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481 results for “network model”
Machine learning pipeline to train toxicity prediction model of FunTox-Networks
<p>Machine Learning pipeline used to provide toxicity prediction in FunTox-Networks</p> <p>01_DATA # preprocessing and filtering of raw activity data from ChEMBL<br> - Chembl_v25 # latest activity assay data set from ChEMBL (retrieved Nov 2019)<br> - filt_stats.R # Filtering and preparation of raw data<br> - Filtered # output data sets from filt_stats.R<br> - toxicity_direction.csv # table of toxicity measurements and their proportionality to toxicity</p> <p>02_MolDesc # Calculation of molecular descriptors for all compounds within the filtered ChEMBL data set<br> - datastore # files with all compounds and their calculated molecular descriptors based on SMILES<br> - scripts<br> - calc_molDesc.py # calculates for all compounds based on their smiles the molecular descriptors<br> - chemopy-1.1 # used python package for descriptor calculation as decsribed in: https://doi.org/10.1093/bioinformatics/btt105</p> <p>03_Averages # Calculation of moving averages for levels and organisms as required for calculation of Z-scores<br> - datastore # output files with statistics calculated by make_Z.R<br> - scripts<br> -make_Z.R # script to calculate statistics to calculate Z-scores as used by the regression models<br> <br> 04_ZScores # Calculation of Z-scores and preparation of table to fit regression models<br> - datastore # Z-normalized activity data and molecular descriptors in the form as used for fitting regression models<br> - scripts<br> -calc_Ztable.py # based on activity data, molecular descriptors and Z-statistics, the learning data is calculated</p> <p>05_Regression # Performing regression. Preparation of data by removing of outliers based on a linear regression model. Learning of random forest regression models. Validation of learning process by cross validation and tuning of hyperparameters.</p> <p>- datastore # storage of all random forest regression models and average level of Z output value per level and organism (zexp_*.tsv)<br> - scripts<br> - data_preperation.R # set up of regression data set, removal of outliers and optional removal of fields and descriptors<br> - Rforest_CV.R # analysis of machine learning by cross validation, importance of regression variables and tuning of hyperparameters (number of trees, split of variables)<br> - Rforest.R # based on analysis of Rforest_CV.R learning of final models</p> <p>rregrs_output<br> # early analysis of regression model performance with the package RRegrs as described in: https://doi.org/10.1186/s13321-015-0094-2</p>
Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks
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Raw datasets for paper "Multi-scale hydraulic graph neural networks for flood modelling"
<p>The repository contains two zip folders for the synthetic and case study datasets (raw_datasets_mesh.zip, raw_datasets_dk15.zip). </p> <p>Each zip folder comprises 4 subfolders (DEM, Geometry, Hydrograph, Simulations), containing the elevation, boundary polygon, discharge hydrograph, and full hydrodynamic results for all simulations.</p> <p>The overview.csv file provides the seeds used for experiment replicability and the runtime of the numerical model on each simulation.</p>
Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
<p>Ocean model salinity data used for the comparison of the ConvLSTM river runoff model and the original E-HYPE based model simulations.</p>
Forecasting model of seasonal dynamics of boll weevil Anthonomus grandis grandis (Coleoptera: Curculionidae) in cotton crops using artificial neural networks
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Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
Supplementary Information for Consonance-emerging Hebbian Learning neural network model predicts discreteness of musical scales and the Natural Just Intonation scale
<p><strong>The following phenomena and features are apparent in music and auditory perception in general: the discreteness of the tones in musical scales</strong> [1]<strong>, the prevalence of the tonal frequency span of one semitone (100 cents) in musical scales across cultures </strong>[1]<strong>, the list of tonal intervals ordered by consonance [2], and the musical performers’ preference of the Natural Just-Intonation scale [3] (A). However, researchers still have no agreement about the causes and the emergence of said phenomena (A). Here we show that the consonance-pattern emerging neural network model introduced in our previous study [4], predicts and yields all the said phenomena (A) with a precision of 1/100<sup>th</sup> of a semitone (1 cent). This precision is beyond the resolution of human hearing </strong>[5], [6], [7]. <strong>Since the Hebbian learning paradigm and harmonicity are the main features of our model, we propose that they are sufficient conditions for any system to yield the said phenomena (A). Therefore, they have a crucial role in processing pitch, consonance, and music perception in general. As a consequence, we additionally propose that the mentioned phenomena (A) are a balanced result of the joint workings of the Hebbian paradigm (nurture and cultural exposure) and harmonicity (auditory physics and biology).</strong></p>
Relativistic electron model in the outer radiation belt using a neural network approach
