1<!DOCTYPE html><html><head><meta charset="utf-8"/><title>Online neuroscience tools | NeuroSuites</title><meta content="initial-scale=1.0, width=device-width, user-scalable=yes" name="viewport"/><link href="/static/favicons/apple-touch-icon.png" rel="apple-touch-icon" sizes="180x180"/><link href="/static/favicons/favicon-32x32.png" rel="icon" sizes="32x32" type="image/png"/><link href="/static/favicons/favicon-16x16.png" rel="icon" sizes="16x16" type="image/png"/><link href="/static/favicons/manifest.json" rel="manifest"/><link color="#4b0ad0" href="/static/favicons/safari-pinned-tab.svg" rel="mask-icon"/><meta content="#ffffff" name="theme-color"/><meta content="NeuroSuites" property="og:site_name"/><meta content="NeuroSuites" property="og:title"/><meta content="NeuroSuites.com" property="og:url"/><meta content="Online neuroscience tools" property="og:description"/><meta content="/static/neurosuite/img/neurosuites_logo_blank_small.png" itemprop="image" property="og:image"/><meta content="website" property="og:type"/><meta content="1440432930" property="og:updated_time"/><link href="/static/django_cms_base/style.css" rel="stylesheet" type="text/css"/><link href="https://fonts.googleapis.com/css?family=Poppins:300,400,500,600,700" rel="stylesheet" type="text/css"/><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/cookieconsent2/3.0.3/cookieconsent.min.css" rel="stylesheet" type="text/css"/>
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26 27 </head> 28 <body><div id="wrapper"><div id="content"><div id="page"><div class="content-div"><div class="navbar navbar-default" id="navbar-top-neurosuite" role="navigation"><div class="menu_block"><div class="container clearfix" id="menu-neurosuites"><div class="logo pull-left"><a class="link-no-decorated" href="/"><span><img alt="NeuroSuites logo" id="neuroSuite_logo_withText" src="/static/neurosuite/img/neurosuites_logo_withText_small.png"/></span></a> <span id="version-number-menu"><a class="link-no-decorated" href="/#what_is_new" id="version-number-menu-link">v1.3 - 22/06/2020</a></span></div><div class="pull-right"><nav class="navmenu center"><ul><li class=""><a href="/morpho/"><span>Machine learning</span></a></li><li class=""><a href="/micro/"><span>Microscopy images</span></a></li><li class=""><a href="/about/"><span>About NeuroSuites</span></a></li></ul></nav></div></div></div></div></div></div><div id="content-neurosuite"><div class="container" text-align="center"><p style="text-align: center;"><img alt="online neuroscience tools" class="align-center" height="61" src="/media/filer_public/39/7e/397eda79-9913-4494-86f8-0c6ef4dec893/neurosuites_slogan.png" width="692"/></p><div class="carousel-style-standard carousel slide" data-interval="5000" data-ride="carousel" id="js-carousel-id-9194"><ol class="carousel-indicators"><li class="active" data-slide-to="0" data-target="#js-carousel-id-9194"></li><li data-slide-to="1" data-target="#js-carousel-id-9194"></li><li data-slide-to="2" data-target="#js-carousel-id-9194"></li><li data-slide-to="3" data-target="#js-carousel-id-9194"></li><li data-slide-to="4" data-target="#js-carousel-id-9194"></li><li data-slide-to="5" data-target="#js-carousel-id-9194"></li></ol><div class="carousel-inner" role="listbox"><div class="item active"><img alt="" class="center-block" src="/media/filer_public_thumbnails/filer_public/40/80/40807ca4-5583-41b3-8ab8-635b7ec3f05e/net1_hubs_prunning.png__1170x501_q85_crop_subsampling-2_upscale.jpg"/><div class="carousel-caption"><h2><a class="href-no-style" href="/morpho/ml_bayesian_networks/" target="_blank"><span style="color: #000000;">Machine learning (Bayesian Networks)</span></a></h2></div></div><div class="item"><img alt="" class="center-block" src="/media/filer_public_thumbnails/filer_public/d3/0f/d30f73c4-2d3f-4612-a0d0-931363594cee/neuroviewer_demo.png__1170x501_q85_crop_subsampling-2_upscale.jpg"/><div class="carousel-caption"><h2><a