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13,064 results for “predictions”
Interpretable prediction for anticancer sensitivity of glycoside amides
<p><a href="https://anti-cancer.eu/" target="_blank" rel="noopener">Development Timeline: Selective Anticancer Logic of Glycoside Amides</a></p> <p>/ Second supplemented edition /</p> <h2>📊 Interpretable Prediction Dataset:</h2> <h3>Transparent Modeling of Anticancer Sensitivity to Glycoside Amides</h3> <p>The current dataset presents the <strong>exact results</strong> of our interpretable prediction model for anticancer sensitivity to glycoside amides. It is provided in *.xlsx format and includes:</p> <ul> <li> <p>✅ Analytical data</p> </li> <li> <p>✅ Theoretical framework</p> </li> <li> <p>✅ Authorial conclusions</p> </li> <li> <p>✅ Full filtered dataset</p> </li> <li> <p>✅ Complete raw data</p> </li> </ul> <p>All information is organized in a <strong>user-friendly structure</strong>, fully compatible with standard data export formats and ready for integration into clinical modeling, pharmaceutical analysis, or transcriptomic mapping.</p> <div> </div> <h3>🔍 Transparency and Scientific Integrity</h3> <p>This dataset is not a closed interpretation. It reflects <strong>our original research findings</strong>, derived from a specific theoretical and biochemical framework. We fully acknowledge that the data may be interpreted differently depending on the analytical model, clinical context, or pharmacological assumptions.</p> <p>That is precisely why we have chosen to publish the <strong>exact numerical calculations</strong>—not just summaries or visualizations. This decision underscores our commitment to <strong>transparency</strong>, <strong>scientific reproducibility</strong>, and <strong>open dialogue</strong> with the broader research community.</p> <div> </div> <h3>🧠 A Platform for Collaboration</h3> <p>We invite clinicians, researchers, and data scientists to explore the dataset, challenge its assumptions, and build upon its structure. Whether used for comparative modeling, transcriptomic validation, or therapeutic design, the data is intended to serve as a <strong>foundation for further inquiry</strong>, not a final verdict.</p>
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
Alphafold2_ab_initio iterative predictions for folding intermediate identification
<p>PDB ids starts from 1 and 8, rmsds, plddts, t-sne embeddings.</p> <p>Check related biorxiv preprint: AlphaFold2 knows some protein folding principles; DOI: https://doi.org/10.1101/2024.08.25.609581.</p>
RDF version of the data from Choi, JS. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources (2018)
<p>This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1038/s41598-018-24483-z">https://doi.org/10.1038/s41598-018-24483-z</a></p> <p>The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun</p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)
<p>This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. <em>Nanomaterials</em> <strong>2020</strong>, <em>10</em>, 2017.</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a></p> <p>The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.</p>
Data from: External validation of prognostic and predictive gene signatures in 1097 European head and neck squamous cell carcinoma patients
<p><span>Anonymized data containing survival endpoints and gene signature scores for head and neck cancer patients.</span></p> <p><span>File <strong>data_os_gs.csv</strong> : data linking overall survival and gene signature scores</span></p> <p><span>File <strong>data_dfs_gs.csv</strong> : data linking disease-free survival and gene signature scores</span></p> <p><span><strong>Variables</strong>:</span></p> <ul> <li><span><em>supertreat_id</em>: patient ID</span></li> <li><span><em>GS_score_172GS</em>: gene signature score for the <em>172-GS</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_3clustersHPV</em>: gene signature score for the <em>3 clusters HPV</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_RSI</em>: gene signature score for the <em>radiosenstivity index (RSI) </em>signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_pancancerCisplatin</em>: gene signature score for the <em>pancancer-cisplatin</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_cl3Hypoxia</em>: gene signature score for the <em>Cl3-hypoxia</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span>Variables only available in <strong>data_os_gs.csv: </strong></span> <ul> <li><span><em>overall_survival_days_2years</em>: Overall survival censored at 2 years since diagnosis. Number of days from diagnosis to death or censoring.</span></li> <li><span><em>overall_survival_days_5years</em>: Overall survival censored at 5 years since diagnosis. Number of days from diagnosis to death or censoring.