Scientific literature survey is fundamental for biotech companies and academic/health industrial organisations carrying out research.
The amount of knowledge available for biotech research is growing continuously. The extraction of information from textual scientific publications into structured databases has a high
demand while it is exceptionally complicated due to its complexity. HubScience offers a unique, hybrid human-machine text mining solution supported by an artificial intelligence-based learning process and fits completely into the traditional research processes, locating and organizing relevant information in previously constructed texts and articles and It accelerates the speed of literature processing by at least ten times. Our solution enables reading and understanding huge amounts of scientific texts, with the help of an artificial intelligence-based learning process and can be taught to reveal hidden connections between separate extracted data, to provide the opportunity to create unique, optimal databases. Key features:
• E·D·T·A – Extreme Deep Text Mining and Analysis, which can be trained for any specifc topic
• Easy to Use Graphical Interface for end users
• Technology that can be easily integrated into any 3rd party systems
• Unique platform oﬀering the advantages of teamwork
• Dual-layer semantic annotation methodology including logical relations