class="href-no-style" href="/morpho/" target="_blank">Morphometric analyzer</a></h2></div></div><div class="item"><img alt="" class="center-block" src="/media/filer_public_thumbnails/filer_public/1a/80/1a8077ed-0087-4d49-b48a-4bd8b08d94f7/supervised_classification_brier_score.png__1170x501_q85_crop_subsampling-2_upscale.png"/><div class="carousel-caption"><h3><a class="href-no-style" href="/morpho/ml_supervised_classification/" target="_blank"><span style="color: #000000; margin-top: 100px;">Machine learning (Supervised classification)</span></a></h3></div></div><div class="item"><img alt="" class="center-block" src="/media/filer_public_thumbnails/filer_public/35/06/3506653c-f8f5-4659-9ab1-cedea15c062c/multimap_demo.png__1170x501_q85_crop_subsampling-2_upscale.jpg"/><div class="carousel-caption"><h2><a class="href-no-style" href="/micro/" target="_blank">Microscopy images</a></h2></div></div><div class="item"><img alt="" class="center-block" src="/media/filer_public_thumbnails/filer_public/83/7a/837affb1-a04a-41a4-b945-f998cc359b50/probability_density_functions_plot.png__1170x501_q85_crop_subsampling-2_upscale.png"/><div class="carousel-caption"><h2><span style="color: #000000;"><a class="href-no-style" href="/morpho/" target="_blank">Statistics engine</a></span></h2></div></div><div class="item"><img alt="" class="center-block" src="/media/filer_public_thumbnails/filer_public/75/4e/754ed7b7-2986-4446-b804-522a2f4cceef/bn_joint_dist_cond.png__1170x501_q85_crop_subsampling-2_upscale.jpg"/><div class="carousel-caption"><h3><span style="color: #000000;"><a class="href-no-style" href="/morpho/ml_bayesian_networks/" target="_blank">Graphical models inference</a></span></h3></div></div></div>
28<a class="left carousel-control" data-slide="prev" href="#js-carousel-id-9194" role="button"><span aria-hidden="true" class="fa fa-chevron-left glyphicon glyphicon-chevron-left"></span> <span class="sr-only">Previous</span></a> <a class="right carousel-control" data-slide="next" href="#js-carousel-id-9194" role="button"><span aria-hidden="true" class="fa fa-chevron-right glyphicon glyphicon-chevron-right"></span> <span class="sr-only">Next</span></a></div><div class="container" style="margin-top: 25px;"><div class="row"><div class="col-xs-12 col-sm-4 col-md-4 col-lg-4"><div class="panel panel-default"><div class="panel-heading"><h4><span aria-hidden="true" class="icon glyphicon glyphicon-cloud"></span>  Cloud solution</h4></div><div class="panel-body"><p><big><span style="color: #000000;">Don't worry if your machine has low resources.</span></big></p><p><big><span style="color: #000000;">All the software tools are executed in our server.</span></big></p></div></div></div><div class="col-xs-12 col-sm-4 col-md-4 col-lg-4"><div class="panel panel-default"><div class="panel-heading"><h4><span aria-hidden="true" class="icon glyphicon glyphicon-flash"></span> Multiple neuroscience fields</h4></div><div class="panel-body"><p><span aria-hidden="true" class="icon glyphicon glyphicon-align-left"></span> <span style="color: #000000;"><big> <a href="/morpho">Machine learning</a></big></span></p><p><span aria-hidden="true" class="icon glyphicon glyphicon-picture"></span> <span style="color: #000000;"><big> <a href="/micro">Microscopy images</a></big></span></p></div></div></div><div class="col-xs-12 col-sm-4 col-md-4 col-lg-4"><div class="panel panel-default"><div class="panel-heading"><h4><i class="glyphicon glyphicon-ok"></i> Always improving</h4></div><div class="panel-body"><p><span style="color: #000000;"><big>We are always actively improving and developing new tools for NeuroSuites.</big></span></p><p><span style="color: #000000;"><big>Feel free to <a href="/about#contact">contact</a> us for help or suggestions.