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1,773 results for “predictive modeling”
Embeddings from protein language models predict conservation and variant effects
<p>For this work, we used protein language model representations (embeddings) to predict sequence conservation without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthew Correlation Coefficient – MCC - for ProtT5 embeddings of 0.596±0.006 vs. 0.608±0.006 for ConSeq).</p> <p><strong><em>ConSurf10k</em>- Dataset for the development of ProtT5cons:</strong> The method (ProtT5cons) predicting residue conservation used <em>ConSurf-DB </em>(Ben Chorin et al. 2020). This resource provided sequences and conservation for 89,673 proteins. For all, experimental high-resolution three-dimensional (3D) structures were available in the Protein Data Bank (PDB) (Berman et al. 2000). As standard-of-truth for the conservation prediction, we used the values from ConSurf-DB generated using HMMER (Mistry et al. 2013), CD-HIT (Fu et al. 2012), and MAFFT-LINSi (Katoh and Standley 2013) to align proteins in the PDB (Burley et al. 2019). For proteins from families with over 50 proteins in the resulting MSA, an evolutionary rate at each residue position is computed and used along with the MSA to reconstruct a phylogenetic tree. The ConSurf-DB conservation scores ranged from 1 (most variable) to 9 (most conserved). The PISCES server (Wang and Dunbrack 2003) was used to redundancy reduce the data set such that no pair of proteins had more than 25% pairwise sequence identity. We removed proteins with resolutions >2.5Å, those shorter than 40 residues, and those longer than 10,000 residues. The resulting data set (ConSurf10k) with 10,507 proteins (or domains) was randomly partitioned into training (9,392 sequences), cross-training/validation (555) and test (519) sets.</p> <p>Uploaded data:</p> <ul> <li>ConSuf10k_PDBid_seq_cons.fasta: fasta file with PDBid, sequence and conservation annotation</li> <li>consurf10k_test_ids.txt: txt file with id's of test set</li> <li>consurf10k_train_ids.txt: txt file with id's of train set</li> <li>consurf10k_val_ids.txt: txt file with id's of cross-validation set</li> </ul>
Is this bug severe? A text-cum-graph based model for bug severity prediction
<p>A snapshot of the dataset has been updated. For the time being, we are publishing a snapshot of the dataset where the bugs were reported after 2017.</p> <p>Paper link: <a href="https://arxiv.org/abs/2207.00623">https://arxiv.org/abs/2207.00623</a> (ECML-PKDD 2022)</p> <p>Cite our paper:</p> <p>@InProceedings{10.1007/978-3-031-26422-1_15,<br> author="Hazra, Rima<br> and Dwivedi, Arpit<br> and Mukherjee, Animesh",<br> editor="Amini, Massih-Reza<br> and Canu, St{\'e}phane<br> and Fischer, Asja<br> and Guns, Tias<br> and Kralj Novak, Petra<br> and Tsoumakas, Grigorios",<br> title="Is This Bug Severe? A Text-Cum-Graph Based Model for Bug Severity Prediction",<br> booktitle="Machine Learning and Knowledge Discovery in Databases",<br> year="2023",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="236--252",<br> isbn="978-3-031-26422-1"<br> }</p> <p><strong>*** Please see the new version. (10.5281/zenodo.5554978)</strong></p> <p>There is a total of six files.</p> <ul> <li><strong>bug_descriptions.csv:</strong> This file contains the bug id and its description.</li> <li><strong>bug_comments.csv:</strong> This file contains three columns. The columns are the bug ids, comments and timestamp of the comment.</li> <li><strong>bug_REPORTED_ON_details.csv:</strong> This file contains the bug id and the package name on which the bug is reported</li> <li><strong>affect_dataset.csv: </strong>This file contains the bug id and the affected packages along with the affect timestamp.</li> <li><strong>bug_heat_2019.csv:</strong> This file contains the bug ids and its bug heats crawled in November 2019.</li> <li><strong>bug_heat_2020.csv:</strong> This file contains the bug ids and its bug heats crawled in November 2020.</li> </ul>
Predictive modeling of moonlighting DNA-binding proteins
<p>This repository contains the codes used for the prediction of moonlighting proteins in the paper "Predictive modeling of moonlighting DNA binding proteins".</p> <p>The repository is organized as the following:</p> <p>1. The DNA binding protein identifiers and their features that were used to train the models for the prediction of DNA binding Moonlighting proteins.</p> <p>2. Five feature sets were used to create Catboost models that make predictions. The source code for generating predictions based on all the features and predictions based on particular features is supplied. In addition, the source code for generating maximum and average ensemble predictions has been made available. A detailed explanation is given in README file.</p>
Predicting Survival of Tongue Cancer Patients by Machine Learning Models
