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 biological questions without mastering coding, querying languages, or arcane statistics.
Our mission is to do for biomedicine what Microsoft OS did for personal computing
Sapiens
SAPIENS
DESIGNED TO BE FULLY CUSTOMIZABLE
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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.
Ontology
Ontology
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
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