The framework analyzes cellular responses to physical microenvironments.
Peter Lee#1501
New findings in Nature Methods highlight how Project Ex Vivo is helping researchers uncover patterns in cell behavior that may lead to more personalized therapies for patients dealing with cancer.
Microsoft researcher Lorin Crawford explains more: https://msft.it/6006vgDS8
Many users praise Microsoft's AI research on cell behavior insights for personalized cancer treatments because it applies AI to real medical problems with strong potential for oncology breakthroughs.
Today in @naturemethods, we shared research on how AI can help us better understand cell behavior, offering new insights into why cancer medicines do not work the same for everyone.
By learning more about cell state — how individual cancer cells respond to their surroundings — we have the potential to match therapies more precisely to each patient and improve outcomes. https://news.microsoft.com/signal/articles/why-dont-cancer-medicines-work-the-same-for-everyone-ex-vivo/
@satyanadella @naturemethods We all trained the AI. We should all benefit.
http://Aipledgeforhumanity.org
@satyanadella @naturemethods AI should solve real life problems not some digital only problems
AI started as the protein development software should continue it's impact on medicine and biology
Thanks for supporting life sciences AI
@satyanadella @naturemethods Cut the crap. We know this for decades you dumb fuck.
@satyanadella @naturemethods Shut the fuck up you piece of shit! Fix your stock!!!!!! $msft
@Microsoft Please stop posting garbage!!!! FIX YOUR STOCK!!!! $MSFT
@satyanadella @naturemethods This cell state approach to modeling individual cancer cell responses to their microenvironment is a strong step toward explaining heterogeneous drug efficacy. Looking forward to the Nature Methods details on the AI methods.
@satyanadella @naturemethods Satya, come on, and support your investors. MSFT is one of the most held stocks by retail investors and frankly this has been embarrassing. 12% down in a straight line.
@Microsoft Hope we soon find a cure for all kind of fucking cancer
As an Oncologist observing this, This may ultimately prove to be one of the most important applications of AI in oncology.
For decades, we classified cancers by where they arose:
Breast.
Lung.
Colon.
Pancreas.
Then we learned to classify them by mutations.
Now we are entering a third era:
Classifying cancer by cellular state.
Two patients may carry the same diagnosis.
The same mutation.
The same stage.
Receive the same drug.
Yet have completely different outcomes.
Why?
Because cancer is not a static photograph.
It is a living, evolving ecosystem.
Every tumor contains millions of cells making different decisions in real time.
Understanding those cellular states may help explain why some patients experience extraordinary responses while others derive little benefit.
This is where AI becomes transformative.
Not because it replaces physicians.
Not because it replaces scientists.
But because it can recognize patterns across billions of biological interactions that no human mind could ever process alone.
The future of oncology may not be asking:
“What cancer does this patient have?”
But rather:
“What is this cancer doing right now?”
That shift is profound.
If we can understand cell state, predict resistance before it occurs, and match therapies to the evolving biology of each patient’s tumor, precision oncology becomes truly precise.
The greatest challenge in cancer medicine has never been killing cancer cells.
It has been understanding them.
AI is giving us a new language to read the hidden conversations occurring inside tumors.
And once we can read those conversations, we may finally learn how to change their ending.
The future of cancer care will not be built by biology alone or computation alone.
It will emerge from the convergence of both.
And that future is arriving faster than most people realize.
This is the right frame, and the reason it matters is that the variation lives inside a single tumor, not only across patients.
Two cells from the same biopsy can sit in different states. One survives the drug while the cell next to it dies. The survivors are what the cancer regrows from.
Bulk sequencing averages the whole tumor into one profile, so it reads the dominant state and misses the rare resistant one. Single-cell resolution is what lets you see the cell that will cause the relapse before it does.
The modeling problem after that is predicting which state a given patient's cells are actually in on treatment day.
@satyanadella @naturemethods 🤖 AI is learning how individual cancer cells respond to their surroundings... 👨👩👧👦 Some cells are just like my teenager—refusing to cooperate before 10 AM.
@satyanadella @naturemethods The biggest breakthrough may be understanding why the same treatment creates different outcomes.
@satyanadella @naturemethods How photonic waves interact with cells at molecular and quantum level? https://chatgpt.com/share/6a16e12c-3438-83e9-89b7-3eefe33b769c
@satyanadella @naturemethods Huge potential here, but the proof will be generalization across diverse cohorts. If the models validate outside the original lab, this could really change treatment matching.
@satyanadella @naturemethods cancer cells don't stop changing state. even a perfect model of what works today doesn't work tomorrow. they've already adapted.
the real constraint: how fast you can measure, test, iterate. not the AI itself.
@Microsoft Very well
@satyanadella @naturemethods The key challenge is not predicting cell behavior, but capturing its context dependence at scale.
@satyanadella @naturemethods Great to see AI accelerating breakthroughs where they matter most.
@satyanadella @naturemethods Hey
“Am in a cell”
Don’t mistake my bar stool is a room please.
I am known not to behave.
Don’t ask why
The framework analyzes cellular responses to physical microenvironments.
Peter Lee#1501New findings in Nature Methods highlight how Project Ex Vivo is helping researchers uncover patterns in cell behavior that may lead to more personalized therapies for patients dealing with cancer.
Microsoft researcher Lorin Crawford explains more: https://msft.it/6006vgDS8