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Data for the thesis "Exploring Heuristics for Predicting Microbenchmark Stability and Code Coverage using Static Code Analysis"
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Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction
<p>This Zenodo repository provides comprehensive resources for the paper titled "Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction" published on <a href="https://academic.oup.com/bioinformatics/article/41/8/btaf429/8238154">Bioinformatics</a>. We created a dataset of 63,000 molecular dynamics simulations by performing 10 simulations of 10 ns on 6,300 complexes. Neural networks were developed to learn from this data in order to predict the binding affinities of protein-ligand complexes. The implementation of these neural networks are available on <a href="https://github.com/ICOA-SBC/MD_DL_BA" target="_blank" rel="noopener">github</a>. Our collection includes training/benchmark datasets, trained statistical models, and results on test sets (CSV & PDF files).</p> <p> </p> <p><strong>Training/benchmark datasets:</strong></p> <p>Training, validation and test sets are provided to train and evaluate the following neural networks:</p> <ul> <li>Pafnucy, Proli and Densenucy without MD data augmentation (dataset file names contain "initial")</li> <li>Pafnucy, Proli and Densenucy with MD data augmentation (dataset file names contain "MDDA")</li> <li>Pafnucy with/without MD data augmentation and Proli and Densenucy with MD data augmentation were also evaluated on the fep test set (test set file name contain "fep")</li> <li>Timenucy and Videonucy using spatiotemporal learning methods (dataset file names contain "4D")</li> <li>Pafnucy without MD data augmentation and on a reduced training set (dataset file names contain "reduced")</li> </ul> <p>For each training methodology (MD data augmentation and spatiotemporal learning), we provide the data for the whole complex, only the ligand or only the protein. Additionally for spatiotemporal learning, we provide the data with only the ligand using the tracking mode.</p> <p> </p> <p><strong>Statistical models:</strong></p> <p>We provide the models trained with Pafnucy, Proli, Densenucy, Timenucy and Videonucy. Each models were trained in 10 replicates. </p> <p>For Pafnucy, Proli, Densenucy, we provide the models trained with random and systematic rotations, as well as with or without MD data augmentation.</p> <p>For Proli, Densenucy, Timenucy and Videonucy, we provide the models trained on the whole complex, only the ligand or only the protein.</p> <p>For Pafnucy we also provide the models trained on the reduced set (5932 complexes).</p> <p> </p> <p><strong>Results on test sets (CSV & PDF files):</strong></p> <p>We provide the predictions on the PDBbind v.2016 core set.</p> <ul> <li>For spatiotemporal learning methods (Timenucy and Videonucy), there are predictions for only 83 complexes, as we did not perform simulations on the whole test set.</li> <li>For models trained with MD DA, predictions were carried on the crystallographic structures as well as on the frames extracted from the simulations performed on the test set (augmented test).</li> </ul> <p>Results on the FEP dataset are also provided for Pafnucy, Proli and Densenucy.</p> <p> </p> <p>The Raw MD data (~4.5 To) are stored, and can be visualized/downloaded, on the <a href="https://mdposit.mddbr.eu/#/browse?search=MDBind">MDDB</a>.</p> <p>This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-A0100712496 & 2022-AD011013521) and CRIANN (Grant 2021002).</p>
Raw Data for the Manuscript Titled "Can Current Molecular Docking Methods Accurately Predict RNA Inhibitors?"
