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34,778 results for “diseases”
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.1.0 - September 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>September 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v2.1.0 - August 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>August 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/August-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.1.0 - August 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>August 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/August-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWLNETS (v2.1.0 - August 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>August 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/August-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.1.0 - September 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>September 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/September-01%2C-2021">here</a>.</li></ul>
Genome-wide meta-analysis of short-tandem repeats for Parkinson's disease risk
<p>Idiopathic Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease and is determined by a combination of genetic and environmental risk factors. The to date largest genome-wide association study (GWAS) on single nucleotide polymorphisms (SNPs) by Nalls at al., 2019, reported 90 SNPs that were independently associated with PD risk. However, common SNPs account only for 16-36% of the total genetic heritability of the disease suggesting that other genetic variants play a role in PD susceptibility. One example of previously understudied genetic variants are short-tandem repeats (STR, also known as microsatellites), i.e., repeating sequence motifs in the human genome of 1-6 nucleotides in length. Thus, in this study, we performed a GWAS on imputed STRs in a large PD case-control dataset (n=4,757), and meta-analyzed these data with those from a previous study of the International PD Genetics Consortium (Bustos et al., 2023) resulting in a total sample size of 43,844 PD cases and controls. Thus, in this study, we performed a GWAS on imputed STRs in a large PD case-control dataset (n=4,757) from the US (the PEG and a GHC-based study) and Denmark (PASIDA).</p>
IMBALANCED MACHINE LEARNING CLASSIFICATION MODELS FOR REMOVAL BIOSIMILAR DRUGS AND INCREASED ACTIVITY IN PATIENTS WITH RHEUMATIC DISEASES
<p>Objective: Predict long-term disease worsening and the removal of biosimilar medication in patients with rheumatic diseases.</p><p>Methodology: Observational, retrospective, and descriptive study. Review of a database of patients with immune-mediated inflammatory rheumatic diseases. Disease worsening and removing biosimilars are imbalanced variables, that require using imbalanced machine learning models selected based on their superior f1-scores and great accuracy. Previously, we selected the most important variables using mutual information tests.</p><p>Results: The best imbalanced machine learning models to predict disease worsening and the removal of the biosimilar obtained f1-scores of 0.52 and 0.63, respectively. Both models are decision trees. In the first one, two important factors are switching of biosimilar and age, and in the second, the relevant variables are optimization and the value of the initial CRP. </p><p>Conclusions: Biosimilar drugs do not always work well for rheumatic diseases. We obtained two imbalanced machine learning models to detect those cases, where the drug should be removed or where the activity of the disease increases from low to high. Our decision trees use variables, such as age or switching, not considered in previous studies.</p>
Figure 1. Landmarks head selection. A. Rhodnius prolixus. B in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 1. Landmarks head selection. A. Rhodnius prolixus. B. Triatoma maculata. Scale bar = 1 mm.
Enhanced mTORC1 signaling and protein synthesis in pathologic alpha-synuclein cellular and animal models of Parkinson's disease
<p>Pathologic alpha-synuclein plays an important role in the pathogenesis of alpha-synucleinopathies such as Parkinson's disease (PD). Disruption of proteostasis is thought to be central to pathologic alpha-synuclein (alpha-syn) toxicity; however, the molecular mechanism of this deregulation is poorly understood. Here we report that pathologic alpha-syn activates the mechanistic target of rapamycin (mTOR) complex 1 (mTORC1) leading to enhanced mRNA translation via binding tuberous sclerosis protein (TSC) 2 and destabilizing the TSC1-TSC2 complex. Genetic and pharmacologic inhibition of mTOR and protein synthesis rescue the dopamine neuron loss, behavioral deficits, and aberrant biochemical signaling in the alpha-syn preformed fibril (PFF) and Drosophila alpha-syn transgenic models of pathologic alpha-syn induced degeneration. Our findings establish a potential molecular mechanism by which pathologic alpha-syn activates mTORC1 leading to enhanced protein synthesis and concomitant neurodegeneration in PD.</p>
