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1,601 results for “prognosis”
MATLAB codes for : "Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach".
<p>The package contains all the materials needed to reproduce the findings of our paper. The paper is published by MDPI Applied Sciences journal and its details are as follow.</p> <p>Berghout, T.; Benbouzid, M. Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach. <em>Appl. Sci.</em> <strong>2023</strong>, <em>13</em>, 10916. https://doi.org/10.3390/app131910916</p> <p>1) Please you need to download the dataset from original link provided by introductory paper (Please read the above paper to find out about the datset used).<br> 2) Put the data in folders "RawData" for both experments.<br> 3) Please run the files for each experiment as provided, in alphabetical order.</p>
ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'
<p>ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'</p>
Interim data for scoping review on diagnosis, prognosis and treatment of pediatric DoC
<p>These are the data produced by the working group, while evaluating the existing literature on diagnosis, prognosis and treatment of pediatric DoC. Files include the results of the systematic search (3 repetitions), the data input of abstraction forms and QUADAS-2 and PROBAST checklists. A study workflow is also provided.</p>
Combined bioinformatics and machine learning methodologies reveal prognosis-related ceRNA network and propose ABCA8, CAT, and CXCL12 as independent protective factors against osteosarcoma
<p><strong>Supplementary Table 1</strong>. Basic traits of the seven microarray datasets from the Gene Expression Omnibus and The Cancer Genome Atlas.</p><p><strong>Supplementary Table 2</strong>. Basic characteristics of the nine differentially expressed circRNAs</p><p><strong>Supplementary Table 3</strong>. Index of concordance (C-index) and variance inflation factor (VIF) of ABCA8, CXCL12, and CAT.</p><p><strong>Supplementary Table 4.</strong> LASSO and cox analysis of ceRNA with coef/se(coef) < 0.01 and P-value of proportional hazards assumption (PH) > 0.05.</p><p><strong>Supplementary Table 5</strong>. Robust rank aggregation analysis of ABCA8, CXCL12, and CAT. LogFC in the four datasets and RRA score of the three RNAs.</p><p><strong>Supplementary Figure 1.</strong> Competitive endogenous RNA in osteosarcoma.</p><p><strong>Supplementary Figure 2.</strong> Protein–protein interaction (PPI) network of genes in the competitive endogenous RNA network</p><p><strong>Supplementary Figure 3.</strong> Proportional hazards assumption (left) and linearity assumption (right).</p><p><strong>Supplementary Figure 4.</strong> Survival analysis for competitive endogenous RNA in osteosarcoma.</p>
The nature and extent of bomb tritium remaining in deep vadose zones: A synthesis and prognosis
<p>Tritium present in deep vadose zones is a useful tracer for estimating groundwater recharge, but its full utility is constrained by not knowing where and for how long the tritium tracing method remains applicable. We obtained 44 tritium profiles from 17 globally distributed sites with vadose zone thicknesses of 13−624 m and used transport models to estimate the number of years that tritium may still be useful. Results show that the method may still be usable for 26 of 44 soil profiles surveyed, mainly in China, Australia, USA, South Africa, and Senegal, with a remaining useful period of between 6 and 83 years. We also developed a statistical model that uses outputs from a hydrological model to predict the applicability of the tritium tracing method. Global implementation of the statistical model showed that the method remains usable at 20% of Earth's land mass (excluding Antarctica and Greenland) over the next few decades.</p>
Pathformer: a biological pathway informed Transformer for disease diagnosis and prognosis using multi-omics data
<p>Integrating multi-omics data offers a more comprehensive view of gene regulation, which would be helpful in achieving accurate diagnosis of diseases like cancer. To improve the accuracy of disease diagnosis and prognosis, we developed Pathformer, a multi-omics integration method for both tissue and liquid biopsy data. We implemented Pathformer's network architecture using the “PyTorch” package in Python v3.6.9, and our codes can be found in the GitHub repository (https://github.com/lulab/Pathformer). This repository contains preprocessed TCGA dataset data, preprocessedliquid biopsy dataset data, result of Pathformer and comparison_methods, mentioned in GitHub project and article.</p>
Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
Predicting cancer prognosis and drug response from the tumor microbiome