</big></span></p></div></div></div></div></div></div><div class="container"><div class="alert alert-info"><h3 style="text-align: center; margin-top: 0px; margin-bottom: 0px; padding-top: 0px; padding-bottom: 0px;"><strong>Latest updates </strong></h3></div><p style="text-align: center;"><big>In our last update we included multi-label classification capacity to <a href="/morpho/ml_supervised_classification/">supervised classification</a> section to train, predict and compare multiple machine learning models for every kind of data set.</big></p><p style="text-align: center;"><big>In our previous update we expanded our neuroscience fields to provide new tools for learning <strong>Gene Regulatory Networks</strong> (GRNs)</big></p><h3 style="text-align: center;"> </h3><p style="text-align: center;"><big>We are proud to introduce you our new set of <strong>machine learning</strong> tools including:</big></p><ul><li style="text-align: center;"><h4 style="text-align: justify;"><big><strong><a href="/morpho/ml_bayesian_networks/">Bayesian Networks</a>:</strong> <small>structures and parameters learning and full visualization and inference</small></big></h4></li><li style="text-align: center;"><h4 style="text-align: justify;"><big><strong><a href="/morpho/ml_supervised_classification">Supervised classification</a>:</strong></big> <big><small>data pre-processing, learning ,inference and evaluation</small></big></h4></li><li style="text-align: center;"><h4 style="text-align: justify;"><big><strong><a href="/morpho/ml_probabilistic_clustering/">Probabilistic clustering</a>:</strong> <small>visualization and inference</small></big></h4></li><li style="text-align: center;"><h4 style="text-align: justify;"><big><strong><a href="/morpho/ml_non_probabilistic_clustering">Non Probabilistic clustering</a>:</strong></big> <big><small>visualization and inference</small></big></h4></li></ul><p style="text-align: justify;"> </p><p style="text-align: center;"><big>Some of these methods like our <a href="https://gitlab.com/mmichiels/fges_parallel_production" target="_blank">FGES-Merge</a> have been specifically designed to infer the structure and parameters of large scales networks like GRNs. Some other methods are not bonded to specific application fields. We also included a large set of methods and tools to work with probabilistic graphical models in a general way, not only for GRN.
28</big></p><p style="text-align: center;">Â </p><div class="row"><div class="col-xs-12 col-sm-4 col-md-4 col-lg-4"><div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title">1. Upload your data set</h3></div><div class="panel-body"><p><big>You can upload <a href="/morpho/select_upload_neurons/" target="_blank">upload your data set</a> of your preferred scientific field.</big></p><img alt="" class="img-responsive" src="/media/filer_public_thumbnails/filer_public/3e/7f/3e7f53a2-bbbf-4818-a2bb-45c90cf11b5f/upload_dataset.png__1170x0_q85_subsampling-2_upscale.jpg"/></div></div></div><div class="col-xs-12 col-sm-4 col-md-4 col-lg-4"><div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title">2. Learn the structure and parameters</h3></div><div class="panel-body"><p><big>Once the data set have been uploaded, go to the "Machine Learning" tab at the left side and click on <a href="/morpho/ml_bayesian_networks/" target="_blank">"Bayesian Networks"</a>.</big></p><p><big>Select the desired features of your data set and click "Continue".</big></p><p><big>Then select the desired algorithm to learn the model structure. Once the structure has been learned you can also learn the continuous parameters of the model.</big></p><img alt="" class="img-responsive" src="/media/filer_public_thumbnails/filer_public/98/a7/98a79b23-3440-4d03-ba13-74e636bb04a8/bn_structure_algorithms.png__11
