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`PANDORA` is a powerful, flexible, open-source and easy to use Data Science Knowledge Discovery software.
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Currently `PANDORA` implements Machine Learning and many other statistical data discovery features ([Hierarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering), [Correlation](https://en.wikipedia.org/wiki/Correlation_and_dependence), [PCA Analysis](https://en.wikipedia.org/wiki/Principal_component_analysis), [UMAP](https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction), [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) and others) that will help you to illustrate dynamic relationships and provide you with a structural sense of your data.
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## Why is this so cool?
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-:file_folder:**feature discovery** you can discover relevant trends and patterns inside your data with ease, that would usually take years of manual handcrafting
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-:chart:**machine learning** Build Machine Learning models with ease, and quickly compare them via our innovative interface
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-:package:**exploratory data analysis** visual analysis of multiple different machine learning results will give you instant insights with help of many different visualization algorithms
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-:arrows_counterclockwise:**sharing is caring** you can share your results with others, deploy your models instantly\*\*(in progress)\_ or download your data for external use
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-:closed_lock_with_key:**privacy and security** hosting `PANDORA` on your own dedicated servers or laptop you don't have to worry about someone else is looking after your data and your models
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## Current version features
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`PANDORA` is a modular open-source software that facilitates data analysis and knowledge discovery in biology and medicine. It is designed to empower non-technical and technical researchers to identify crucial patterns in biomedical data by providing an easy-to-use graphical user interface and standardized pipelines. It offers more than 200 machine-learning algorithms to choose from, including hierarchical clustering, correlation, PCA analysis, UMAP, t-SNE, and others. The software also features a drag-and-drop user interface, support for high-sparsity data, local and cloud data storage, built-in data preprocessing, and a variety of visualization algorithms for exploratory data analysis.
-**200+** machine learning algorithms to <ahref="https://topepo.github.io/caret/available-models.html"target="_blank">choose from</a>
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- nicely designed **drag&drop** user interface to easily apply _data modeling techniques_
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- supports **high sparsity** data via data imputation or <ahref="https://cran.r-project.org/web/packages/mulset/index.html"target="_blank"title="Multiset Intersection Generator">mulset</a>
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- supports **local** and **cloud** backend data storage
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- compare all **model performance measures** in one place
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- visual **data analysis** that supports _clustering_ and _correlation graphs_
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- visual **feature analysis** with dot-plots that supports **280 visual styles**
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- visual **model performance comparison** and **model insights**
- public dataset repository import to **easily import** and **analyze** already published data\*_(in progress)_
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- integrated **SAM (Significance Analysis of Microarrays)** technique for finding significant genes in a set of microarray experiments
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-**multi-language** localization support
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-**model & data export** take your ML models and other performed analysis, reproducibility code and associated data with you on the go\*_(in progress)_
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---
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## Installation Quick-start
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### Easy
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This section describes the process of pulling a pre-built version of `PANDORA` from [DockerHub](https://hub.docker.com/).
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If you are beginner or you wish to quickly check it out this is recommended way to start `PANDORA`. This can also be very handy for developers for development without polluting the host machine.
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The `PANDORA` source code includes a [Dockerfile](https://github.com/genular/pandora-backend/blob/master/documentation/docker_images/Dockerfile).
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`PANDORA` can be easily installed using Docker, a pre-built version of the software can be pulled from [DockerHub](https://hub.docker.com/). In order to run a test instance of PANDORA, users will first need to prepare their local environment by downloading, installing and configuring [Docker](https://www.docker.com/).
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#### Requirements
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##### Software:
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- Windows, Linux or MacOS
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-[Docker](https://docs.docker.com/engine/installation/) (`version 17.05` or later is required)
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##### Minimum hardware recommendation:
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##### Minimum suggested hardware recommendation:
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- 64GB RAM
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- 8 CPU Cores / 16 threads with 3.60 GHz base frequency
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#### Running PANDORA Docker Container
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In order to run a test instance of `PANDORA` we first need to prepare the local environment.
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1. Download and install [Docker](https://docker.com). When you finished installing [Docker](https://docs.docker.com/engine/installation/) please _continue_ to steps below.
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2. Lets download and run the `genular/pandora` image from DockerHub:
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#### Running PANDORA
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- Please **open** your favorite **Terminal** and **run** the **command below**. If on Windows - open `Windows Power Shell` => _Click Start, type PowerShell, and then click Windows PowerShel_
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> _If you wish to get correct time inside PANDORA, replace TZ=<timzone> variable with your timezone. You can find list of supported timezones [here](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones)_
3. Once command is executed and the `PANDORA`is downloaded and started you can access it on `http://localhost:3010`via your web favorite browser _(we recommend [Firefox](https://www.mozilla.org/en-GB/firefox/new/))_ and create your administrator account.
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`PANDORA`will be downloaded and started, and it can be accessed via a web browser at [http://localhost:3010](http://localhost:3010)
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- If you get asked, please _allow connections_ through your _Windows Firewall_.
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#### Reinstalling PANDORA
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In order to re-install `PANDORA` you need to delete previously installed `PANDORA` Docker container and associate data/volumes. More info about that on ,<ahref ="https://docs.docker.com/config/pruning/"target="_blank">official Docker documentation.</a> Be sure to stop currently running container (if any). To delete all Docker Images, Containers, Volumes, and Networks execute following:
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To reinstall PANDORA, users will need to delete the previously installed PANDORA Docker container and associated data/volumes by stopping the currently running container:
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```bash
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docker stop genular
@@ -118,19 +82,6 @@ If you need any help, please use [community forums](https://community.genular.or
| Localization | Help us translate `PANDORA` into your language. If some translation is missing or incorrect you can easily help us by correcting it. |[Join our Translation Community](https://crowdin.com/project/genular)|
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| Tutorials | Help others use and understand `PANDORA`| Write a tutorial or record it, with usage examples |
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| Organizing | Ask clarifying questions on recently opened GitHub issues to move the discussion forward |[Here](https://github.com/genular/pandora/issues)|
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| Write article | Help others understand what is Machine Learning & how can they apply it, by publishing blog post |[e-mail us](mailto:info@genular.com)|
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### Reaching Out
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If you'd like to start a conversation feel free to [e-mail us](mailto:info@genular.com).
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