198. IPD Sharing: UNDECIDED. Countries: 1. Publications: 14.
Precision Medicine to Predict the Trajectory of Liver Cirrhosis: Prospective Cohort Study
ClinicalTrials.gov study NCT05899309. IPD Sharing: NO. Countries: 1. Publications: 2.
Community Health Workers and Precision Medicine
ClinicalTrials.gov study NCT04843332. IPD Sharing: NO. Countries: 1. Publications: 4.
PREcision Medicine Directed Corticosteroids In Children With preSchool Wheeze
ClinicalTrials.gov study NCT06580600. IPD Sharing: NO. Countries: 1. Publications: 0.
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
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.