Yuzhou Chang

Joint postdoc at OSUMC and BIDMC.
Graph signal processing
Spatial omics
Graph representation learning

[Elizabeth McKenna @ElizSMcKenna]

Now online in @CD_AACR: Same-Slide Spatial Multi-Omics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment - by Stephanie Pei Tung Yiu, @Yuzhou_Chang, @yeoyaoyu, Huaying Qiu, Wenrui Wu, @SizunJ, and colleagues

[Sizun Jiang @SizunJ]

Chuffed to share KRONOS: A foundation model for Spatial Proteomics! https://t.co/Fp09v1irAi

Exciting work with the indomitable @AI4Pathology, led by the amazing @mshaban_ai, @Yuzhou_Chang, with Huaying, @yeoyaoyu, @GuillaumeJaume, Andrew Song, and our collaborators

[Yuzhou Chang @Yuzhou_Chang]

@Guangyu_Wang01 congrats! Important tool!

[Guangyu Wang @Guangyu_Wang01]

We are so happy to publish our new foundation model in Nature Methods. we trained OmiCLIP, a vision-omics due modalities model to bridge pathology image and transcriptomic, and Loki, a platform using OmiCLIP as backbone for ST and HE image cross analysis. https://t.co/UkHryBRFqQ

[Victoria Slocum @victorialslocum]

Multi-agent architectures are the FUTURE

Here are 6 different types:

𝟭. 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 (𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹) 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
A supervisor agent orchestrates multiple specialized agents.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦:
• One agent retrieves information from internal data sources
• Another agent specializes in public information from web searches
• A third agent specializes in retrieving information from personal accounts (email, chat)

𝟮. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
Incorporates human verification before proceeding to next actions, used when handling sensitive information.

𝟯/𝟱. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹) 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
Agents communicate directly with one another in a many-to-many fashion. Forms a decentralized network without strict hierarchical structure.

𝟰. 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
Agents process tasks in sequence, where one agent's output becomes input for the next.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: Three sequential agents where:
• First query agent retrieves information from vector search
• Second query agent retrieves additional information from web search based on first agent's findings
• Final generation agent creates a response using information from both query agents

𝟱. 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
Includes agents dedicated to transforming data.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦:
• A transformation agent that enriches data at insert-time or transforms existing collections

There are also some other patterns that can be combined with these architectures:
• 𝗟𝗼𝗼𝗽 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: Iterative cycles for continuous improvement
• 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: Multiple agents working simultaneously on different parts of a task
• 𝗥𝗼𝘂𝘁𝗲𝗿 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: A central router determining which agents to invoke
• 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿/𝘀𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘇𝗲𝗿 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: Collecting and synthesizing outputs from multiple agents

Check out this ebook for more info: https://t.co/pfM1lcp3SC

[Lambda @LambdaAPI]

Multi-node NVIDIA HGX B200-accelerated clusters are available NOW, on-demand through Lambda 1-Click Clusters.

[Ahmet F. Coskun @ahmetfcoskun]

Spatial proteomics is here, but spatial functional proteomics?

Here is our Nature BME paper on spatial protein interactomics (https://t.co/xGZ6DUQTWh) illuminating how 47 proteins co-localize/ interact within 20 nm and their function in tissues.

@naturemethods @rita_strack

[Akshay 🚀 @akshay_pachaar]

Model Context Protocol (MCP), clearly explained:

[Matt Dancho (Business Science) @mdancho84]

RIP Data Scientists.

The Generative AI Data Scientist is NOW what companies want.

This is actually good news. Let me explain:

[Yuzhou Chang @Yuzhou_Chang]

Lastly, I am very excited to integrate GSP, a novel mathematical theory based on graphs with novel types of spatial omics data. Moving forward, we will continue to explore and develop new theoretical models in conjunction with spatial biotechnology. ⏭️ To be continued ⏭️

[Yuzhou Chang @Yuzhou_Chang]

Following Yao's post @yeoyaoyu , I'm very excited to share the principles of spectral graph cross-correlation (SGCC). I would also like to express my gratitude for the support and guidance from my co-mentors @SizunJ and @QinMaBMBL.

[YaoYu Yeo @yeoyaoyu]

Spatial proteomics & transcriptomics are amazing technologies that have transformed how we study diseases. Can we do more?

Here's our new preprint that combines both assays into one! (massive thanks to @SizunJ @Yuzhou_Chang Huaying @QinMaBMBL et al.

https://t.co/I0bsLsjhXk

[Yuzhou Chang @Yuzhou_Chang]

Fifth, we apply SGCC across multiple samples, obtaining a series of SGCC scores as spatial factors. We then use differential expression analysis or time-series modeling to calculate gene expressions that correlate with these spatial factors. Yao has previously described.

[Yuzhou Chang @Yuzhou_Chang]

@yeoyaoyu @SizunJ @QinMaBMBL teamwork🙌

[YaoYu Yeo @yeoyaoyu]

Spatial proteomics & transcriptomics are amazing technologies that have transformed how we study diseases. Can we do more?

Here's our new preprint that combines both assays into one! (massive thanks to @SizunJ @Yuzhou_Chang Huaying @QinMaBMBL et al.

https://t.co/I0bsLsjhXk