Nextnet

What is Nextnet?

Nextnet is a cloud-based platform built for scientists in the life sciences. Its goal is to help you find hidden connections across data sources, develop new hypotheses, and make decisions collaboratively. It offers relational search, conceptual search, and a semantic biomedical knowledge network that uses AI to make sense of biomedical information. Nextnet aims to bring together the world’s biomedical knowledge and make it accessible, letting you run semantic searches, extract key concepts from scientific papers and databases, and use that network to make better-informed choices. It also supports teamwork by letting you tag, annotate, and analyze concepts. Learn more at getnextnet.com.

Who created Nextnet?

Nextnet was developed by a team of scientists and developers who wanted to change how knowledge discovery and decision-making happen in biomedicine. They built a cloud-based tool that helps scientists spot hidden links in data and brainstorm hypotheses together, with semantic and conceptual search capabilities, relational search, and a no-code interface for searching data. By aggregating information from a wide range of biomedical sources, Nextnet helps scientists make better-informed decisions and speed up research and development.

What is Nextnet used for?

• Finding hidden connections across different data sources
• Developing new hypotheses
• Making decisions collaboratively
• Searching semantically using natural language or keywords
• Discovering concepts pulled from scientific literature and molecular databases
• Speeding up unbiased decision-making
• Cutting through clutter in scientific literature
• Tagging and annotating concepts
• Accessing information on pathogens and microbes
• Understanding concepts hierarchically

Who is Nextnet for?

• Scientists
• Researchers
• Biotechnologists

How to use Nextnet

1. Visit the Nextnet platform website.
2. Use the cloud-based tools to uncover hidden connections in your biomedicine and life sciences data.
3. Run semantic searches using everyday language or keywords to explore ideas and concepts, not just papers or authors.
4. Use relational search to generate objective hypotheses from a large knowledge network assembled from multiple sources.
5. Use conceptual search to extract key concepts from scientific literature and molecular databases, mapped onto purpose-built ontologies.
6. Switch between Graph and List views to speed up unbiased decision-making.
7. The AI-powered engine continuously pulls in concepts from a wide range of biomedical data sources.
8. Collaborate with other scientists by tagging and annotating concepts to accelerate discovery and research.