Furkan Kurtoglu 🧬🦠🖥️🧫

Multiscale Systems Biologist • Bridging micro and macro levels • @MathCancer Lab and @IULuddy Alumni • @PhysiCell user • Open-Source

[Furkan Kurtoglu 🧬🦠🖥️🧫 @FKurtogluSysBio]

Hello World ! Starting today, I will share one great tool/software related systems biology/comp biology on each mondays.

[Dr. Luke in China @96Stats]

My university lab (shanghai jiao tong @sjtu1896 ) just made an “agentic” AI system for diagnosing rare diseases really well. It doesn’t just guess either, it shows traceable reasoning linked to evidence so you can verify.

This specific paper looks really cool too, it works like a team (multiple agents not just one chatbot). It takes messy clinical data and outputs a really nice list with reasons, exactly whats needed!

The west very rarely makes AI for rare diseases because it’s not profitable, so its nice to see China doing lots of work on it. This is the type of work that will help save lives (not chatgpt/claude chatbots..) and its where China is absolutely leading in

[Mingyao Li @DrMingyaoLi]

Excited to share our new paper in Nature Methods! 🚀
We present iSCALE, a “Google Map” for spatial transcriptomics, scaling cellular-resolution gene expression mapping to large-sized tissues beyond the reach of current platforms. 👉https://t.co/WBvR4Cyirr

[MorphoNet @MorphoTweet]

Having difficulties segmenting your 3D🔬 datasets ? You can try the CellposeSAM plugin🧑‍💻directly in MorphoNet in one click ! No python required 😌

[Denis Wirtz @deniswirtz]

Human endometrial tissue mapped using CODA and InterpolAI

Red: endothelial cells; yellow: epithelial cells; purple: stromal cells

More about this here: https://t.co/ABJrYxgVw2
More about CODA: https://t.co/OrRXmybBZe
More about InterpolAI: https://t.co/LIpzl3heoB

[Nature Rev Immunol @NatRevImmunol]

Linking tumour angiogenesis and tumour immunity https://t.co/u0akpv195N

[adi @adonis_singh]

im sorry but what is this graph

[Prof. Nikolai Slavov @slavov_n]

A periodic reminder to leave behind simplistic & naive notions.

[Yohan @yohaniddawela]

Changing your map’s resolution can change your conclusions.

It’s called the Support Effect.

And it distorts everything from poverty estimates to climate models.

Here’s how it works:

[Neil Renic @NC_Renic]

GPT-6 will have the intelligence to open a bakery instead of doing a PhD

[Lydia Finley @lydfinley]

Our latest work, led by @__btjackson and Angela Montero @MSKCancerCenter demonstrates how intracellular metabolic gradients induce dependence on exogenous pyruvate in embryonic stem cells

[Furkan Kurtoglu 🧬🦠🖥️🧫 @FKurtogluSysBio]

@cryptomit @GithubProjects You can run it locally with ollama using your local LLM. They support it

[Jeffrey West @mathoncbro]

The end of the genetic paradigm of cancer.

[Melike Dönertaş @melikedonertas]

Nature publishing a piece on unpaid academic labor is the best joke I’ve read in a while.
https://t.co/hLK5opHafC

[Sushmita Roy @sroyyors.bsky.social,genomic.social @sroyyors]

Rich resource paper from HubMap https://t.co/vUxgbYNp5d

[Nurdan Kar, PhD @nkarmath]

I am happy to share that I have organized my first symposium. I look forward to many exciting contributions! ⬇️
https://t.co/PlYjxWjTL5

[Organoid News @organoidscience]

💾 Save this review for a literature recap on vascularized #organoids.

Scientists provide an in-depth overview of the current landscape of vascularized organoid fabrication and functionality, addressing challenges and opportunities within the field.

https://t.co/ZgauOocBIn

[Eytan Ruppin, MD, PhD @EytanRuppin]

Delighted to announce the publication of our 10 hallmarks of AI in precision oncology Review paper in Nature Cancer! This work provides a comprehensive, up-to-date synthesis of AI’s transformative role in cancer care. Check it out here: https://t.co/DiU1W66MAo

[Holger Heyn @hoheyn]

Ever wondered why we do all these #Omics?
Here is your answer: https://t.co/wexzIPc5wt 📜

Multi-Omics integration of a Human Cell Atlas (@humancellatlas ) of the Kidney, coordinated by the #HCA Standards and Technology Working Group and co-led by @eli_mereu.

Datasets:
10x Genomics scRNA 3´and 5 plus single-nuclei 3´ RNA-seq. Special guests: Smart-seq2 and scNMT-seq.

Highlights:
1. Identification of rare and clinically relevant cell types, such as WFDC2+ thick ascending limb cells and Norn cells (EPO-producing fibroblasts).
2. Improved kidney disease trait heritability analysis and enhanced functional annotation of disease-associated loci.

Extra feature:
scOMM, an interpretable machine learning tool for multimodal cell-type classification and benchmarking.

Enjoy reading bioRxiv & medRxiv:
https://t.co/wexzIPc5wt

[Nature Reviews Genetics @NatureRevGenet]

New online! Beyond the black box with biologically informed neural networks https://t.co/prbcPOr63Y

[bioRxiv SysBio @biorxiv_sysbio]

Donor-Specific Digital Twin for Living Donor Liver Transplant Recovery https://t.co/36UsTW51KZ #biorxiv_sysbio