<p>This dataset includes the models, dataset, and extra figures for the paper titled</p> <p>Relativistic electron model in the outer radiation belt using a neural network approach</p> <p> </p>
[Re] An anatomically constrained neural network model of fear conditioning
<p>The results contained within this archive correspond to the Python re-implementation of the computation model and replication of the classical conditioning experiment described in Armony et al. (1995). The data was generated by running the program using the 14th frequency as the Conditioned Stimulus (CS_IDX = 13) and setting the random seed for the <a href="https://numpy.org/">Numpy</a> library to 3 (NUMPY_SEED = 3).<br> During the pre- and post-conditioning testing phases, the activation values of all the neurons in the model have been recorded in different <a href="https://pandas.pydata.org/">pandas.DataFrames</a>. At the end of the experiment, those DataFrames have been written to disk using the <a href="https://hdfgroup.org/">HDF5</a> file format. It should be noted that although this might have been unnecessary given the size of the final dataset, the file has been further compressed to save on space.</p> <p>The HDF5 file format works similarly to dictionaries in Python, or Maps in other programming languages. That is, the data is organized into tables/arrays each associated with a unique key. In the case of the current dataset, the keys are the name of the different layers in lowercase (i.e.: mgm, mgv, cortex, and amygdala). Then, the array corresponding to each of those key includes the layer's neural activities for all frequencies, and for both the pre- and post-conditioning phases.<br> The columns making up each table are:</p> <ul> <li>The "Frequency" index with values in the range [1-15],</li> <li>One column for storing the activity of each unit ("Unit 1", ..., "Unit N", where N = 3 or N = 8 depending on the layer),</li> <li>The last column, entitled "Phase", contains string representations of the phase during which the activity was recorded (either "Pre-conditioning" or "Post-conditioning").</li> </ul> <p>The data included in the archive can be retrieved and stored in a dictionary for further processing using the following Python script:</p> <pre><code class="language-python">import pandas as pd # DATA_PATH is the absolute path to the file containing the hdf5 formated data data = {k: pd.read_hdf(DATA_PATH, key=k) for k in ['mgm', 'mgv', 'cortex', 'amygdala']}</code></pre> <p> </p>
An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media
<p>This data includes the training and testing dataset for DNN design and the observation data of synthetic example for validation. </p> <p>The code of TNNA-AUS inversion method.</p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
<p>Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.</p>
SAGE Sky Images and model checkpoints for "Let's Unleash the Network Judgement: A Self-supervised Approach for Cloud Image Analysis" paper
<p>This dataset contains:</p> <ul> <li>84912 images collected from our camera (a 6MP Fisheye Sky facing camera with 192 degrees horizontal and vertical angle view). Each image is a JPEG image, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 2048x2048, components 3. Images were recorded on October–December 2021. The dataset is clusterized with images belonging to 20 different clusters/classes. We used PCA_MLE plus SOM to produce such clusterization. Files Cluster_*.zip</li> <li>11 model checkpoints from the training process. Files checkpoint*.pth and log.txt</li> <li>Output feature vectors and their corresponding file names resulting from inference . File features.zip</li> <li>The clusterization produced on <a href="https://github.com/CV-Application/WSISEG-Database ">WSISEG-Database</a>. File README.txt </li> </ul>
Supplementary Material to "Analysis of nationwide groundwater monitoring networks using lumped-parameter models and groundwater"
<p>This repository contains the Supplementary Material to "Analysis of nationwide groundwater monitoring networks using lumped-parameter models and groundwater", submitted to Journal of Hydrology for review.</p> <p> </p>
Chemical Networks and Model Output and for "Evidence of Photochemistry in an Exoplanet Atmosphere"
<p>The volume mixing ratio output of the key sulphur species computed by photochemical models for producing Fig. 1 </p> <p>Synthetic spectra in Fig. 2</p> <p>The photochemical networks used in each model.</p>
Data for: Fishing triggers trophic cascade in terms of variation, not abundance, in an allometric trophic network model
<p>Trophic cascade studies often rely on linear food chains instead of complex food webs and are typically measured as biomass averages, not as biomass variation. We study trophic cascades propagating across a complex food web including a measure of biomass variation in addition to biomass average. We examined whether different fishing strategies induce trophic cascades and whether the cascades differ from each other. We utilized an allometric trophic network (ATN) model to mechanistically study fishing-induced changes in food web dynamics. Different fishing strategies did not trigger traditional, reciprocal trophic cascades, as measured in biomass averages. Instead, fishing triggered a variation cascade that propagated across the food web, including fish, zooplankton and phytoplankton species. In fisheries that removed a large amount of top-predatory and cannibalistic fish, the biomass oscillations started to decrease after fishing was started. In fisheries that mainly targeted large planktivorous fish, the biomass oscillations did not dampen but slightly increased over time. Removing species with specific ecological functions might alter the food web dynamics and potentially affect the ecological resilience of aquatic ecosystems.</p>