<p>Tumor gene expression is predictive of patient prognosis in some cancers. However, RNA-seq and whole genome sequencing data contain not only reads from host tumor and normal tissue, but also reads from the tumor microbiome, which can be used to infer the microbial abundances in each tumor. Here, we show that tumor microbial abundances, alone or in combination with tumor gene expression data, can predict cancer prognosis and drug response to some extent – microbial abundances are significantly less predictive of prognosis than gene expression, although remarkably, similarly as predictive of drug response, but in mostly different cancer-drug combinations. Thus, it appears possible to leverage existing sequencing technology, or develop new protocols, to obtain more non-redundant information about prognosis and drug response from RNA-seq and whole genome sequencing experiments than could be obtained from tumor gene expression or genomic data alone.</p>
Metabolic syndrome for the prognosis of postoperative complications after open pancreatic surgery in Chinese adult: a propensity score matching study
<p><strong>Background: </strong>To investigate the relationship between metabolic syndrome (MS) and postoperative complications in Chinese adults after open pancreatic surgery.</p> <p><strong>Methods: </strong>Relevant data were retrieved from the Medicalsystem® database of Changhai hospital (MDCH). All patients who underwent pancreatectomy from January 2017 to May 2019 were included, and relevant data were collected and analyzed. A propensity score matching (PSM) and a multivariate generalized estimating equation were used to investigate the association between MS and composite compositions during hospitalization. Cox regression model was employed for survival analysis.</p> <p><strong>Results: </strong>1481 patients were finally eligible for this analysis. According to diagnostic criteria of Chinese MS, 235 patients were defined as MS, and the other 1246 patients were controls. After PSM, no association was found between MS and postoperative composite complications (OR: 0.958, 95%CI: 0.715-1.282, P=0.958). But MS was associated with postoperative acute kidney injury (OR: 1.730, 95%CI: 1.050-2.849, P=0.031). Postoperative AKI was associated with mortality in 30 days and 90 days after surgery (P<0.001).</p> <p><strong>Conclusions: </strong>MS is not an independent risk factor correlated with postoperative composite complications after open pancreatic surgery. But MS is an independent risk factor for postoperative AKI of pancreatic surgery in Chinese population, and AKI is associated with survival after surgery.</p>
Identifying a novel ferroptosis-related prognostic score for predicting prognosis in chronic lymphocytic leukemia
<p><span><strong>Background</strong>:</span><span> Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western world. Although the treatment landscape for CLL is rapidly evolving, there are still some patients who remain drug resistance or disease refractory. Ferroptosis is a type of lipid peroxidation-induced cell death and has been suggested with a prognostic value in several cancers. Our research aims to build a prognostic model to improve risk stratification in CLL patients and facilitate more accurate assessment for clinical management.</span></p> <p><span><strong>Methods</strong>: </span><span>The differentially expressed ferroptosis-related genes (</span><span>FRGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Least Absolute Shrinkage and Selection Operator (LASSO) Cox algorithms were performed to construct a prognostic risk model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were performed to estimate the immune infiltration score and immune-related pathways. A total of thirty-six CLL patients in our center were enrolled in this study as a validation cohort. Moreover, a nomogram model was established to predict the prognosis.</span></p> <p><span><strong>Results</strong>: </span><span>A total of differentially expressed 15 FRGs with prognostic significance were screened out. After minimizing the potential risk of overfitting, we constructed a novel ferroptosis-related prognostic score (FPS) model with nine FRGs (AKR1C3, BECN1, CAV1, CDKN2A, CXCL2, JDP2, SIRT1, SLC1A5 and SP1), and stratified patients into low-risk and high-risk groups. Kaplan–Meier analysis showed that patients with high FPS had worse overall survival (OS) (P<0.0001) and treatment-free survival (TFS) (P<0.0001). ROC curves evaluated the prognostic prediction ability of the FPS model. Additionally, the immune cell types and immune-related pathways were correlated with the risk scores in CLL patients. In the validation cohort, the results confirmed </span><span>that the </span><span>high</span><span>-</span><span>risk group was related to worse OS (P<0.0001), progress-free survival (PFS) (P=0.0140) and TFS (P=0.0072). </span><span>In</span><span> the multivariate analysis, only FPS (P=0.011) and CLL-IPI (P=0.010) were independent risk indicators for OS. Furthermore, we established a nomogram including FPS and CLL-IPI which could strongly and reliably predict individual prognosis.</span></p> <p><span><strong>Conclusion</strong>: </span><span>A novel FPS model could be used in CLL for prognostic prediction. The model index may also facilitate the development of new clinical ferroptosis-targeted therapies in patients with CLL.</span></p>
Dataset from: "Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion"
