The PanGenomic BioGrid Oracle: Innovation at the Heart of Cognit’s LGM

What does Cognit do?

Well if we invoked Steve Jobs, he would probably say “At Cognit, we’re not just creating technology; we’re crafting experiences that revolutionize how we understand and interact with the world of genomics. It’s about making the complex simple and unlocking possibilities that were once thought to be in the realm of science fiction. Our work isn’t just a step forward; it’s a leap into a future where technology empowers humanity to solve its most intricate puzzles.” This literally captures the essence of everything we do in the simplicity of that narration.

However, since I am not him, I chose to write a rather intricate blog about Cognit’s groundbreaking innovation instead!

Cognit is crafting a cross-cell, cross-species Large Genomic Model (LGM) that enables high-resolution functional genomics through closed-loop, in-silico, gene and cell engineering, redefining the paradigms in genomics research. No, Cognit’s LGM is not an LLM.

At the heart of this LGM lies an innovation that is ground breaking. For lack of better names, it’s called the “PanGenomic BioGrid Oracle”, which represents a transformative leap in the field of genomics research, bringing forth a new era of understanding in genomics. Yes we shall come up with cooler names in the future, for now it is just functional names.

This sophisticated deep generative AI, meticulously constructed from the ground up, is designed to navigate and decode the complex n-dimensional grammar of genomics, offering unprecedented insights into the molecular intricacies of cancer.

What is a PanGenomic BioGrid Oracle?

Let’s break down the terms,

PanGenomic: This part of the term suggests a scope that extends beyond a single species. “Pan-genomic” approaches consider the entire spectrum of genetic material within a species or across multiple species. This includes core genes (common to all individuals of a species) and accessory genes (which varies among individuals).

The PanGenomic architecture represents all eukaryotes. While there are millions of species in eukaryotes, Cognit is focusing on the most prominent among Animals, Plants, Fungi, Protists and Chormista, which provide the highest variability in datasets.

BioGrid - A Multidimensional Lattice of Genomic Data: The BioGrid, integral to the Oracle, represents a multidimensional lattice of biological data. It is meticulously structured across various axes, encapsulating a wealth of genomic information. Imagine that there is one BioGrid per species and each BioGrid encapsulates a wealth of information.

Oracle: The Oracle in the PanGenomic BioGrid functions as a sophisticated prediction engine. It interprets the extensive genomic data across multiple species, utilizing advanced machine learning and generative AI. The Oracle’s key role is to uncover patterns, forecast genetic outcomes, and offer insights, particularly in areas like disease progression, gene expression, and therapeutic strategies. This component transforms the BioGrid to a dynamic resource for in-depth genomic exploration and personalized medicine applications.

For example, lets consider the BioGrid for humans,

• X-Axis (Gene Annotations): This axis includes 60,000 gene annotations (20,000 protein coding), capturing the vast genetic diversity within and across species. It includes information on gene sequences, alleles, motifs and mutations.
• Y-Axis (Cellular States and Clinical Variables): It encompasses different cellular conditions, disease states, and clinical parameters. For cancer, this includes various cell types (tumor, immune, stromal cells), stages of disease progression, and cell treatment regimes.
• Z-Axis (Molecular Bioassays): This axis details various molecular bio assays, such as Chromatin Immunoprecipitation (ChIP) for transcription factors and histone modifications, CAGE tracks for gene expression, DNASE/ATAC-seq for chromatin accessibility, and RNA sequencing for transcriptomic profiling.

The PanGenomic Perspective in Oncogenomics

The PanGenomic approach adopted by Cognit is revolutionary in its capacity to consider the full genetic diversity within a species or across different species. This extensive scope is particularly pivotal in cancer research, where genetic heterogeneity plays a critical role in tumor development and progression. The PanGenomic BioGrid Oracle learns behavior across eukaryotes in how cells, GRNs and cell signaling pathways function.

For example in breast cancer, where it incorporates an extensive range of genetic and environmental factors. This approach includes a focus on high-penetrance genes alongside BRCA1 and BRCA2, which, though less commonly mutated, play a significant role in cancer risk when altered. High-penetrance genes such as TP53, PTEN, STK11, and PALB2 are integral to this analysis. Their mutations, though rarer, can dramatically increase the likelihood of breast cancer and often have distinctive patterns of tumor development and progression.

Additionally, the BioGrid Oracle’s analysis integrates various treatment regimens, understanding how genetic profiles interact with different therapeutic approaches. This includes the assessment of responses to chemotherapy, hormone therapy, targeted therapies like PARP inhibitors, and emerging immunotherapies. By evaluating the efficacy of these treatments in the context of specific genetic backgrounds, the model aids in tailoring personalized treatment plans.

Furthermore, the BioGrid Oracle’s model extends its analysis across multiple cell types within the tumor microenvironment. This encompasses not just the cancer cells themselves but also the surrounding stromal cells, immune cells, and other components of the tumor microenvironment. Understanding the interactions among these various cell types, in the context of genetic variations and treatment responses, is crucial for a comprehensive understanding of tumor behavior and progression.

By encompassing a wide array of high-penetrance genes, intricate treatment regimens, and diverse cellular interactions, the Oracle offers a deeply nuanced understanding of breast cancer. This leads to more informed decision-making in treatment planning and provides insights that could pave the way for breakthroughs in personalized cancer therapy and prevention strategies.