Impact of Image Processing Settings for Radiomic Features in Alzheimer's Disease Using 18F-FDG and 11C-PIB PET Scans
<p>Radiomics is an established method for calculating features for computer-aided diagnosis and has been vastly applied to oncological studies. This study aimed to assess the impact of image processing in radiomic features in neuroimaging. Fifteen Alzheimer's disease subjects and 18 healthy individuals underwent [18F]-2-fluoro-2-deoxy-D-glucose (FDG) and 11C-labelled Pittsburgh Compound B (PIB) PET scans. T1-MRI scans were used for cerebellar and grey matter (GM), and white matter (WM) tissue delineation. PET images were registered to MRI (MR space) and transformed to MNI space. All images were normalized to cerebellar uptake (SUVR). All possible combinations of the following settings were considered to extract feature values: (1)tracer: FDG or PIB; (2)space: MR or MNI space; (3)discretization: fixed bin number (BN) of 64, fixed bin sizes (BS) of 0.05 or 0.25; and (4)volume of interest (VOI): GM, WM, or BRAIN (GM+WM). Features that correlated (>0.9) to traditional metrics (average VOI SUVR and volume) in any configuration were removed. Correlation of feature values between configurations, redundancy, and harmonization of feature values were tested. Image processing settings highly affect radiomic feature values and should be carefully taken into consideration during study design and should be properly reported.</p><p> </p><p>The enclosed datasets refer to the work developed at the University Medical Center Groningen and consists of extracted feature values used in the publication.</p>
Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease [Dataset]
<p>This datasets contain the subthalamic local field potentials (LFPs) from 21 Parkinson's disease patients, used in the work "Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease". Subthalamic LFPs are stored in MATLAB format (.mat). Further information on patients can be found in the main article.</p>
Quantitative disease resistance in wild Silene vulgaris to its endemic pathogen Microbotryum silenes-inflatae collection sites
<p>Details presented on the collections sites of <em>Silene vulgaris</em> seeds from natural populations, for use in the study, Quantitative Disease Resistance in wild <em>Silene vulgaris</em> to its Endemic Pathogen <em>Microbotryum silenes-inflatae</em>. In this study the seeds were collected a mixed half and full sibling families (seeds from the same maternal plants) and used to produce plants that were then cloned by vegetative cuttings. The infection rates of these clones, as well as of their F1 offspring, by the fungus Microbotryum silenes-inflatae are also presented.</p>
A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells
<p>Annotated WES VCF file and phenotype file used in Gene burden analysis of Naaldijk et al., "A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells", 2023. </p><p> </p>
Data and code for: Does increasing temperature accentuate disease impacts on fisheries species? A meta-analysis
<p>Rapid warming could drastically alter host-parasite relationships, which is especially important for fisheries crucial to human nutrition and economic livelihoods; yet we lack a synthetic understanding of how warming influences parasite-induced mortality in these systems. We conducted a meta-analysis using 301 effect sizes from 60 empirical papers on harvested aquatic species and determined the relationship between parasite-induced host mortality and temperature and how this relationship was altered by host, parasite and study design traits. Overall, temperature increased parasite-induced host mortality; however, the magnitude and sometimes direction of this relationship varied. Hosts from the order Salmoniformes experienced a greater increase in parasite-induced mortality with temperature than average. Opportunistic parasites were correlated with a greater increase in host mortality with temperature than average, while bacterial parasite-induced mortality was lower than average as temperature increased. Thus, parasites will generally increase host mortality as the environment warms; however, this effect will vary among systems.</p>
Animal disease data complementing the European Union One Health 2022 Zoonoses Report
<p>This dataset contains the mandatory annual data reported for bovine tuberculosis and for bovine and ovine and caprine brucellosis based on Directive 2003/99.</p>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
Modelling data for: Short-course combination treatment for experimental chronic Chagas disease
