Building an Explainable AI future to accelerate drug discovery and development.
Building an Explainable AI future to accelerate drug discovery and development.
NExTNet is building an Explainable AI platform that empowers biologists and bench scientists to ask and answer complex questions without mastering coding, querying languages, or arcane statistics.
Our mission is to do for biomedicine what 'Microsoft's Operating System (OS)' did for personal computing
Sapiens
SAPIENS
Semantically connecting disjointed data
Our state-of-the-art NLP technology transforms non-human readable: unstructured or semi-structured data into a human-understandable Ontology, i.e., understandable to a human user. So instead of sifting through tables of rows and columns, or complex sequencing reads, users explore real-world objects that represent these bio-entities (cells, genes, proteins, disease, pathways etc.) and the relationship between them, that those tables and rows actually represent.
Data Integration and Analysis is the seminal problem
Biological data is messy, disjointed, and complex.
Non-uniform nomenclature of the bio-terms makes things even more confusing e.g., genes and proteins have different aliases and IDs from different data sources, different ontological associations, transcript differences.
Such heterogenous data don't share a common language (Ontology) to talk to each other.
Add to this, the explosion of biomedical data (>10,000 biomedical papers published in English daily; 10Million GBs of molecular data, e.g., sequencing and expression, available per researcher up from 100GB per researcher in 2000 etc.).
The ability to analyze such disjointed data in a user-friendly manner has exponentially fallen behind.
To solve this, we are building the foundational software platform to enable bench scientists to search, analyze and interact with their data as frictionlessly as possible.