2870x0_q85_subsampling-2_upscale.jpg"/></div></div></div><div class="col-xs-12 col-sm-4 col-md-4 col-lg-4"><div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title">3. Visualize the model and make inference</h3></div><div class="panel-body"><p><big>View your probabilist graphical model in a full dynamic environment where you can also make inferences.</big></p><p><big>You can also upload your model already created externally to visualize it in our <a href="/morpho/ml_bayesian_networks/" target="_blank">application.</a></big></p><img alt="" class="img-responsive" src="/media/filer_public_thumbnails/filer_public/af/d6/afd648f2-a9da-4409-a1a5-13375eca2b92/grn_2.png__1170x0_q85_subsampling-2_upscale.jpg"/></div></div></div></div></div><div class="container" style="margin-top: 25px; margin-bottom: 100px;"><hr/><div class="container" style="margin-bottom: 25px;"><div class="row"><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><h2><span style="color: #000000;"><strong>What is NeuroSuites?</strong></span></h2><p>Â </p><p><big><span style="color: #000000;">NeuroSuites is an <strong>online platform</strong> to run multiple neuroscience tools in a very <strong>easy</strong> way.</span></big></p><hr/><p><big><span style="color: #000000;"><span>To start using NeuroSuites just click on your preferred category on the tab at the top of the page.</span></span></big></p><hr/><p><big><span style="color: #000000;">It provides you multiple tools to analyze neuroscience data. You will <strong>not need to install anything</strong> to run the tools provided here, everything is online. Furthermore, when you are done, you can <strong>export</strong> your results to your own computer.</span></big></p><p>Â </p></div><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><figure><img alt="Neuron morphometric overview" height="359" src="/media/filer_public_thumbnails/filer_public/15/f6/15f68a2e-b4c5-4d8f-bdd2-f32b54273625/morpho_analyzer_neuron_demo.png__714x516_q85_subsampling-2.jpg" width="500"/><figcaption>Neuron morphometric overview</figcaption></figure></div></div></div><div class="container" style="margin-bottom: 25px;"><div class="row"><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><figure><img alt="Neuromorpho.org search demo" src="/media/filer_public_thumbnails/filer_public/11/a3/11a3d917-c26a-4d0a-b303-88c7a7f2e1ec/neuromorpho_search_demo.png__1010x421_q85_subsampling-2.jpg"/><figcaption>Neuromorpho.org search demo</figcaption></figure></div><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><h2><span style="color: #000000;"><strong>Who can use NeuroSuites?</strong></span></h2><p>Â </p><p><big><span style="color: #000000;"><strong>Everyone</strong> can use NeuroSuites but it is intended to <strong>neuroscientists</strong> or <strong>data scientists</strong> that need to use neuroscience software in a fast and easy way without installing a lot of software in their computers.</span></big></p><p><big><span style="color: #000000;"><strong>No computer science</strong> or programming knowledge are <strong>required</strong>.</span></big></p></div></div></div><div class="container" style="margin-bottom: 25px;"><h2><span style="color: #000000;"><strong>What services are available?</strong></span></h2><p>Â </p><div class="row"><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><h3><a href="/morpho"><span style="color: #000000;"><strong>Machine learning</strong></span></a></h3><p>Â </p><p><big><span style="color: #000000;">We have many available tools , some are focused in analyzing neurons morphology reconstructions and the others are general purpose tools like the statistics engine, supervised classification models, Bayesian networks, etc.</span></big></p><p>Â </p><p><big><span style="color: #000000;">You can <a href="/morpho/select_upload_neurons">upload your own data set</a> or you can <a href="/morpho/select_neuromorpho_neurons">select the neurons in the NeuroMorpho.org</a> database.</span></big></p><p><big><span style="color: #000000;">Then you can use the following tools to analyze your neurons:</span></big></p><h3><span style="color: #000000;">L-Measure - Extract morphological measurements</span></h3><p><big><span style="color: #000000;">This tool allows researchers to extract quantitative morphological measurements from neuronal reconstructions.
