Revolutionizing Multiomics Research with Generative AI.

Cognit rapidly accelerates research in drug discovery and pre-clinical trials in identifying multi-omics biomarkers and fine-mapping diseases.

It is extremely hard for Life Sciences to catch up on the advancements in AI, while also being focused on Biology.

Deep RL and Generative AI in genomics is a new area of science where AI-based in-silico modeling is helping achieve better discovery and prediction, reducing the cost of scientific validations required, and aiding in precision delivery.

Several challenges in genomics and multi-omics systems require cutting-edge Artificial Intelligence, Math, and Physics modeling that helps optimize the end-to-end pipelines.

Regulatory and Combinatorial Genomics.

Cognit's Phase-1 platform is focused on Gene expression prediction and Gene expression engineering.
The Gene expression prediction platform takes any DNA sequence (DNASE) across any cell/tissue type and eukaryotes and can predict the gene expression and transcriptomic profile of the gene.
Cognit's AI can predict gene expressions given mutations in many parts of the cis or trans regulatory elements.
Cognit's perturbative oracle allows rapid CRISPR-like experiments in-silico, enabling gene expression engineering.
In-silico saturation mutagenesis experiments with gene knock-out and engineered mutations across introns, exons, and regulatory elements to predict gene expression.

Cognit's Gene Expression Oracle ~ Applications

Gene expression is useful for identifying a disease's molecular signature and correlating a pharmacodynamic marker with the dose-dependent cellular responses to the exposure to a drug.
Cognit is revolutionizing drug discovery, clinical development, and commercialization of new drugs through cheap, in-silico experiments.
Directly predicting gene expression from DNA has significant advantages in rapidly accelerating the fine-mapping of diseases.
Enables significant leap in saturation mutagenesis that can be conducted in-silico.
Significant improvements in Directed Evolution.
Accelerated development of de-novo Biomolecules & Protein Engineering.

Cognit Edge

The core premise of Cognit is to run self-learning models in a distributed environment to accommodate very large architectures.

Decentralized Intelligence to run the workload across several nodes. (approach Genomics as a Large Model Architecture).
Learn from Epigenetic influences which require large model architectures.
Ethical, Responsible AI with explainability built from ground up.
Self-governing AI and Math models for Autonomous modeling using distributed learning.
New generation Deep Learning models that work in noisy/low data settings.
Focused on the discovery phase (read, write, deliver) with minimal or missing data.
Platform focus on whole-genome and single-cell computational investigations.
Multimodal, multi-agent learning systems that learn from across scenarios (motifs, functions, expressions, alleles).
Privacy of data, transparency of decisions, and pre-emptive controls with causal inference frameworks.