</span></li> <li><span><em>overall_survival_status_2years</em>: Overall survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if dead. </span></li> <li><span><em>overall_survival_status_5years</em>: Overall survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if dead. </span></li> </ul> </li> </ul> <ul> <li><span>Variables only available in <strong>data_dfs_gs.csv:</strong></span> <ul> <li><span><em>disease_free_survival_days_2years</em>: Disease-free survival censored at 2 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</span></li> <li><span><em>disease_free_survival_days_5years</em>: Disease-free survival censored at 5 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</span></li> <li><span><em>disease_free_survival_status_2years</em>: Disease-free survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </span></li> <li><span><em>disease_free_survival_status_5years</em>: Disease-free survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </span></li> </ul> </li> </ul>
Dataset - Papyrus 2024 - A large scale curated dataset aimed at bioactivity predictions
<p><strong>This update of release 2024.1 fixes the following:</strong></p> <ul> <li>Metadata in the columns <em>type_IC50</em>, <em>type_EC50</em>, <em>type_KD</em>, <em>type_Ki</em>, and <em>type_other</em> did not contain multiple values when multiple pChEMBL values where available but reported only a single value. This fix ensures all values are reported.</li> <li>Molecules were incorrectly standardized and mixtures were included in the dataset. Standardization (using the <a href="https://github.com/OlivierBeq/papyrus_structure_pipeline" target="_blank" rel="noopener">papyrus_structure_pipeline</a>) is now correctly enforced and mixtures have been removed.</li> </ul> <p><strong>Changes since version 05.6</strong></p> <ul> <li>ChEMBL data was updated to ChEMBL version 34</li> <li>data from the IUPHAR/BPS Guide to PHARMACOLOGY has been included</li> <li>data from Pickett et al.'s publication on MMP-12 has been included (<a href="https://doi.org/10.1021/ml100191f">ACS Med Chem Lett. 2011 Jan 13; 2(1): 28–33. DOI: 10.1021/ml100191f</a>)</li> </ul> <p><strong>Papyrus++:</strong></p> <p>Previous versions mistakenly considered a deviation of 2 log units around compound-target pairs to determine the reproducibility of assays (see published article for more details). This has been fixed to 0.5 log units to ensure data points fall within a maximum range of 1 log unit. As a result, the number of entries in the Papyrus++ set from this release has drastically reduced compared to previous releases.</p>
Spatial predictions of the morpho-ecological state of Finnish palsa mires
<p>Spatial predictions of the probability of a good morpho-ecological state are provided for Finnish palsa mires as a TIFF file with a 10 m resolution, using the EUREF FIN TM35FIN coordinate system.</p> <p>These predictions were produced through spatial modeling that combined classified point data on the state of Finnish palsa mires (Ruuhijärvi et al., 2022) with high-resolution (10 m) environmental datasets. The predictions were computed for the extent of palsa mires (Tammilehto et al., 2024). Modelling was conducted in mgcv package (version 1.9.0; Wood, 2011) in R (version 4.3.2; R Core Team 2023). The predictions were developed during the preparation of the manuscript: <em>"The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling"</em> (Leppiniemi et al., 2024, in-review).</p> <p> </p> <p>References:</p> <p>Leppiniemi, O., Karjalainen, O., Aalto, J., Yletyinen., E., Luoto, M., & Hjort, J. 2024. The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling. (In-review).</p> <p>R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (accessed 14 October 2024).</p> <p>Ruuhijärvi, R., Salminen, P., & Tuominen, S., 2022. Distribution range, morphological types, and state of palsa mires in Finland in the 2010s. <em>Suo</em> 73, 1–32. (In Finnish with English summary).</p> <p>Tammilehto, A., Härmä, P., Kallio, M., Törmä, M., Saikkonen, A., Tuominen, S., Impiö, M., Heikkinen, M., Kervinen, M., Jussila, T., Böttcher, K., Pääkkö, E., Kokko, A., Mäkelä, K., & Anttila, S., 2024. Ylä-Lapin luonnon kaukokartoitus – Projektin loppuraportti osa 1 – Aineistot ja menetelmät. Vantaa. (In Finnish).</p> <p>Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Series B Stat. Methodol. 73, 3–36. https://doi.org/10.1111/J.1467-9868.2010.00749.X</p> <p> </p>
PhasAGE Training School 1 - Computational prediction and databases of protein phase separation - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Computational prediction and databases of protein phase separation Overview- LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1-Protein aggregation prediction-PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Linear motifs identification and prediction - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Computational prediction of intrinsic disorder in proteins-DisProt - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Computational prediction of intrinsic disorder in proteins-MobiDB - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Data for Predictive Modelling of Laminated Composite Plates
