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.

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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.

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.