Deep Generative AI: Deciphering Genomic Complexity

The Artificial Intelligence employed by the Oracle is a deep generative model, uniquely architected to interpret the highly complex language of genomics. Unlike linear models, this AI employs sophisticated math to analyze and predict the intricate interplay of genetic factors.

The PanGenomic approach helps the AI models to regularize and learn behaviors across eukaryotes which would not be evident if the AI models are only trained on gene sequences from a single species.

Capabilities:

• Predictive Modeling: It can predict how specific genetic alterations contribute to cancer progression and how these changes might respond to various treatments.
• Pattern Recognition: The AI excels at identifying patterns in genetic data, such as recognizing signatures of drug resistance or susceptibility to targeted therapies in various cancer types.
• Gene Interaction Mapping: It can decipher the complex web of gene interactions, essential in understanding oncogenic pathways and identifying potential therapeutic targets.

N-Dimensional Genomic Grammar: The Language of Life

The n-dimensional genomic grammar genomics is a concept that transcends the traditional one-dimensional view of linguistics. It acknowledges the multifaceted interactions and regulatory mechanisms that define genomic functionality.

In oncogenomics:

• The model can elucidate how transcription factors interact in a 3D chromatin structure, affecting gene expression patterns in cancer cells.
• It can analyze how epigenetic modifications, like DNA methylation and histone changes, influence gene expression in various cancer types, such as colorectal and pancreatic cancers.

Cognit’s Artificial Intelligence Brilliance in Genomics

The mathematical and computational foundation of Cognit’s LGM, powered by it’s PanGenomic BioGrid Oracle is characterized by its use of advanced math and ground-up design for n-dimensional grammar.

Cognit’s LGM is not just a singular model; rather, it comprises a compendium of models that work in harmony, reinforcing and enhancing one another’s capabilities.

• Genomic Vectors and Tensors: These tools are employed to represent and analyze complex interactions between genes, epigenetic factors, and environmental influences.
• Differential Equations and Topological Analysis: The AI is remodeled with math to capture dynamic genomic interactions and uncover hidden patterns in large-scale genomic data, facilitating new discoveries in cancer biology.
• Convolutional Neural Networks with Attention: CNNs with attention mechanisms are integrated to analyze spatial patterns within genomic data. The attention layers enable the model to focus on specific regions within the genomic sequences, identifying critical features that might influence gene expression and regulation. This approach is particularly effective in parsing through high-dimensional data, such as that found in chromatin accessibility or methylation patterns, allowing for more precise identification of biologically relevant signals.
• Graph Neural Networks: These are utilized to interpret the intricate network of genomic relationships. By leveraging the structure of graph theory, Graph Neural Networks can effectively model the connections and interactions between various genomic elements, such as gene-gene interactions, gene-protein relations, and other complex biological pathways.
• Modified Transformers: This approach adapts the transformer architecture, commonly used in language processing, to suit the multidimensional nature of genomic data. Unlike traditional models that handle linear sequences, these modified transformers are designed to process and analyze the complex, multi-faceted structure of genomic grammar. This allows for a more nuanced understanding of genomic interactions and regulatory mechanisms across different dimensions, enhancing the accuracy and depth of genomic analysis.
• Reinforcement Learning based on Gene Perturbations: This technique applies reinforcement learning algorithms to study the effects of gene perturbations. By simulating various genetic modifications and observing the resultant changes in cellular behavior or gene expression, the model learns to predict the outcomes of gene edits or mutations. This method is instrumental in understanding gene function and interaction, particularly in complex diseases like cancer, where gene perturbations play a crucial role in disease progression and treatment responses.
• BioMedical Hypothesis Generator: This advanced component of the AI system synthesizes the insights gathered from various analytical models to formulate cohesive and plausible biological hypotheses. Leveraging the data processed through genomic vectors, tensors, and the various neural network analyses, this generator is designed to propose hypotheses about gene function, disease mechanisms, or potential therapeutic targets. It integrates complex genomic interactions, cellular dynamics, and molecular assays to suggest comprehensive and scientifically grounded hypotheses, thereby accelerating the pace of discovery and decision-making in biomedical research, particularly in fields like oncogenomics and personalized medicine.

Impact on Cancer Research and Treatment

Cognit LGM’s potential impact on cancer research and personalized medicine is immense. Its ability to integrate and analyze vast datasets can lead to more precise cancer subtyping, biomarker discovery, and the development of novel therapeutic strategies.

Examples:

• In melanoma, the system could identify novel genetic pathways involved in resistance to immunotherapies.
• For leukemia, it can provide insights into how specific genetic aberrations impact response to targeted drugs.

The Future Trajectory

Looking forward, the integration of single-cell genomics and further advancements in AI algorithms will significantly enhance the BioGrid Oracle’s capabilities. This will allow for an even more detailed understanding of tumor heterogeneity and the tumor microenvironment, crucial for advancing cancer treatment.

The PanGenomic BioGrid Oracle by Cognit.AI marks a new frontier in oncogenomics. By leveraging the power of deep generative AI and a multidimensional approach to genomic analysis, it offers a profound understanding of the complex genomic grammar underlying cancer.

This innovation is set to transform the landscape of cancer research, paving the way for more effective, personalized treatments and a deeper understanding of one of humanity’s most challenging diseases.