28</span></big></p><h3><span style="color: #000000;">NeuroViewer - 3D Neuron reconstruction</span></h3><p><big><span style="color: #000000;">3D Neuron reconstruction visualization package</span></big></p><h3><span style="color: #000000;">Statistics engine</span></h3><p><big><span style="color: #000000;">Descriptive statistics:</span></big><big><span style="color: #000000;">univariate, bivariate and multivariate analysis and visualization.</span></big></p><p><big><span style="color: #000000;">Inferential statistics: confidence intervals, hypothesis testing (one sample t-test, two independent samples t-test).<br/> <br/> Interactive plots with Plotly (histograms, probability density functions, box plots, 2D and 3D scatter plots, Chernoff faces, Radar charts, Parallel coordinates, Andrew curves and much more!), custom options, exporting formats, etc. Everything online. Check out the L-Measure tool in the Morphometric Analyzer to see it in action.</span></big><big><span style="color: #000000;"></span></big></p><h3><span style="color: #000000;">Machine learning models</span></h3><p><big><span style="color: #000000;"><a href="/morpho/ml_bayesian_networks/">Bayesian Networks</a>: structure and parameters learning for continuous and discrete datasets. Full visualization and inference for continuous BNs.</span></big></p><p><big><span style="color: #000000;"><a href="/morpho/ml_probabilistic_clustering/">Probabilistic clustering graphical models</a>: full visualization and inference for continuous models.</span></big></p><p><big><span style="color: #000000;"><a href="/morpho/ml_supervised_classification/">Supervised classification</a>: learning and inference of multiple supervised classification models: k-Nearest neighbors, rule induction (CN2), decision tree, random forest, SVM, neural network, LDA, QDA, logistic regression, Naive Bayes, TAN, bagging meta-classifier, boosting (AdaBoost) meta-classifier, stacking meta-classifier. It also lets to perform classification for a multi-label classification problem using problem transformation approaches like binary relevance, classifier chains, label powerset, or RAKELd. The other approach for this kind of problems is with algorithm adaptation methods like multi-label knn or multi-label SVM.</span></big></p><p><big><span style="color: #000000;"><a href="/morpho/ml_non_probabilistic_clustering">Non probabilistic clustering</a>: Discover groups in the data using multiple non probabilisitc clustering methods models: hierarchical agglomerative clustering, k-means, dbscan, affinity propagation and spectral. after it you can download the data with clusters and visialize them on a PCA with 2 components plot.</span></big></p><h3><span style="color: #000000;">NeuroSTR - Validator, format converter</span></h3><p><big><span style="color: #000000;">NeuroSTR is a neuroanatomy toolbox. It reads and processes three-dimensional neuron reconstructions in the most common file formats and offers a huge set of functions and utilities to work with them.</span></big></p><h3><span style="color: #000000;">3DBasalRM - Repair cut-points in the basal arborization</span></h3><p><big><span style="color: #000000;">Data-driven repairing model that detects cut-points in the basal arborization and then repairs them using a growth model built from complete three-dimensional neuron reconstructions As result a neuron in JSON format is returned.</span></big></p><h3><span style="color: #000000;">GabaClassifier - Interneuron classifier</span></h3><p><span style="color: #000000;"><big>Classifies the given interneuron morphology into one of the 8 possible classes.</big></span></p><h3><span style="color: #000000;">3DspineS - Dendritic spine simulation</span></h3><p><big><span style="color: #000000;">This mathematical approach could provide a useful tool for theoretical predictions on the functional<br/> features of human pyramidal neurons based on the morphology of dendritic spines.</span></big></p><h3><span style="color: #000000;">3DSomaMS - Delimit the neuronal soma</span></h3><p><span style="color: #000000;"><big>This software provides a mathematical definition and an automatic segmentation method to delimit theneuronal soma.</big></span></p><h3><span style="color: #000000;">3DSynapsesSA - Analyze spatial distribution of cortical synapses</span></h3><p><span style="color: #000000;"><big>Process and analyze patterns in the three-dimensional spatialdistribution of cortical synapses.</big></span></p><h3><span style="color: #000000;">Dendrite arborization simulation</span></h3><p><span style="color: #000000;"><big>Generation of synthetic neurons with soma and dendrites.</big></span></p><p> </p></div><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><figure><img alt="Neurons overview" height="248" src="/media/filer_public_thumbnails/filer_public/85/dd/85dd3bd5-ae28-4735-af3c-fad702bbc114/neurons_overview_demo.png__726x451_q85_subsampling-2.jpg" width="400"/><figcaption>Neurons overview</figcaption></figure><figure><img alt="3DBasalRM demo" src="/media/filer_public_thumbnails/filer_public/bd/01/bd01c065-1cc6-499a-a9ba-8105421937f8/3dbasalrm_demo.png__800x346_q85_crop_subsampling-2.jpg"/><figcaption>3DBasalRM demo</figcaption></figure><figure><img alt="statistics engine" src="/media/filer_public_thumbnails/filer_public/83/7
