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Section 6 focuses on the machine learning part of NeuroSuites, including various data preprocessing techniques, available supervised or unsupervised learning algorithms, and Bayesian networks.

In Section 5 we present statistical tools aimed at exploratory data analysis and finding insights from this dataset, both with discrete and continuous variables. Section 4 focuses on the use of applications specifically oriented to neuroscience, where depending on the application, a previous selection of neuron reconstructions by the user may be needed.

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We also describe the services available on the platform and the main workflow when selecting a data source to use for a software tool. Section 3 analyzes how NeuroSuites is implemented by reviewing the main technologies that make up its architecture. This comparison will help us identify the most convenient type of users on our platform. In Section 2, we compare our platform with other currently available software, highlighting its main strengths compared to its competitors. The web access is open and can be consulted from different devices as a result of its responsive web design. In addition, no software installation is required on the computer, since it is designed as a web application, and all algorithms are executed on the backend of the deployed server, rather than requiring local computers with a large computational capacity to run the experiments. However, many other tools have a more generic aim and can also be used by data scientists from other research fields. In NeuroSuites we can find different parts related to a specific tool or set of processing tools, with emphasis on the field of neuroscience, such as morphological reconstructions and visualization of microscopy data. This platform is specifically designed to manage large-scale problems, which are common in some neuroscience fields. This motivated the development of NeuroSuites, 1 a web application platform with numerous tools to help data scientists, and in particular neuroscientists, use specialized software to handle problems in their application domain. In particular, the analysis of neuroscience data can be especially complex, as some of these datasets (such as those from microarrays) can have tens of thousands of variables creating a need to develop powerful and efficient tools to handle the high dimensionality of these problems. There is a vast amount of data available today, and consequently, various software exists that aims to transform data into new knowledge. As future work, we want to further expand the list of available software tools as well as improve the platform interface according to user demands. NeuroSuites has different neuroscience-oriented applications and tools to integrate statistical data analysis and machine learning algorithms commonly used in this field. Thanks to its defined architecture, it is able to handle large-scale problems common in some neuroscience fields. We compare our platform with other software currently available, highlighting its main strengths. In this work we present NeuroSuites, an easy-access web platform with its own architecture. Handling these data is complex and requires the use of efficient tools to transform them into useful knowledge. Nowadays, an enormous amount of high dimensional data is available in the field of neuroscience.

Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain.

José Luis Moreno-Rodríguez *, Pedro Larrañaga and Concha Bielza