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“All that takes is just the right incentive.” Reliably identifying the mutations that matter for disease risk, drug response, and personalized medicine remains one of the hard problems in genomics. On the latest Novelty Search, Minos explained how SN107 uses Bittensor to improve variant calling, the step that turns...

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Openτensor Foundaτion

171,014 subscribers

23,157 次观看 • 2 个月前 •via X (Twitter)

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12 条评论

Openτensor Foundaτion 的头像
Openτensor Foundaτion2 个月前

Watch the full episode with @theminos_ai here:

On Chain Fox 🦊 的头像
On Chain Fox 🦊2 个月前

@theminos_ai 🚀🚀

ThisisYeesus 的头像
ThisisYeesus2 个月前

@theminos_ai 🔥 🔥 🔥

Alex 的头像
Alex2 个月前

@theminos_ai bittensor:native 💎

🇺🇸 DOUBLE-INFINITI-88 🇺🇸 的头像
🇺🇸 DOUBLE-INFINITI-88 🇺🇸2 个月前

@theminos_ai This is massive! 🔥 Bittensor powering real breakthroughs in genomics with Minos SN107 – right incentives unlocking better mutation detection and faster validation. Game changer for precision medicine! 🚀🙌 #Bittensor #TAO #MinosSN107 #DI88

khay 的头像
khay2 个月前

@theminos_ai const at it again

Zaki fathurrobani 的头像
Zaki fathurrobani2 个月前

@theminos_ai dump Tao 🤣🤣

Dr/ Ahmed Eljarhy 的头像
Dr/ Ahmed Eljarhy2 个月前

@theminos_ai i sold this trach called tao not moving and it died

Lamida 的头像
Lamida2 个月前

@theminos_ai Minos turns genome research into a fast live global experiment.

Maciej 'Mack' Stachowiak 的头像
Maciej 'Mack' Stachowiak2 个月前

computational reproducibility is not yet a clinical validation, plus there is a problem of overfit the models to standard benchmarking datasets (like Genome in a Bottle). This might/will also fail on generate edge cases or different sequencing platforms (like shifting from Illumina to PacBio)

Trung Duong 的头像
Trung Duong2 个月前

@theminos_ai Totally agree! Genomics is wild rn, so much potential 🔬

Jason傑森 🇭🇰 | 🛠️ 的头像
Jason傑森 🇭🇰 | 🛠️2 个月前

@theminos_ai 嘿基因组要來扭轉變速球了嗎

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Demis Hassabis just described the moment medicine stops treating disease and starts deleting it from the source code. For all of human history, doctors have fought symptoms. The tumor. The organ failure. The collapse. Always downstream. Always after the damage has already started. The cause sits upstream. Written into the DNA. Ninety-eight percent of the human genome sits in non-coding regions. For decades, science understood the genes but couldn’t read the vast dark territory between them. That’s where most disease hides. Hassabis: “It takes the big, long genetic sequences and then it tries to predict, if you made a mutation to this particular single letter, single position in the genetic sequence, will that be a harmful mutation that might cause disease, or is it benign?” AlphaGenome reads your entire genetic sequence and identifies the exact letter that’s corrupted. Not a region. Not a probability range. A single position in a three-billion-letter sequence. That alone would be a generational breakthrough. But most diseases aren’t that clean. Hassabis: “What if they’re multigenic diseases where there’s cascades of mutations causing the problem? Those are even harder to detect, but actually perfect for sort of AI.” One mutation is hard enough to find. A cascade of mutations interacting across the genome is a problem no human researcher can hold in their head at once. Three billion data points. Compounding errors across all of them. The human brain cannot solve that. AI doesn’t solve it either. It maps it. All of it. At once. The most devastating diseases on Earth. The ones medicine has called untreatable for generations. They are not mysteries to the algorithm. They’re compute problems. But finding the error was only ever half the equation. You also need the ability to fix it. That tool already exists. CRISPR is a molecular scalpel. It cuts DNA at exact positions. The limitation was never the editing. It was knowing exactly where to cut. Hassabis: “A kind of combination of things like AlphaGenome and CRISPR could be incredibly powerful.” AI reads the code. CRISPR rewrites it. One finds the mutation. The other corrects it at the source. Not managing symptoms. Not slowing progression. Deleting the error from the genome. The implications go beyond treatment. A disease corrected at the genetic level doesn’t just disappear from one patient. It disappears from their bloodline. The read access is here. The write access exists. The merge is inevitable. The era of accepting a broken genetic hand is ending. We stopped being passengers in our own biology.