<p>This dataset comprises run-to-failure sensor data derived from a mathematical model simulating wave energy converter behavior, particularly for Oscillating Water Column Turbines (OWCTs). The dataset includes vibration, temperature, pressure, acceleration, strain, flow, torque, rotation, and remaining useful life (RUL) readings for one life cycle. Through normalization, the data is scaled uniformly for robust analysis and interpretation. Researchers can leverage this dataset to develop and validate their prognostic models for OWCTs.</p> <p>To cite this dataset, please refer to:</p> <p>Berghout Tarek and Benbouzid Mohamed. (2024). Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion. SSRN, 1–22. <span><a href="https://dx.doi.org/10.2139/ssrn.4825408" target="_blank" rel="noopener"><span>http://dx.doi.org/10.2139/ssrn.4825408</span></a> </span></p>
Mutation frequency and copy number alterations determine prognosis and metastatic tropism in 60.000 clinical cancer samples
<p>The intricate interplay between somatic mutations and copy number alterations critically influences tumour evolution and patient prognosis. Traditional genomic studies often overlook this interplay by analysing these two biomarker types in isolation. We developed INCOMMON, a computational method to detect allele-specific copy number alterations from clinical targeted panels without matched normal, discover recurrent tumour-specific patterns of co-existing mutations and copy-number alterations, and stratify patients based on these composite genotypes for downstream analyses of survival, metastatic propensity and organotropism. The tool can be used as an open-source R package available at <a href="https://github.com/caravagnalab/INCOMMON">https://github.com/caravagnalab/INCOMMON</a>, and a shiny application available at <a href="https://ncalonaci.shinyapps.io/incommon/" target="_blank" rel="noopener">https://ncalonaci.shinyapps.io/incommon/</a>. This repository contains all the scripts that we used to analyse PCAWG, TCGA, MSK-MetTropism and AACR GENIE-Dfci data, and all the relevant results in the form of data tables.<strong></strong></p>
Original Data of Paper: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis
<p>Paper title: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis. Our paper was submitted to PeerJ recently. This data file is the original data of this study which contains all the original data involved in this work.</p>
Supplementary materials for "SPAG9 expression predicts a good prognosis in patients with clear cell renal cell carcinoma: A bioinformatics integrative analysis"
<p>Supplementary materials for "SPAG9 expression predicts a good prognosis in patients with clear cell renal cell carcinoma: A bioinformatics integrative analysis".</p>
Lipidomics for diagnosis and prognosis of pulmonary hypertension
<p>Pulmonary hypertension (PH) is associated with high morbidity and mortality with an urgent need for diagnostic and prognostic biomarkers.</p> <p>A training cohort of PH patients, disease controls without PH, and healthy controls was investigated using metabolomics and machine learning. Specific free fatty acid (FFA)/lipid-ratio biomarkers were diagnostic and predictive for PH survival with an area under the curve (AUC) of 0.89. FFA/lipid-ratio performance was independently validated in PH patients from other centers(AUC 0.90). Survival could be predicted in an age-independent manner and a combination with established clinical scores (FPHR4p, COMPERA 2.0) increased the scores hazard risk.</p> <p>Our mechanistic studies in healthy and diseased pulmonary artery endothelial and smooth muscle cells indicate a functional involvement of increased FFA levels in pathophysiology of PH. In conclusion, lipidomic changes in PH can be used as a novel diagnostic and prognostic approach and may help the discovery of new therapeutic targets.</p>
Dataset: Automated quantification of stromal tumour infiltrating lymphocytes is associated with prognosis in breast cancer.
<p>Daraset of breast nulcei segmentation associated to the article "Gonzàlez-Farré, M., Gibert, J., Santiago-Díaz, P. <em>et al.</em> Automated quantification of stromal tumour infiltrating lymphocytes is associated with prognosis in breast cancer. <em>Virchows Arch</em> (2023). https://doi.org/10.1007/s00428-023-03608-4"</p>
Sarcopenia index predicts short-term prognosis of head and neck squamous cell carcinoma
<p>Dataset of "Sarcopenia index predicts short-term prognosis of head and neck squamous cell carcinoma"</p>
Pembrolizumab in Patients With Poor-Prognosis Carcinoma of Unknown Primary Site (CUP)
ClinicalTrials.gov study NCT03391973. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High-dose Chemotherapy for Poor-Prognosis Relapsed Germ-Cell Tumors
ClinicalTrials.gov study NCT00936936. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Neuroimaging Biomarkers of Prognosis in Motor Functional Neurological Disorders
ClinicalTrials.gov study NCT03398070. IPD Sharing: NO. Countries: 1. 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
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.