Tremendous advances in different areas of knowledge are producing vast volumes of data, a quantity so large that it has made necessary the development of new computational algorithms. Among the algorithms developed, we find Machine Learning models and specific data mining techniques that might be useful for all areas of knowledge. The use of computational tools for data analysis is increasingly required, given the need to extract meaningful information from such large volumes of data. However, there are no free access libraries, modules, or web services that comprise a vast array of analytical techniques in a user-friendly environment for non-specific users. Those that exist raise high usability barriers for those untrained in the field as they usually have specific installation requirements and require in-depth programming knowledge, or may result expensive. As an alternative, we have developed DMAKit, a user-friendly web platform powered by DMAKit-lib, a new library implemented in Python, which facilitates the analysis of data of different kind and origins. Our tool implements a wide array of state-of-the-art data mining and pattern recognition techniques, allowing the user to quickly implement classification, prediction or clustering models, statistical evaluation, and feature analysis of different attributes in diverse datasets without requiring any specific programming knowledge. DMAKit is especially useful for users who have large volumes of data to be analyzed but do not have the informatics, mathematical, or statistical knowledge to implement models. We expect this platform to provide a way to extract information and analyze patterns through data mining techniques for anyone interested in applying them with no specific knowledge required. Particularly, we present several cases of study in the areas of biology, biotechnology, and biomedicine, where we highlight the applicability of our tool to ease the labor of non-specialist users to apply data analysis and pattern recognition techniques. DMAKit is available for non-commercial use as an open-access library, licensed under the GNU General Public License, version GPL 3.0. The web platform is publicly available at https://pesb2.cl/dmakitWeb. Demonstrative and tutorial videos for the web platform are available in https://pesb2.cl/dmakittutorials/. Complete urls for relevant content are listed in the Data Availability section.

在不同知识领域的巨大进步正在产生大量数据，数量如此之大，以至于有必要开发新的计算算法。在开发的算法中，我们发现机器学习模型和特定的数据挖掘技术可能对所有知识领域都有用。鉴于需要从如此大量的数据中提取有意义的信息，越来越需要使用计算工具进行数据分析。但是，在针对非特定用户的用户友好环境中，没有免费的访问库，模块或Web服务包含大量分析技术。对于那些未经培训的人，现有的设备通常会提出特殊的安装要求，并且需要深入的编程知识，这给那些未经培训的人带来了很高的可用性障碍，或可能会导致价格昂贵。作为替代方案，我们开发了DMAKit，这是一个由DMAKit-lib支持的用户友好的Web平台，它是用Python实现的新库，可简化对不同种类和来源的数据的分析。我们的工具实现了各种各样的最新数据挖掘和模式识别技术，使用户可以快速实现分类，预测或聚类模型，统计评估以及对各种数据集中不同属性的特征分析，而无需任何特定的操作编程知识。DMAKit对于需要分析大量数据但又没有信息，数学或统计知识来实现​​模型的用户特别有用。我们希望该平台能够为那些有兴趣在不需要特殊知识的情况下应用数据的人提供一种通过数据挖掘技术来提取信息和分析模式的方法。特别是，我们介绍了生物学，生物技术和生物医学领域的一些研究案例，其中我们着重介绍了该工具的适用性，以减轻非专业用户使用数据分析和模式识别技术的工作。DMAKit可以作为开放访问库用于非商业用途，并已根据GNU 3.0版GNU通用公共许可证获得许可。该网络平台可从https://pesb2.cl/dmakitWeb公开获得。可在https://pesb2.cl/dmakittutorials/中获得有关Web平台的演示视频和教程视频。相关内容的完整网址在“数据可用性”部分列出。