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🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but *NOT* the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance
6:30

🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but *NOT* the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance

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96,002 次观看 • 1 年前

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1:05

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

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Andrew Ng just released a free 2-hour course on complete Harness Engineering How to go from one prompt to a reliable system of agents that can run, test, and improve themselves: 09:14 - Build your first agent from scratch 33:11 - Master agent loops 1:02:46 - Turn loops into reliable workflows 1:30:15 - Build agents that improve their own work 1:49:05 - Run the complete system without supervision Model → Harness → Reliable Software Most agent tutorials stop once the model can call a tool This one shows you how to build the infrastructure around it within the first 20 minutes Most people are still prompting one agent at a time Andrew Ng is already teaching the layer above: Harnesses that give agents context, tools, tests, and feedback Watch this brilliant course and build the harness Then read the full architecture below ↓
1:48:52

Andrew Ng just released a free 2-hour course on complete Harness Engineering How to go from one prompt to a reliable system of agents that can run, test, and improve themselves: 09:14 - Build your first agent from scratch 33:11 - Master agent loops 1:02:46 - Turn loops into reliable workflows 1:30:15 - Build agents that improve their own work 1:49:05 - Run the complete system without supervision Model → Harness → Reliable Software Most agent tutorials stop once the model can call a tool This one shows you how to build the infrastructure around it within the first 20 minutes Most people are still prompting one agent at a time Andrew Ng is already teaching the layer above: Harnesses that give agents context, tools, tests, and feedback Watch this brilliant course and build the harness Then read the full architecture below ↓

Ichigo

98,555 次观看 • 16 天前

Professor Jones, whose work sits at the center of the climate change modeling apparatus, stated plainly that he would not share his data even if the World Meteorological Organization requested it. His reasoning was that 25 years of work had gone into the research, and he saw no obligation to hand it over to people whose aim was to find something wrong with it. That statement is worth reading slowly, because it is a precise inversion of how science is supposed to work. The whole point of publishing a scientific claim is that other researchers can examine the underlying data, attempt to replicate the result, and either confirm or challenge the conclusion. That is not a hostile process. That is the process. Replication is the mechanism by which a scientific claim earns the right to be called scientific. When a researcher withholds the data behind conclusions that are being used to drive policy affecting hundreds of millions of people, the enterprise has left the domain of science and entered the domain of politics. The 25 years invested in the work is not a justification for withholding it. If anything, the scale of that investment makes independent scrutiny more important, not less. You do not get to claim scientific authority while simultaneously blocking the one process that confers it.
0:47

Professor Jones, whose work sits at the center of the climate change modeling apparatus, stated plainly that he would not share his data even if the World Meteorological Organization requested it. His reasoning was that 25 years of work had gone into the research, and he saw no obligation to hand it over to people whose aim was to find something wrong with it. That statement is worth reading slowly, because it is a precise inversion of how science is supposed to work. The whole point of publishing a scientific claim is that other researchers can examine the underlying data, attempt to replicate the result, and either confirm or challenge the conclusion. That is not a hostile process. That is the process. Replication is the mechanism by which a scientific claim earns the right to be called scientific. When a researcher withholds the data behind conclusions that are being used to drive policy affecting hundreds of millions of people, the enterprise has left the domain of science and entered the domain of politics. The 25 years invested in the work is not a justification for withholding it. If anything, the scale of that investment makes independent scrutiny more important, not less. You do not get to claim scientific authority while simultaneously blocking the one process that confers it.