<p>This is the additional data for the manuscript titled "Can Current Molecular Docking Methods Accurately Predict RNA Inhibitors?". These files include <strong>(a) </strong>The docked scores for scoring potential analysis, <strong>(b)</strong> The docked scores for ranking potential analysis, <strong>(c)</strong> The top five docked poses from each docking method (AutoDock Vina, HADDOCK, HDOCK and RLDOCK) and <strong>(d) </strong>The Molecular Dynamics (MD) simulation parameter and topology files used for pose refinment.</p>
Portal frame bridge video with semantic predictions
<p>This is a video representing how a 3D point cloud acquisition is made for a portal frame bridge. The predictions of a semantic segmentation model that are inferred during the point cloud acquisition are shown on the side. When the scanning of the bridge is complete, the 3D point cloud automatically includes the semantic label per 3D point, thanks to the semantic segmentation model. </p>
Figures with raw prediction samples from submissions to ICDAR'24 MapText Competition
<h2>Raw predictions sample from MapText submissions</h2> <p><em>Generated on June 6th, 2024</em></p> <p>Files structure: <code>{TASK}/{SUBSET}/{SELECTION}/{IMAGEID}.pdf</code> where</p> <ul> <li><code>{TASK}</code> is "task1", "task2", "task3" or "task4"</li> <li><code>{SUBSET}</code> is "rumsey", or "ign" (for French land registers dataset)</li> <li><code>{SELECTION}</code> is "random", "easy" or "hard"</li> <li><code>{IMAGEID}</code> is the image id in the dataset</li> </ul> <p>Example: <code>20-raw-predictions/task2/ign/hard/000016.pdf</code></p> <p>For each image, the PDF contains a comparison of the raw predictions of each submission, with the ground truth.</p> <p>For each task, the random images are the same, but the easy and hard images are different: their selection is based on the mean performance of all submissions regarding the main evaluation metric of the task.</p> <p>Sorting strategies for each task:</p> <ul> <li>Task 1: by detection quality (Panoptic Quality) for isolated words</li> <li>Task 2: by detection quality (Panoptic Quality) for word groups</li> <li>Task 3: by detection quality (Panoptic Quality) for isolated words while constraining matches between the ground truth and predictions to have exactly the same transcription</li> <li>Task 4: by character quality (Panoptic Character Quality) for word groups</li> </ul> <p>Task details and legend for each task:</p> <ul> <li><code>task1/</code>: detections for isolated words. Red means cropped or ignored regions, blue means prediction, green is ground truth.</li> <li><code>task2/</code>: detections for word groups. Groups are colored with different colors, and a link is drawn between successive group members. Ground truth is displayed separately.</li> <li><code>task3/</code>: detections and transcriptions of isolated words. Groups are colored with different colors, and the transcription for each isolated work is overlaid as black text. Ground truth is displayed separately.</li> <li><code>task4/</code>: detection and transcription of word groups. Same visualization as task3, but different sorting for easy and hard images.</li> </ul> <p><br>An additional directory <code>extrafigs/</code> contains a couple of figures with a different layout for inclusion in the report.</p> <p><em>These figures were produced by MapText'24 organizers.</em></p>
A Cross-Sectional Odyssey: Unraveling Treatment Efficacy, Comorbid Impact, and Predictive Markers in Necrotizing Otitis Externa
<p>This study embarks on a comprehensive cross-sectional analysis to explore the multifaceted landscape of Necrotizing Otitis Externa (NOE). By evaluating the efficacy of diverse treatment regimens, the investigation reveals critical insights into the recovery rates, hospitalization durations, and recurrence rates associated with antibiotic monotherapy, combined antibiotic therapy, surgical intervention, and hyperbaric oxygen therapy. Additionally, the impact of comorbid conditions such as diabetes and immunocompromised states on clinical outcomes underscores the complexity of NOE management. Treatment complications, mortality rates, and hospitalization durations differ significantly among patients with varying comorbidities, as reflected by the ANOVA p-values. This odyssey also delves into the initial and final responses to treatment, offering predictive markers for clinical improvement and stability. The integration of these findings aims to enhance therapeutic strategies, providing a nuanced understanding of NOE and guiding clinicians towards optimized patient care.</p>
Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction
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Data used in Predictions based on pixel data: Insights from PDEs and finite differences
<p>This dataset contains the time sequence pixel data used in "Predictions based on pixel data: Insights from PDEs and finite differences" by Elena Celledoni, James Jackaman, Davide Murari and Brynjulf Owren. Each data file is stored using pickle and has the structure</p> <p>Once loaded, the data can be accessed with <strong>data[i][j][x,y] </strong>where</p> <ul> <li><strong>i</strong> chooses an independent simulation</li> <li><strong>j</strong> specifies the time step</li> <li><strong>x</strong> and <strong>y</strong> specify the location in space.</li> </ul>
Raw data for "Ancestral structure prediction reveals the conformational impact of the RuBisCO small subunit across time".