28a/837affb1-a04a-41a4-b945-f998cc359b50/probability_density_functions_plot.png__800x346_q85_crop_subsampling-2.png"/><figcaption>Statistics engine</figcaption></figure><a href="http://neurosuites.com/morpho/ml_bayesian_networks" target="_blank"><figure><img alt="Machine learnig (Bayesian Networks)" src="/media/filer_public_thumbnails/filer_public/af/d6/afd648f2-a9da-4409-a1a5-13375eca2b92/grn_2.png__800x346_q85_crop_subsampling-2.jpg"/><figcaption>Machine learnig (Bayesian Networks)</figcaption></figure></a><figure><img alt="NeuroSTR validator demo" src="/media/filer_public_thumbnails/filer_public/ad/ab/adab96ca-a06c-4ed3-b79f-64452e0e0876/neurostr_validator_demo.png__800x305_q85_crop_subsampling-2.jpg"/><figcaption>NeuroSTR validator demo</figcaption></figure><figure><img alt="3DSomaMS demo" src="/media/filer_public_thumbnails/filer_public/e7/74/e774b6a7-1c90-4c71-8517-5631784da05d/3dsomams_demo3d.png__800x305_q85_crop_subsampling-2.jpg"/><figcaption>3DSomaMS demo</figcaption></figure><figure><img alt="3DSynapsesSA feret demo" src="/media/filer_public_thumbnails/filer_public/35/26/3526be2a-b4f6-4e20-a688-5549334eed3f/3dsynapsessa_demo_feret.png__800x230_q85_crop_subsampling-2_upscale.jpg"/><figcaption>3DSynapsesSA feret demo</figcaption></figure></div></div><div class="container" style="margin-top: 50px; margin-bottom: 25px;"><div class="row"><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><figure><img alt="Multimap demo" src="/media/filer_public_thumbnails/filer_public/ce/d1/ced1f618-6c9d-4942-ab45-c8db203c5fb3/multimap_demo_menu.png__800x377_q85_crop_subsampling-2.jpg"/><figcaption>Multimap demo</figcaption></figure></div><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><h3><a href="/micro"><span style="color: #000000;"><strong>Microscopy images</strong></span></a></h3><h3><a href="/micro/multimap"><span style="color: #000000;">MultiMap</span></a></h3><p><big><span style="color: #000000;">Multimap is an extensible application to create, visualize and analyze spatial data on maps.<br/> Maps can be both geographical and non-geographical maps (for example maps created from biological images).</span></big></p></div></div></div><div class="container" style="margin-top: 50px; margin-bottom: 25px;"><div class="row"><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><h3><span style="color: #000000;"><strong>New tools and updates are coming...</strong></span></h3></div><div class="col-xs-12 col-sm-6 col-md-6 col-lg-6"><h2 style="font-style: italic;">Coming soon!</h2></div></div></div></div><div class="container" id="what_is_new"><h3 style="text-align: center;"><span style="color: #000000;">Release change logs</span></h3><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;"><span aria-hidden="true" class="icon glyphicon glyphicon-bullhorn"></span> What is new in version 1.3 <span class="pull-right" style="color: #000000;">Released: 02/07/2020</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Supervised multi-label classification functionality added</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;"><span aria-hidden="true" class="icon glyphicon glyphicon-bullhorn"></span> What is new in version 1.2 <span class="pull-right" style="color: #000000;">Released: 03/06/2020</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Non probabilistic clustering section added</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;"><span aria-hidden="true" class="icon glyphicon glyphicon-bullhorn"></span> What is new in version 1.1 <span class="pull-right" style="color: #000000;">Released: 02/04/2020</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Supervised classification - data pre-processing section added</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;"><span aria-hidden="true" class="icon glyphicon glyphicon-bullhorn"></span> What is new in version 1.0 <span class="pull-right" style="color: #000000;">