Repositiry for the article: "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain Mbebi & Zoran Nikoloski
<p>This is the repository for the manuscript "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain J. Mbebi & Zoran Nikoloski.</p> <p><strong>Organisation</strong></p> <ol> <li>The folder Codes contains the following R scripts with the K-folds cross-validation option to learn the hyperparameters:</li> </ol> <ul> <li>Mixed_L1L21_GRN.R which computes L1L21-solution</li> <li>Mixed_L1L21G_GRN.R which computes L1L21G-solution</li> <li>Mixed_L2L21_GRN.R which computes L2L21-solution</li> <li>Mixed_L2L21G_GRN.R which computes L2L21G-solution</li> <li>L1L21_Dream5_Scerevisiae_example_run.R is an example run using the L1L21-solution with S. cerevisiae data (Network 4 in DREAM5 challenge) All files needed to successfully run "L1L21_Dream5_Scerevisiae_example_run" are locaded in the folder Codes.</li> </ul> <p>2. The folder Figures contains all figures in the manuscript.</p> <p>3. The folder Inferred-networks contains all network objects for each dataset and each inference methods in the comparative analysis.</p> <p><strong>Dependencies and required packages</strong></p> <p>The following packages are required for the contending approaches in the comparative analysis: "devtools", "foreach", "plyr", "glmnet" and "randomForest".</p> <p><strong>GENIE3</strong></p> <p>The GENIE3 package can be installed from: <a href="http://bioconductor.org/packages/release/bioc/html/GENIE3.html">http://bioconductor.org/packages/release/bioc/html/GENIE3.html</a></p> <p><strong>TIGRESS</strong></p> <p>The TIGRESS repository can be obtained from: <a href="https://github.com/jpvert/tigress">https://github.com/jpvert/tigress</a></p> <p><strong>ENNET</strong></p> <p>The ENNET repository can be obtained from: <a href="https://github.com/slawekj/ennet">https://github.com/slawekj/ennet</a></p> <p><strong>PLSNET</strong></p> <p>The Matlab source code of PLSNET can be obtained from: <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17</a></p> <p><strong>PORTIA</strong></p> <p>The PORTIA repository can be obtained from: <a href="https://github.com/AntoinePassemiers/PORTIA">https://github.com/AntoinePassemiers/PORTIA</a></p> <p><strong>D3GRN</strong></p> <p>The Matlab source code of D3GRN can be obtained from: <a href="https://github.com/chenxofhit/D3GRN">https://github.com/chenxofhit/D3GRN</a></p> <p><strong>Fused-LASSO</strong></p> <p>The fused-LASSO repository can be obtained from: <a href="https://github.com/omranian/inference-of-GRN-using-Fused-LASSO">https://github.com/omranian/inference-of-GRN-using-Fused-LASSO</a></p> <p><strong>ANOVerence</strong></p> <p>Because of some technical issues (e.g code's accessibility: <a href="http://www2.bio.ifi.lmu.de/%CB%9Ckueffner/anova.tar.gz">http://www2.bio.ifi.lmu.de/˜kueffner/anova.tar.gz</a>), we were not able to reproduce ANOVerence results and used the inferred network from DREAM5 challenge instead.</p> <p>4. Although the codes here were tested on Fedora 29 (Workstation Edition) using R (version 4.2.2), they can run under any Linux or Windows OS distributions, as long as all the required packages are compatible with the desired R version.</p>
Data from Genome scale metabolic network modelling for metabolic profile predictions
<p>Data used to produce figures 4, 5 and 6 in the paper Genome scale metabolic network modelling for metabolic profile predictions.</p>
Dataset: Modular Impedance Matrix Method for Transient Modeling in Pipe Network Systems
<p>Unsteady flow is an important engineering problem in urban pipe network systems, requiring pressure and flow rate management analyses and reliable drinking water quality maintenance. Efficiently solving the hyperbolic partial differential equation and integrating it with various boundary conditions under the complex layout scenarios of pipe networks is a challenging issue for pipeline modelers. Frequency-domain modeling with a time-domain response was developed as an alternative to the traditional method of characteristics. However, this solution requires a substantial array size for large pipe network systems, significantly affecting applicability in field pipe network systems. This study proposes an innovative transient analysis method, the modular impedance matrix method, to solve the most labor- and cost-intensive computational issues affecting the unsteady flow analysis of large, complicated pipe networks. This method was applied to a field pipe network system and its performance compared to existing approaches. The algorithm of the proposed method fundamentally solved the computational problems associated with other methods, and its modular scheme allowed feasible integration with an analytical formulation that can be tailored to the modeler's preferences. The modular impedance matrix method's strength can be amplified according to the size and complexity of the pipe network system owing to its unique complementary validation capability. </p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
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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.