<p>The repository contains raw data for the article "Ancestral structure prediction reveals the conformational impact of the RuBisCO small subunit across time".</p> <p>The repository contains:</p> <ol> <li>Dataset for the RbcL and RbcS sequences along with the inferred sequences for the ancestors of interest</li> <li>Phylogenetic tree for the concatenated and separate RbcL-RbcS sequences.</li> <li>Structures for the extant and ancestral RuBisCO complexes used in the study.</li> <li>Solvated pdb files for creating the topology files required for MD-simulations.</li> </ol>
raw data Figure 1 of the paper entitled "Plasma metabolic signature predictive of developing cancer in patients with Lynch syndrome "
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Predicting Potential Distribution of Ageratina adenophora (Spreng.) King and H. Rob. in Shivapuri Nagarjun National Park, Nepal under Climate Change Scenarios
<p>Data used in the paper</p>
Kroll et al., 2024. Behavioural pharmacology predicts disrupted signalling pathways and candidate therapeutics from zebrafish mutants of Alzheimer's disease risk genes
<p>Data repository for</p> <p>François Kroll, Joshua Donnelly, Joshua Donnelly, Güliz Gürel Özcan, Eirinn Mackay, Jason Rihel</p> <div> <div><strong>Behavioural pharmacology predicts disrupted signalling pathways and candidate therapeutics from zebrafish mutants of Alzheimer’s disease risk genes</strong></div> <br> <div>eLife, 2024.</div> <br> <div><a href="https://doi.org/10.7554/eLife.96839.1">https://doi.org/10.7554/eLife.96839.1</a></div> <div> </div> <div>Code is found in the <a href="https://github.com/francoiskroll/ZFAD">GitHub repository</a>. Please see notes there.</div> </div> <p>___</p> <p>Contact:</p> <p>Twitter: @francois_kroll</p> <p>Email: francois@kroll.be</p>
Code for GMD publication - Introducing Graupel Density Prediction in Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) Microphysics and Evaluation of the Modified Scheme During the ICE-POP Field Campaign
<p>Code for GMD publication - <span>Introducing Graupel Density Prediction in Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) Microphysics and Evaluation of the Modified Scheme During the ICE-POP Field Campaign</span></p> <p>In this repository, we include the source codes for WRF microphysics parameterization used in the GMD publication "Simulated Prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign."</p> <p>The revised WDM6 code, which predicts the graupel density and the original WDM6 code are uploaded. </p> <p>Each code is modified so that detailed microphysical processes can be found in wrfout.</p> <p>Namelist files shows the namelist.input for each case.</p> <p>Furthermore, scripts for figures in the manuscript are included, along with wrfout files.</p>
A Riskscore Model for Predicting Survival, Tumor Microenvironment, Immunotherapy and drug sensitivity of Lung Squamous Cell Carcinoma Based on PI3K/AKT/MTOR Pathway-Related Genes
<p>Firstly, the data we provide is the raw data downloaded from the TCGA database. Secondly, we provide the following explanations for the raw data, taking Figure 1 as an example:</p> <p>First, we selected a LUSC dataset from the TCGA database that includes RNA-seq data (FPKM values) and clear clinical information, consisting of 51 normal samples and 501 LUSC samples. The raw data <span>can be found in</span> the "mRNA" document in the "Fig.1" file<span>, and t</span>he data includes TCGA <span>id</span> information.</p> <p>Next, the data was imported into "mRNA_edgeR" to analyze whether the RNA-seq data from these 552 samples meet the criteria of |logFC| > 0.585 and FDR < 0.05, <span>and the results are shown in</span> the "diffSig" table. Subsequently, the genes in the "diffSig" table were intersected with 105 PAGs, <span>and</span> 44 key PAGs <span>were identified, </span>as shown in Figure 1.</p> <p>The <span>other</span> figures can <span>also </span>be reproduced sequentially based on the methods described <span>above</span>.</p> <p>Furthermore, due to the limited number of LUSC patients, the clinical information in the database is relatively incomplete. Hence, the clinical information that we provide constitutes the entire content available in the database.</p>
Clinical Dataset for the Paper 'Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis'
