Name: Prot?g?
Author: Center for Biomedical Informatics Research

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Protégé, developed by Stanford University, is a free, open-source ontology editor and framework aimed at building intelligent systems. It is used by a wide range of users, including academic, government, and corporate users, in professions such as biomedicine, e-commerce, and organizational modeling.

## Key Features

**Ontology Editor**: According to the vendor, Protégé provides a user-friendly interface for creating, modifying, and managing ontologies. Users can define classes, properties, individuals, and relationships within the ontology.

**Plug-in Architecture**: The vendor states that Protégé offers a flexible plug-in architecture that allows users to customize the tool by adding or creating plug-ins to meet their specific requirements. This enables integration with other tools and problem solvers.

**Support for OWL 2 and RDF**: Protégé fully supports the latest OWL 2 Web Ontology Language and RDF specifications, ensuring compatibility and interoperability with other systems. Users can create ontologies using these standards.

**Extensibility and Rapid Prototyping**: Built on Java, Protégé provides an extensible environment for ontology development. According to the vendor, users can rapidly prototype and develop intelligent systems by leveraging its flexible and customizable framework.

**Collaboration and Sharing**: Protégé supports collaboration and sharing of ontologies among users. It allows for sharing via a server, enabling teamwork within teams or across organizations. Access control and versioning features ensure proper coordination and control over ontology development.

**Community Support**: According to the vendor, Protégé has an active community of users and developers who provide support, documentation, and contribute plug-ins. Users can seek assistance, ask questions, and share experiences through mailing lists and community forums.

**Flexible Application Development**: Protégé serves as a flexible base for developing ontology-based applications. Developers can integrate the output of Protégé with rule systems or other problem solvers to build a wide range of knowledge-based solutions.

KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.

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Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization.  Its Data Science Workspace is a collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle. The Machine Learning Runtime (MLR) provides data scientists and ML practitioners with scalable clusters that include popular frameworks, built-in AutoML and optimizations.

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Prot?g?

Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

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Center for Biomedical Informatics Research

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