Revolutionizing Multiomics Research with Deep Learning AI.

Cognit consolidates and optimizes horizontal AI modeling needs across research in Multi-Omics in low data settings to accelerate research and reduce cost.

Thanks for submitting!

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

Computational Genomics is a new area of science where 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.

Challenges that span across genomics modeling systems.

  •  In excess of 60% of the AI-modeling issues in computational genomics are standard across all organizations.

  • Missing data, Low data, and noisy layered data across multi-omics make it hard to improve specificity.

  • Low specificity affects downstream scientific validations which are extremely costly.  

Cognit enables a comprehensive platform to perform in-silico experiments that cut across omics research.

  • Mathematical formalisms for spatial and temporal encodings.

  • Heterogeneous data aggregation from across gene and cell atlas.

  • Closed-loop collaboration tool from computation-to-bench-to-computation.

  • Cross-collaboration tool across multi-omics research.

  • Parameter estimations, treatment of uncertainty, simulation modeling, and prediction.

  • In-silico models and oracles for perturbative experiments using synthetic sequences.

  • In-silico CRISPR experiments by mutating motifs in genomic regions.

  • Deep RL imputations and queries when closely related genes are missing from the reference database.

  • Automatic Sequence Alignment, Auto Annotation, Motifs search, Expressions

  • Integrate advanced AI models across academia and industry for analysis of Transcription, Regulation, and Translation.

  • Structure-function relationships for large and small nucleic acids, proteins, and macromolecular assemblies.

  • Toolkits for advanced stochastic and non-linear heuristics.

  • Image analysis and visualization for high-dimensional data.

Cognit Edge

Core premise of Cognit is to run self-learning models in a distributed environment to accommodate very large architectures (GPUs fail here)

  • 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.