28Released: 12/11/2019</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Supervised classification section added</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.9 <span class="pull-right" style="color: #000000;">Released: 07/23/2019</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Bayesian Networks: beta version released. Structure and parameters learning, full visualization and inference.</span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Probabilistic clustering graphical models: beta version released. Full visualization and inference.</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.8.1 <span class="pull-right" style="color: #000000;">Released: 02/22/2019</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- First basic version of Bayesian Networks visualization tool.</span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Now Apache Parquet files (.parquet.gzip) are supported in the upload dataset page in the morphometric analyzer.</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.8 <span class="pull-right" style="color: #000000;">Released: 10/15/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- New input source: upload any dataset in CSV; Automatically detect discrete and continuous data and analyze it in metadata discrete and continuous stats;</span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- New functionalities in the statistics engine: statistics for discrete data (descriptive and inferential), find automatically the fittest distribution in continuous data</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.7 <span class="pull-right" style="color: #000000;">Released: 08/25/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- New section in statistics engine: inference statistics (confidence intervals, hypothesis testing)Â in L-Measure tool</span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- New plots in descriptive multivariate statistics engine: Radar plots, Andrew Curves in L-Measure tool</span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- New functionality to include the labels of the additional data (i.e. data from NeuroMorpho.org or another sources in the future) of the neurons in the plots in L-Measure tool</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.6 <span class="pull-right" style="color: #000000;">Released: 07/27/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Statistics engine included: descriptive statistics in L-Measure tool</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.5 <span class="pull-right" style="color: #000000;">Released: 07/06/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Tools included: Dendrite arborization simulation</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.4 <span class="pull-right" style="color: #000000;">Released: 06/29/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Tools included: GabaClassifier, 3DSomaMS, 3DSynapsesSA</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.3.1 <span class="pull-right" style="color: #000000;">Released: 06/19/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Miscellaneous</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Page speed optimized</span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">
28- NeuroSuites has now been deployed in our new dedicated private server</span></div></div><div class="container" style="margin-top: 25px; margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.3 <span class="pull-right" style="color: #000000;">Released: 05/31/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- NeuroMorpho API client updated to support multiple filters in the queries.</span></div><p>Â </p><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Miscellaneous</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- First public release with this new domain name.</span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Home page and About page included. Now you can <a href="/about#contact">contact us</a> for help or suggestions!</span></div></div><div class="container" style="margin-bottom: 25px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.2 <span class="pull-right" style="color: #000000;">Private release: 05/10/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Tools included: 3DBasalRM, 3DSpineS.</span></div><p>Â </p><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Microscopy images</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Tools included: MultiMap.</span></div><p>Â </p><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Security updates</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Included TLS protection to support HTTPS.</span></div></div><div class="container" style="margin-bottom: 100px;"><div class="alert alert-success"><h4 style="margin-left: 10px;">Version 0.1 <span class="pull-right" style="color: #000000;">Private release: 03/06/2018</span></h4></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;"><big>Machine learning</big></span></div><div style="background: #eeeeee; border: 1px solid #cccccc; padding: 5px 10px;"><span style="color: #000000;">- Tools included: L-Measure, Neuroviewer, NeuroSTR validator, NeuroSTR format converter.</span></div></div></div></div></div><footer></footer></div>
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