Innovative perspective. I hear you. However, the AI-generated images ( including those in cognit.ai/) are amateurish and disservice your intended purpose.
A closer look at what you wrote in cognit.ai/ reveals a disconnect between what your titles seem to convey and what you really have to offer. But, perhaps I am not privee to information you are privee to. Wish you all the best for sure.
I hear you on the image. Typically those images serve as a background when we share links on social media which helps with content displayed on a darker contrast. There is no other meaning or purpose behind the images.
Cognit is a private entity working on simulating gene regulatory networks. CELL is a separate consortium not related to Cognit. Unsure where the disconnect might be :)
Thanks for the clarifications. I guess I am not seeing your mathematical solutions yet. If your simulations are based on CELL data as it is, then I am wondering if you are factoring in all the mathematical / statistical limitations related too how the omics data was generated in the first place.
PS: Empirical limitations constitute another layer of complexity/heterogeneity that deserve consideration as well-- as they pertains to their bearing on downstream mathematics/ statistics--.
Brilliant point on “how the omics data was generated in the first place”. This matters a LOT and unfortunately cannot be controlled either in public datasets, nor across labs. Instead, it is best to rely on mathematical models which are built on stochastic input in a Sobolev space that accommodates weak derivatives.
For a heavier mathematical insight for the solutions, treating the sequencing pipeline as a random operator that maps latent molecular fields to observed counts, all empirical quirks such as Poisson sampling, amplification bias, dropout, batch transformations etc and are folded into that mapping. Heteroscedastic, heavy-tailed noise is captured with a Lévy family whose parameters are calibrated hierarchically from control replicates.
The resulting inverse problem is regularised in Sobolev norms; the reconstructed signal seeds the reaction–diffusion–advection SPDE powering the simulation, while its posterior covariance becomes coloured noise that propagates measurement uncertainty downstream.
Batch effects enter as low-rank perturbations to the same operator and are aligned through mutual-nearest-neighbour references, then marginalised to keep global uncertainty coherent. Variable tissue dissociation, antibody cross-reactivity, and other wet-lab artefacts integrate identically: each step gains its own stochastic operator.
The Seal of Rigor mandates that every submission list the full operator chain, the chosen Sobolev order, and the calibrated error budget, enabling reviewers to reproduce the entire uncertainty pipeline. Empirical limitations thus remain explicit random variables whose influence is quantified throughout the simulation.
We have not published the blueprint for CELL consortium yet, it is coming soon... :)
Innovative perspective. I hear you. However, the AI-generated images ( including those in cognit.ai/) are amateurish and disservice your intended purpose.
A closer look at what you wrote in cognit.ai/ reveals a disconnect between what your titles seem to convey and what you really have to offer. But, perhaps I am not privee to information you are privee to. Wish you all the best for sure.
I hear you on the image. Typically those images serve as a background when we share links on social media which helps with content displayed on a darker contrast. There is no other meaning or purpose behind the images.
Cognit is a private entity working on simulating gene regulatory networks. CELL is a separate consortium not related to Cognit. Unsure where the disconnect might be :)
Thanks for the clarifications. I guess I am not seeing your mathematical solutions yet. If your simulations are based on CELL data as it is, then I am wondering if you are factoring in all the mathematical / statistical limitations related too how the omics data was generated in the first place.
PS: Empirical limitations constitute another layer of complexity/heterogeneity that deserve consideration as well-- as they pertains to their bearing on downstream mathematics/ statistics--.
Brilliant point on “how the omics data was generated in the first place”. This matters a LOT and unfortunately cannot be controlled either in public datasets, nor across labs. Instead, it is best to rely on mathematical models which are built on stochastic input in a Sobolev space that accommodates weak derivatives.
For a heavier mathematical insight for the solutions, treating the sequencing pipeline as a random operator that maps latent molecular fields to observed counts, all empirical quirks such as Poisson sampling, amplification bias, dropout, batch transformations etc and are folded into that mapping. Heteroscedastic, heavy-tailed noise is captured with a Lévy family whose parameters are calibrated hierarchically from control replicates.
The resulting inverse problem is regularised in Sobolev norms; the reconstructed signal seeds the reaction–diffusion–advection SPDE powering the simulation, while its posterior covariance becomes coloured noise that propagates measurement uncertainty downstream.
Batch effects enter as low-rank perturbations to the same operator and are aligned through mutual-nearest-neighbour references, then marginalised to keep global uncertainty coherent. Variable tissue dissociation, antibody cross-reactivity, and other wet-lab artefacts integrate identically: each step gains its own stochastic operator.
The Seal of Rigor mandates that every submission list the full operator chain, the chosen Sobolev order, and the calibrated error budget, enabling reviewers to reproduce the entire uncertainty pipeline. Empirical limitations thus remain explicit random variables whose influence is quantified throughout the simulation.
We have not published the blueprint for CELL consortium yet, it is coming soon... :)
Thank you. Looking forward to it.