<p><span>Chagas disease, caused by the protozoan parasite <em>Trypanosoma</em> <em>cruzi</em>, affects millions of people in the Americas and across the world leading to considerable morbidity and mortality. Current treatment options, benznidazole (BNZ) and nifurtimox, offer limited efficacy and often lead to adverse side effects due to long treatment durations. Better treatment options are therefore urgently required. Here we describe a pyrrolopyrimidine series, identified through phenotypic screening, that offers a clear opportunity to improve on current treatments. In vitro cell-based washout assays demonstrate that compounds in the series are incapable of killing all parasites, however, combining these pyrrolopyrimidines with a sub-efficacious dose of BNZ can clear all parasites in vitro after five days. Importantly, these findings were replicated in a clinically predictive<em> in vivo</em> model of chronic Chagas disease, where five days of treatment with the combination was sufficient to prevent parasite relapse. Comprehensive mechanism of action studies, supported by ligand-structure modelling, show that compounds from this pyrrolopyrimidine series inhibit the Q</span><sub><span>i</span></sub><span> active site of <em>T. cruzi</em> cytochrome <em>b</em>, part of the cytochrome <em>bc1</em> complex of the electron transport chain. Knowledge of the molecular target enabled a cascade of assays to be assembled to evaluate selectivity over the human cytochrome <em>b</em> homologue. As a result, a highly selective and efficacious lead compound was identified. The combination of our lead compound with BNZ rapidly clears<em> T. cruzi</em> parasites, both <em>in vitro</em> and <em>in vivo</em>, and shows great potential to overcome key issues associated with currently available treatments. </span></p>
Data from: Ablation of Hoxb3os in mouse polycystic kidney disease exacerbates cystogenesis
<p>Autosomal dominant polycystic kidney disease (ADPKD) is a monogenic disorder characterized by the formation of cysts in the kidney and is primarily caused by mutations in two genes, <em>PKD1</em> and <em>PKD2</em>. In this study, we investigated the role of the long noncoding RNA (lncRNA) <em>Hoxb3os</em> in ADPKD by ablating its expression in the mouse. <em>Hoxb3os</em>-null mice were viable and had grossly normal kidney morphology but displayed activation of mTOR/Akt signaling and subsequent increase in kidney cell proliferation. To determine the role of <em>Hoxb3os</em> in cystogenesis, we crossed the <em>Hoxb3os</em>-null mouse to two orthologous <em>Pkd1</em> mouse models: <em>Pkhd1/Cre</em>;<em>Pkd1<sup>F/F</sup></em> (rapid cyst progression) and <em>Pkd1<sup>RC/RC</sup></em> (slow cyst progression). Ablation of <em>Hoxb3os</em> exacerbated cyst growth in both models. To gain insight into the mechanism whereby <em>Hoxb3os</em> inhibition promotes cystogenesis, we performed proteomic analysis of mTOR/Akt pathway between <em>Pkd1</em> single-knockout (SKO) and <em>Pkd1-Hoxb3os</em> double-knockout (DKO) mice. Compared to SKO, DKO mice presented with enhanced levels of total and phosphorylated Rictor. This was accompanied by increased phosphorylation of Akt<sup>Ser473</sup>, a known mTORC2 effector site. Physiologically, kidneys from DKO mice displayed between 40-50% increase in cell proliferation. Results from this study indicated that ablation of <em>Hoxb3os</em> in mouse PKD exacerbated cystogenesis and dysregulated mTORC2.</p>
Repeat expansions associated with human disease are present in diverse organisms
<p>These data are associated with the <a href="https://doi.org/10.57844/arcadia-e367-8b55">Repeat expansions associated with human disease are present in diverse organisms</a> pub from Arcadia Science. The code associated with production of these data is available on the GitHub repos linked in the above publication. Version 3 is the correct version of this record.</p> <p>koala_repeat_length_outliers.xlsx and full_koala_results.txt are tables containing results of repeat expansion counting from koala population sequencing data. </p> <p>full_results_20230802_foldseekandblast.csv is a table of the hits with sequence or structural similarity to human repeat expansion proteins. </p> <p>foldseek_aa_fasta.zip contains amino acid fasta files for foldseek hits of dRE proteins.</p> <p>All other tables are described in the NCBI_taxid_to_lineage_and_barchart_tree_plotting.ipynb and profile_repeats.ipynb jupyter notebooks <a title="NCBI_taxid_to_lineage_and_barchart_tree_plotting.ipynb" href="https://github.com/Arcadia-Science/2023-repeats-profiling">here</a>.</p> <p> </p>
Articulatory features of plosive consonants for early detection of Parkinson's disease
<p>These datasets contain temporal and spectral features extracted from the Voice Onset Time (VOT) segments of /p/, /t/ and /k/ syllables for a audio database composed of 27 individuals diagnosed with PD and 27 healthy controls. Data are presented both in CSV and Excel formats.</p> <p>These datasets have been created and used in a full computer-aided diagnosis system. All information about the feature extraction process is presented in:</p> <p>David Montaña, Yolanda Campos-Roca, Carlos J. Pérez, A Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson’s disease, Computer Methods and Programs in Biomedicine, Volume 154, 2018, 89-97, ISSN: 0169-2607, DOI: 10.1016/j.cmpb.2017.11.010.</p> <p> </p> <p> </p>
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.