Graphen Artificial Intelligence
AI tool for the future precision medicine and precision health.
With the development of sequencing technology in recent years, sequencing capability extends from a few interested points to whole genome/exome-wide sequencing. The discovered mutation space, started with the Human Genome Project, grow fast than ever, therefore identify all polymorphisms and mutations in genome/exome sequence remains a crucial but first-of-all step. Graphen’s system of mutation identification utilize promising procedures to rapidly identify existed mutations from direct NGS output data, but also remains the flexibility to extend, modify, and customize the reference resources and output contents, laying the foundation of all the mutation intelligence capabilities.
In: Direct NGS output data / Out: aligned and mutation/polymorphisms annotated files
Medical literature contains massive information and abundant hidden details in the previous in-field developments, and comprehending a single literature required lot of time and energy to traced back through the previous publishes. Using Natural Language Processing (NLP) technology combined with graph technology, we can comprehensively understand not only the knowledge and intends within the literature, but also able to generate results base on the information within the entire citation network, provide precisive, expert-like summarizations.
In: Medical literature / Out: content summarization and key contents related to the subject
Protein-protein interactions play a key role in performing cellular functions. However, proteins that suffer mutations may cause their functional disorders. The function prediction model can provide protein-protein interaction is loss-of-function or gain-of-function after protein mutated.
In: Protein Complex PDB file / Out: Mutation PDB file
