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Cellformatica @Cellformatica_

GenAI Biomedical Discovery Co-pilot. Be ahead of the curve - interpret and rank high throughput data. Generate causal analysis on specific subjects. cellformatica.com Joined May 2024
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Cellformatica @Cellformatica_

2 years ago

Hear how Sam Bollinger of Jackson lab, Stanford, uses Cellformatica to study adipogenesis. youtu.be/rI-yGH9ug1w

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Cellformatica @Cellformatica_

2 years ago

Hear our CEO talk about how Cellformatica AI works youtu.be/3U7El2WO2WU?si… Its use for spatial data is described. Of interest to @nanostringtech @10xGenomics @AkoyaBio

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Garry P. Nolan @GarryPNolan

2 years ago

@Cellformatica_ My lab has in part been responsible for the data overload. Biological sciences today has succeeded in high throughput data analysis. CellFormatica helps with data overload using LLMs:

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Cellformatica @Cellformatica_

2 years ago

𝗕𝗶𝗼𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗔𝗜 𝗖𝗼-𝗽𝗶𝗹𝗼𝘁 (𝗙𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝗼𝗻) formaticon.cellformatica.com We are unveiling a new AI capability for biomedical inquiry. While it is still taking shape, we are excited to present it now to a broad audience for public trial and feedback. Formaticon enables users to have a personal biomedical discovery co-pilot/assistant tailored to provide precise scientific rationalization, references, new hypotheses generation, and novelty evaluation, whether as a single question or for a high-throughput study. Formaticon allows for creating profiles, running queries, storing and analyzing data, and purchasing computational credits through credit card transactions. In the coming days, we will be posting more videos and manuals with examples of Formaticon use. In brief, Formaticon works in two modalities: Batch Query (the primary modality) and a Detailed Report with Causal Networks. 𝗕𝗮𝘁𝗰𝗵 𝗤𝘂𝗲𝗿𝘆: ⬩ Takes as input a question addressed to an entity (gene, drug, disease, observation, organelle, etc.) ⬩ Can handle a single entity or thousands of entities of the same class ⬩ Returns a table where rows contain answers to the question posed to every entity in the input list where the columns contain: ⬩ Answers (Effect Rationale) ⬩ Effect Score ⬩ Confidence Score ⬩ Novelty Score ⬩ Novelty Rationale ⬩ Validation Experiments (coming soon) 𝗘𝗳𝗳𝗲𝗰𝘁 𝗥𝗮𝘁𝗶𝗼𝗻𝗮𝗹𝗲: ⬩ Contains information about the entity ⬩ New hypotheses on how the entity is key to the question asked ⬩ Precise literature references Scores: ⬩ Allow entity prioritization 𝗡𝗼𝘃𝗲𝗹𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 identifies entities most likely to be 𝑫𝒊𝒔𝒄𝒐𝒗𝒆𝒓𝒊𝒆𝒔 (unpublished but highly plausible associations) We are confident that nearly any scientific question or entity, such as genes, cell types, or microenvironments, can be addressed within the framework of the Batch Query modality.

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Cellformatica @Cellformatica_

2 years ago

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