<p>This dataset accompanies the manuscript titled "Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis." It includes clinical data used for training and evaluating the machine learning models described in the paper. The dataset contains baseline clinical data of 154 RA patients who were treated with bDMARDs. The labels for remission, and effectiveness (remission and low disease activity) were applied after a 6-month follow-up based on EULAR criteria on DAS28ESR. The sustained effectiveness label indicates maintaining effectiveness within 6 months after initially achieving effectiveness.</p> <p><strong>Crossponder Authors:</strong></p> <ul> <li>Fatemeh Salehi (email: <a rel="noreferrer">fatemeh.salehihafshejni@fau.de</a>)</li> </ul>
AlphaFold2 predicted structures of ThsA and ThsB proteins
<p>This Zenodo record contains the AlphaFold2 models described in the manuscript: Structural characterization of macro domain-containing Thoeris antiphage defense systems</p>
Accurate prediction of the solid-state region of the Ni-Al phase diagram including configurational and vibrational entropy and magnetic effects
<p>Documentation for the Dataset used in the publication entitled "Accurate prediction of the solid-state region of the Ni-Al phase diagram including configurational and vibrational entropy and magnetic effects" <br>** These datasets comprise all configurations uesd in Ni-Al system and their formation enthalpies at different temperatures, where fcc Al and fcc Ni were used as reference state. **<br>** More details about the methodology can be found in the paper "Wei Shao, José Manuel Guevara-Vela, Antonio Fernández-Caballero, Sha Liu, Javier LLorca, Accurate prediction of the solid-state region of the Ni-Al phase diagram including configurational and vibrational entropy and magnetic effects, Acta Materialia, 2023"**</p> <p>1. bcc-Ni-Al.zip<br>- Description: bcc-Ni-Al.zip is a compressed folder. It contains Al1-xNix configurations with bcc lattice used to fit the cluster expansion (CE). Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>2. bcc-with-vacancies-Ni-Al.zip<br>- Description: bcc-with-vacancies-Ni-Al.zip is a compressed folder. It contains (AlVa)x(AlNi)1-x configurations with bcc-with-vacancies lattice used to fit the CE. Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>3. fcc-Ni-Al.zip<br>- Description: fcc-Ni-Al.zip is a compressed folder. It contains Al1-xNix configurations with fcc lattice used to fit the CE. Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>4. Formation enthalpies of bcc-Ni-Al.xlsx<br>- Description: Formation enthalpies of bcc lattice in Ni-Al system at different temperatures, which includes the effect of lattice vibration. The fcc Al and fcc Ni were used as reference states.</p> <p>- Variable description by columns:<br> 1-(Folder name) - type: numerical (integer)<br> Description: Each folder name in the bcc-Ni-Al.zip corresponds to a configuration<br> 2- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 3- (H_f^(conf)(DFT) (eV/atom) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by density functional theory (DFT).<br> 4- (H_f^(conf)(CE)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K fitted by CE. <br> 6- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 7- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K calculated by DFT and bond length vs. bond stiffness relationship (L-S).<br> 8- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K fitted by CE. <br> 10- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 11- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K calculated by DFT and L-S.<br> 12- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K fitted by CE. <br> 14- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 15- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K calculated by DFT and L-S.<br> 16- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K fitted by CE.<br> 18- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 19- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K calculated by DFT and L-S.<br> 20- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K fitted by CE.</p> <p><br>5. Formation enthalpies of bcc-with-vacancies-Ni-Al.xlsx<br>- Description: Formation enthalpies of bcc lattice with vacancies in Ni-Al system at different temperatures, which includes the effect of lattice vibration and magnetism. The fcc Al and fcc Ni were used as reference states.