<p>This repository contains the dataset used in the paper "Predicting Survival of Tongue Cancer Patients by Machine Learning Models." The dataset contains information on 1712 tongue cancer curative surgery recipients. Each row represents one patient. The meaning of each variable is summarized here:</p> <ul> <li>id: patient identifier</li> <li>gender: patient sex</li> <li>survival: patient survival status at follow-up; 0: survival, 1: death</li> <li>follow_time: length of follow-up period in days</li> <li>part: site of operation</li> <li>stage: tumor stage; 0: very small, no spreading, 1: small, no spreading, 2: some growth, spreading, 3: large, spreading to surrounding tissue or lymph nodes, 4A/4B/4C: larger, metastasis to at least one other organ</li> <li>op: operation status; 1: complete</li> <li>rt: radiation therapy status; 0: not received, 1: received</li> <li>ct: chemotherapy status; 0: not received, 1: received</li> <li>t_stage: tumor size; 1 (small) to 4 (large)</li> <li>n_stage: metastasis to lymph nodes; 0 (no metastasis) to 3 (metastasis to multiple lymph nodes)</li> <li>grade: tumor grade; 1 (no proliferation) to 3 (aggressive proliferation)</li> </ul>
Dataset and structure database for an ML model to predict diffusivity in ZIF variants
<p>This dataset accompanies the publication titled "Data Mining for Predicting Gas Diffusivity in Zeolitic-imidazolate Frameworks (ZIFs)" (DOI: <a href="https://doi.org/10.1039/D2TA02624D">https://doi.org/10.1039/D2TA02624D</a>)</p> <p><a href="https://zenodo.org/api/files/b80f6d07-3bf4-484c-97ac-5d579fb0cc27/ESI_2_dataset.xlsx?versionId=dc4525d0-1c5c-478c-9bef-a156587ad69b">ESI_2_dataset.xlsx</a>: Descriptors for all ZIFs of the publication and simulations output, in the form of diffusivities of gas molecules (He up to iso-butane), in all ZIFs.</p> <p>ZIF_database.zip: ZIP file containing all ZIFs prepared by the authors (as discussed in the publication), through various units replacements, in the SOD topology, in .pdb format.</p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"
<p>This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh.<br> We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor.</p> <p>Each file has its header, describing each column.</p> <p> </p>
Data for Accident Severity Prediction Modelling for Indian Highways Case Study
<p>Accident Data: Road accidents data is of Indian Highways sections Pune-Solapur and Bengal (BAEL) Section. For the Pune-Solapur Section of NH-9, which is located between Km.144/400 and Km. 249/000 in the state of Maharashtra, accident dates from 2013 to 2018. For the Six-Laning of Barwa-Adda-Panagarh Section of NH-2, which includes Panagarh Bypass and is located in the States of Jharkhand and West Bengal Stretch, accident dates from 2015 to 2019 for the stretch between km 398.240 and km 521.120. </p> <p>The data is sorted and analyzed using Random Forest Machine Learning for Accident Severity Prediction Modelling.</p> <p>Acknowledgement: We highly acknowledge the two organizations 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company for making the raw data available.</p> <p>Source: 1. National Highways Authority of India, 2. IL&FS Engineering and Construction Company.</p> <p> </p>
Dataset for Bayesian parametric models for survival prediction in medical applications
<p><strong>Data Source</strong></p> <p>The data for these experiments were derived from these sources:</p> <p>* Hosmer Jr DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modeling of Time-to-Event Data. 2nd ed: John Wiley & Sons; 2008.</p> <p>* Jd K, Prentice R. The statistical analysis of failure time data. New York: John Wiley and Sons; 1980.</p> <p>* Fleming T, Harrington D. Counting Processes and Survival Analysis: John Wiley & Sons; 1991.</p> <p> </p> <p>The raw data was downloaded from web archive.</p> <p>https://web.archive.org/web/20170114043458/http://www.umass.edu/statdata/statdata/data/</p> <p><strong>Contents</strong></p> <p>Each folder contains the original data, a textfile with a description of the data, and the pre-processed version with one-hot encoded variables. An additional YAML file is included with the list of included variables, name of the time and censor variable, name of continuous variables and splitting and partitioning information.</p>
A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model
<p>A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model</p>
Dataset for the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest"
<p>Datasets to accompany the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest". Written by J. Elgy and P. D. Ledger (Keele University, 2023).</p> <p>The datasets include data files, meshes, and source code for generating figures from the paper. This requires the open source MPT-Calculator software available at <a href="https://github.com/MPT-Calculator/MPT-Calculator%7D">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch).</p> <p>The authors gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1</p> <p> </p>
Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials
<p>Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation.</p> <p>Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.</p>
Arthropod pitfall trap biomass captured (weekly) and pitfall biomass model predictions (daily) near Toolik Field Station, Alaska, summers 2012-2016.
This data set contains information about the per pitfall trap arthropod biomass captured (or modeled using GAM modelling approaches) near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It is associated with publication DOI: 10.1111/jav.01712.
