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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.

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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.

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