</p> <p>- Variable description by columns:<br> 1-(Folder name) - type: numerical (integer)<br> Description: Each folder name in the bcc-with-vacancies-Ni-Al.zip corresponds to a configuration.<br> 2- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 3- (H_f^(conf)(DFT) (eV/atom) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by DFT.<br> 4- (H_f^(conf)(CE)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K fitted by CE. <br> 6- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 7- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K calculated by DFT and L-S.<br> 8- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K fitted by CE. <br> 10- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 11- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K calculated by DFT and L-S.<br> 12- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K fitted by CE. <br> 14- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 15- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K calculated by DFT and L-S.<br> 16- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K fitted by CE.<br> 18- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 19- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K calculated by DFT and L-S.<br> 20- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K fitted by CE.</p> <p><br>6. Formation enthalpies of fcc-Ni-Al.xlsx<br>- Description: Formation enthalpies of fcc lattice in Ni-Al system at different temperatures, which includes the effect of lattice vibration. The fcc Al and fcc Ni were used as reference states.<br>- Variable descriptions by columns are the same as those of Formation enthalpies of bcc-Ni-Al.xlsx.</p> <p><br>7. ECIs of bcc-Ni-Al at different temperatures.txt<br>- Description: ECIs of bcc lattice in Ni-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>8. ECIs of bcc-with-vacancies-Ni-Al at different temperatures.txt<br>- Description: ECIs of bcc lattice with vacancies in Ni-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>9. ECIs of fcc-Ni-Al at different temperatures.txt<br>- Description: ECIs of hcp lattice in Ni-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>10. Clusters of bcc-Ni-Al.txt<br>- Description: Cluster information of bcc lattice in Ni-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>11. Clusters of bcc-with-vacancies-Ni-Al.txt<br>- Description: Cluster information of bcc lattice with vacancies in Ni-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>12. Clusters of fcc-Ni-Al.txt<br>- Description: Cluster information of fcc lattice in Ni-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p>
Shear Sonic Prediction Using Supervised Machine Learning: Case Study Talang Akar Formation
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Predicting critical transitions with surrogate data-based machine learning: Project
<p>This project accompanies the Github repository <a href="https://github.com/ZhiqinMa/surrogate_data_based_machine_learning">https://github.com/ZhiqinMa/surrogate_data_based_machine_learning</a>. It contains the model time series data that are used to train the machine learning algorithms, as well as all the process and result files. Please extract all <code>.rar</code> files to <code>G:/surrogate_data_based_machine_learning_v4.0.0</code> folder to run this project.</p>
Data from: Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes
<div> <div> <div> <p><strong>Background</strong>: Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data.</p> <p><strong>Methods</strong>: We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts.</p> <p><strong>Results</strong>: PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range=11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (OR=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 80th percentile of the PRS- CS distribution had significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P=2.4×10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC=0.839 vs. AUC=0.895, P=6.8×10-9).</p> <p><strong>Conclusions</strong>: Genome-wide PRS has potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.</p> <p><strong>Citation</strong>: Nakase T, Guerra GA, Ostrom QT, et al. Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes. <em>Neuro-Oncology</em>. Published online June 25, 2024:noae112. doi:10.1093/neuonc/noae112</p> </div> </div> </div>
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.