<p>Two different problems, i.e. a low-dimensional (LD) and a high-dimensional (HD) problems are considered. The LD problem has 2 variables for a 4-ply symmetric square composite laminate. Similarly, the HD problem consists of 16 variables for a 32-ply symmetric square composite laminate. The value of <em>h </em>for LD and HD problems is taken as 0.005 and 0.04 respectively.</p> <p>For each problem, three different types of sampling technique, i.e. random sampling (RS), Latin hypercube sampling (LHS) [1] and Hammersley sampling (HS) [2] are adopted. The RS, LHS and HS primarily differ in the uniformity of sample points over the design space such that RS has the least and HS has the maximum uniform distributions of sample points. Based on the recommendations of Jin et al. [3], and Zhao and Xue [4], 72 and 612 sample points are considered in each training dataset of LD and HD problems respectively.</p> <p>Based on the FE formulation, several high-fidelity datasets for the LD and HD problems are generated, as presented in the Supplementary Material file “Predictive modelling of laminated composite plates.xlsx” in nine sheets that are organized as detailed out in Table 1.</p> <p>References:</p> <p>1. McKay, M. D.; Beckman, R. J.; Conover, W. J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. <em>Technometrics</em>, <strong>2000</strong>, <em>42</em>, 55-61.</p> <p>2. Hammersley, J. M. Monte Carlo methods for solving multivariable problems. <em>Annals of the New York Academy of Sciences</em>, <strong>1960</strong>, <em>86</em>, 844-874.</p> <p>3. Jin, R.; Chen, W.; Simpson, T. W. Comparative studies of metamodelling techniques under multiple modelling criteria. <em>Structural and Multidisciplinary Optimization</em>, <strong>2001</strong>, <em>23</em>, 1-13.</p> <p>4. Zhao, D.; Xue, D. A comparative study of metamodeling methods considering sample quality merits. <em>Structural and Multidisciplinary Optimization</em>, <strong>2010</strong>, <em>42</em>, 923-938.</p>
ZIRFs: zero-inflated random forests for estimating gene regulatory networks from single cell RNA-seq data (assessment of predictive accuracy and VIM stability)
<p>We developed a zero-inflated random forests (ZIRFs) algorithm to produce a metric of connection strength between regulator genes and target genes. This file contains SCENIC results for the aorta and diaphragm tissue data sets from the Tabula Muris Consortium results. SCENIC is a genetic regulatory network analysis published by Aibar et al. (2017). The purpose of the data sets and R source code are described by README files in each directory.</p>
Measurement and prediction of bottom boundary layer hydrodynamics under modulated oscillatory flows
<p>Experimental and numerical model data pertaining to the manuscript "Measurement and prediction of bottom boundary layer<br> hydrodynamics under modulated oscillatory flows" accepted for publication in Coastal Engineering (<a href="https://doi.org/10.1016/j.coastaleng.2021.103954">https://doi.org/10.1016/j.coastaleng.2021.103954</a>)</p> <p>Please read the README.txt file for more information.</p>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
<p>This dataset contains replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". The dataset consists of pickled Pandas DataFrame files, along with training, validation, and (for DB5-Plus) test filename lists for cross-validation, that can be used to develop and evaluate protein interface prediction models. This dataset also contains the externally generated residue-level PSAIA and HH-suite3 features for users' convenience (e.g. raw MSAs and profile HMMs for each protein complex). Our GitHub repository linked in the "Additional notes" metadata section below provides more details on how we parsed through these files to create our cross-validation datasets. The GitHub repository for DIPS-Plus also includes scripts that can be used to impute missing feature values and convert the final "raw" complexes into DGL-compatible graph objects. Since our final DGL graph representation for each complex uses PyTorch tensors in its construction of residue embeddings, the final representation of each complex can easily be adapted to fit the users' needs (e.g. feeding a complex's 2D residue feature tensors into a convolutional neural network).</p>
Dataset related to article "New in silico models to predict in vitro micronucleus induction as marker of genotoxicity"
<p>The .txt file contains the dataset of the in silico model for genotoxicity as induction of micronuclei.</p> <p>The .doc file contains the descriptors of the models and the structural alerts.</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.