Randall Carlson

25,234 次观看 • 3 个月前

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0:40

I don't tweet much about Bittensor but I've been watching it from a distance for a few months and it's one of the more interesting corners of crypto right now. So here's a fun chart: the three year history of subnet's share of new bittensor:native emissions: - Oct 2023: a small committee of validators picked the winners. One subnet took half of everything. - Feb 2025: the dTAO upgrade hands that decision to the market. Within weeks most of the old leaderboard is gone; Chutes takes #1 and keeps it into 2026. - Jul 2026: a new rule (the "Emission Gate") cuts inactive subnets to zero. 48 of 129 now get nothing. - Today's #1: Minos, a genomics subnet, taking 12.5% of the new TAO.

Edgar

11,243 次观看 • 20 天前

the future of POLITICS is clipping. Kevin O'Leary explained the clipping business indirectly. the people he pays most now take content, cut it into a 59-second clip, and bring in customers, because the work is measurable. swap customers for voters and that's a campaign. clipping isn't one ad buy with a ceiling. it's hundreds of creators turning one piece of content into thousands of clips that compound across every feed, and you only pay for the views that land. it's already everywhere. Netflix clips comedy specials. countries clip themselves for tourism. Zohran Mamdani streams for an hour as mayor of NYC and that hour turns into a week of clips across every platform. politics is just the next vertical, and the highest-stakes one. every future campaign will run this, local to federal. the ones that don't get outpaced by the ones that do.
0:27

the future of POLITICS is clipping. Kevin O'Leary explained the clipping business indirectly. the people he pays most now take content, cut it into a 59-second clip, and bring in customers, because the work is measurable. swap customers for voters and that's a campaign. clipping isn't one ad buy with a ceiling. it's hundreds of creators turning one piece of content into thousands of clips that compound across every feed, and you only pay for the views that land. it's already everywhere. Netflix clips comedy specials. countries clip themselves for tourism. Zohran Mamdani streams for an hour as mayor of NYC and that hour turns into a week of clips across every platform. politics is just the next vertical, and the highest-stakes one. every future campaign will run this, local to federal. the ones that don't get outpaced by the ones that do.

Mufasa

14,058 次观看 • 3 个月前

UAE’s🇦🇪 Habib Al Mulla: ‘I don’t believe the news that is spreading, that UAE is going to be part of the attacks on Iran and the Iranian people, that is not going to happen. At the end of the day, we are bound by geography. Iran is a neighbouring country. We have been living together for years and years, and for years and years to come we always wanted to have a peaceful, normal relationship with Iran, irrespective of the regime that is there.’ -حبيب الملا, one of the UAE’s most respected legal authorities on the latest episode of Going Underground Watch the full interview in the quoted post, or watch it on Rumble, link below in the replies👇
3:17

UAE’s🇦🇪 Habib Al Mulla: ‘I don’t believe the news that is spreading, that UAE is going to be part of the attacks on Iran and the Iranian people, that is not going to happen. At the end of the day, we are bound by geography. Iran is a neighbouring country. We have been living together for years and years, and for years and years to come we always wanted to have a peaceful, normal relationship with Iran, irrespective of the regime that is there.’ -حبيب الملا, one of the UAE’s most respected legal authorities on the latest episode of Going Underground Watch the full interview in the quoted post, or watch it on Rumble, link below in the replies👇

Going Underground

196,930 次观看 • 6 个月前

Mainnet is on the horizon! And that is only possible by having partners that can build the foundation of libraries, standards, and utilities of the network. In this episode of #Unshielded, what stands out in this conversation is how much of OpenZeppelin efforts goes into the unglamorous work; the essential resources for builders. That work is informed by years of lessons across different ecosystems. If you want to know how to build safer smart contracts at scale, listen to Luca share his insights with Midnight's Lauren Lee . Full discussion in the latest episode of Unshielded. 👇 YouTube:
0:34

Mainnet is on the horizon! And that is only possible by having partners that can build the foundation of libraries, standards, and utilities of the network. In this episode of #Unshielded, what stands out in this conversation is how much of OpenZeppelin efforts goes into the unglamorous work; the essential resources for builders. That work is informed by years of lessons across different ecosystems. If you want to know how to build safer smart contracts at scale, listen to Luca share his insights with Midnight's Lauren Lee . Full discussion in the latest episode of Unshielded. 👇 YouTube:

Midnight

13,407 次观看 • 7 个月前