MiRoR4_P1_A systematic review describes models for recruitment prediction at the design stage of a clinical trial
<p>This dataset is related to the publication "A systematic review describes models for recruitment prediction at the design stage of a clinical trial".</p>
A Dataset of Pull Requests and A Trained Random Forest Model for predicting Pull Request Acceptance
<p>A Curated Dataset of 470,925 pull requests for 3349 popular NPM packages, description of the variables, code snippet for creating a Random Forest model for predicting pull request acceptance, and a pre-trained Random Forest model (in R). The dataset is for the ESEM-2020 paper: "Impact of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem" (<a href="https://arxiv.org/abs/2007.04816">https://arxiv.org/abs/2007.04816</a>). </p> <p>Citation:</p> <pre>@inproceedings{dey2020effect, title={Effect of technical and social factors on pull request quality for the npm ecosystem}, author={Dey, Tapajit and Mockus, Audris}, booktitle={Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)}, pages={1--11}, year={2020} }</pre>
Dataset for: African manatee (Trichechus senegalensis) habitat suitability at Lake Ossa, Cameroon using trophic state models and predictions of submerged aquatic vegetation
<p>See research article here: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.8202</p> <p>Aim: The present study aims at investigating the past and current trophic status of Lake Ossa and evaluating its potential impact on African manatee health.</p> <p>Location: Lake Ossa is known as a refuge for the threatened African manatees in Cameroon. Little information exists on the water quality and health of the ecosystem as reflected by its chemical and biological characteristics.</p> <p>Methods: Aquatic biotic and abiotic parameters including water clarity, nitrogen, phosphorous and chlorophyll concentrations were measured monthly during four months at each of 18 water sampling stations evenly distributed across the lake. These parameters were then compared with historical values obtained from the literature to examine the dynamic trophic state of Lake Ossa.</p> <p>Results: Results indicate that Lake Ossa’s trophic state parameters doubled in only three decades (from 1985 to 2016), moving from a mesotrophic to a eutrophic state. The decreasing nutrient gradient moving from the mouth of the lake (in the south) to the north indicates that the flow of the adjacent Sanaga River is the primary source of nutrient input. Further analysis suggests that the poor transparency of the lake is not associated with chlorophyll concentrations but rather with the suspended sediments brought-in by the Sanaga River. Consequently, our model demonstrated that despite nutrient enrichment, less than 5% of the lake bottom surface sustained submerged aquatic vegetation. Thus, shoreline emergent vegetation is the primary food available for the local manatee population. During the dry season, water recedes drastically and disconnects from the dominant shoreline emergent vegetation, decreasing accessibility for manatees.</p> <p>Main conclusions: The current study revealed major environmental concerns (eutrophication and sedimentation) that may negatively impact habitat quality for manatees. Efficient land use and water management across the entire watershed may be necessary to mitigate such issues.</p>
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
A Queueing Network Model for Performance Prediction of Apache Cassandra
<p>The dataset consists in several csv files containing Cassandra and ScyllaDB performance.</p> <p>The experiments are organized in folders. There are three main folders containing:<br> - Cassandra 4 nodes: the files related to the Cassandra experiments conducted on a cluster composed of four nodes.<br> - ScyllaDB 4 nodes: The files related to the ScyllaDB experiments conducted on a cluster composed of four nodes. <br> - Cassandra QUORUM variant: the simulation data where a different kind of QUORUM is implemented in Cassandra.<br> <br> "Cassandra 4 nodes" and "ScyllaDB 4 nodes" include some subfolders, each one containing the files of the Consistency Level applied for those experiments. Each experiment is composed by three files (data*.csv) with the data reported by Yahoo! Cloud System Benchmark (YCSB) in the end of the experiment execution. Each folder contains also a sim.csv file with the data gathered from the simulation of the model inside Java Modeling Tool.</p> <p>The data*.csv files are composed by:<br> -Number of threads or clients<br> -Overall Throughput<br> -Number of Read requests<br> -Overall Read Response Time<br> -95 percentile Read Response Time<br> -99 percentile Read Response Time<br> -99.9 percentile Read Response Time<br> <br> Differently, the sim.csv files are composed by:<br> -Number of threads or clients<br> -Overall Throughput<br> -Overall Read Response Time</p>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction
<p>The code, dataset, and model weights are described in the paper "Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction."</p> <p> </p> <p><strong>experiment_results.zip:</strong> Contains generated results that can reproduce the result from the reported paper.</p> <p><strong>benchmark.zip:</strong> Contains docking and affinity input data of the interformer. You can use the source code to make predictions and reproduce the number of the reported paper.</p> <p><strong>checkpoints.zip: </strong>Contains one weight for the Energy and four PoseScore and Affinity models.</p> <p><strong>source_code_1.0.zip:</strong> Contains the initial version of the source code.</p> <p><strong>interformer_train.tar.gz:</strong> Contains prepared training data for interformer. poses/ contains all structure need for training, poses/ligand contains the re-docking poses generated by interformer energy, poses/ligand/rcsb contains the conformation of reference ligand, poses/pocket contains all pocket extract by raw PDB from rcsb, poses/uff contains all ligand conformation minimized using UFF from reference ligand, and train/ contains the training csv.</p> <p><strong>baseline_results.tar.gz:</strong> Contains the predictions from three methods: Interformer, DiffDock, and DeepDock. The results align with the exact numbers reported in the paper. For further details, please refer to the <em>eda/ </em>directory.</p> <p> </p> <p>You can also find the newest version of the source code at <a href="https://github.com/tencent-ailab/Interformer" target="_blank" rel="noopener">https://github.com/tencent-ailab/Interformer</a></p> <p> </p>
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.