Here is the link to the original article: https://www.toutiao.com/article/7094940100450107935/
Here is the link to the original article: https://www.toutiao.com/article/7094940100450107935/
Seems pretty effective. :)
What a beacon of light and inspiration you are. Thanks for your work.
That said, I welcome you to publish your work so it can become even better after a formalized peer-review process.
I'd love to chat.
1. do you have a patreon or something where people can support you?
2. do you know how to obtain the nucleotide sequences of c-MYC?
3. have you seen machine learning applications that can predict protein function? specifically, the goal is to predict if protein X could participate in cleaving of DNA similar to CRISPR Cas9? false positives are okay, provided the negatives are accurate (i.e., guarantees protein cannot cleave DNA like Cas9).
Check:
https://www.ensembl.org/Homo_sapiens/Transcript/Summary?db=c...
This is the main transcript, the remaining 9 are in the table
for instance, how to obtain the nucleotide sequences for TP53, or for the NHN/RuvC domains of CRISPR Cas9?
thanks again for your help.
same site (ENSEMBL): https://www.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=...
also you can check human genes at GeneCards: https://www.genecards.org/
re other questions: The crucial thing is: what are your goals? Bioinformatics and cancer biology in particular are rather vast fields. Questions and answers make sense if you can place what you just got in some framework. And getting there is non-trivial, see i.e. +800pp book: https://www.amazon.com/Lewins-GENES-XII-Jocelyn-Krebs/dp/128...
is it possible to find nucleotide sequences from amplified c-MYC from cancerous tissue, either burkitt's lymphoma or TNBC?
ensembl is valuable, but it would be more efficient to hire someone to decode the different options. what type of researchers understand the different sequence options on an ensembl page: cancer biologist, geneticist, bioinformatics scientist?
thanks again for your help!
the goal is conducting some first-principles analysis of cancer, nothing fancy and very likely a waste of time.
Whole brain radiotherapy works by killing everything a little bit in the hopes that the tumors die first (e.g. like chemo). There are good reasonswhy this tends to mostly-sort-of be true, but getting the balance right is hard and too much dose will definitely cause other problems.
SRS is a way of targeting radiation directly to locations to kill cells, with less effective dose (hence damage) to other parts of the brain.
It's all pretty harsh stuff, and you can die from the necrotic tissue caused by it, also.
Often with this kind of disease you know you aren't going to cure someone, but you can get rid of symptoms and make people more comfortable (palliative care).
Not least, Nvidia's documentation is not the best resource to learn from. This seems like quite a lot of work to understand ML and write custom CUDA code to get this to work. Do you have any insight about how you taught yourself these things and what tools you use?
Usually you would - nowadays at least - use someone else's optimized kernel + ML library but if you wanted to roll your own that's doable.
Calico (California Life Corporation)
(I’m hoping to work there after my PhD)
It's a nice thought exercise, but of course not realistic. FAANGs wouldn't be able to attract or retain the talent if they were purely in scientific medical research (where do you think those fancy offices and fat paychecks comes from?).
Having said that, it doesn't have to be either/or. I strongly believe that companies can be successful in balancing between thriving commercial businesses and using its profits to advance research that can benefit the society at large. Case in point: Google's DeepMind has for years published groundbreaking work in various areas including breast cancer research [1], protein folding [2], energy savings and climate change [3], and more. Also Calico [4], or its investments in health startups via GV, and of course all the well known services to users.
What I'd like to see is more companies following a path where a portion of its profits is used to fund research in areas that can cause societal change for the better (which, in its turn, may eventually became new sources of revenue down the road - e.g. protein folding, self-driving cars).
(disclaimer: Googler here, but no relationship with any of the projects mentioned)
[1] https://www.deepmind.com/publications/international-evaluati...
[2] https://www.technologyreview.com/2021/07/22/1029973/deepmind...
[3] https://www.deepmind.com/blog/real-world-challenges-for-agi
A decade ago I did a voluntary work on a simple app for oral cancer detection(questionnaire, data-entry) with an oncologist, Who used it to for survey in the tribal regions of India and he used to say how lack of early detection is the number one reason for so many deaths(many die without knowing that they had cancer).
He's now settled in Germany, But I would still pass this story to him & perhaps his colleagues in India could make use of it.
Thank you.
I have a question for you. What is the tech stack that you use?
And if it is not too much: What resources did you use to learn Deep Learning?
I apologize if this has been asked and answered before, but do you speak Mandarin, or was the interview in English?
Asking out of curiosity if it’s the former, and if so, how difficult was it to learn whilst also working on this and other things? And are there any resources or tips you might share that you found helpful?
That's why not one startup has hacked healthcare in America, not one. No breakaway successes making pharma cheaper. Like those incubators in Bangladesh, for premature babies not startups that is, those did OK. Some pill startups yes, but again that's an expensivification of medicine. If you can make medicine more expensive, they welcome you in!
Jim Clark tried this, he was on a roll after Silicon Graphics and Netscape. Huge roll about as strong as Elon Musk as a serial entrepreneur. Then he targeted healthcare and couldn't do shit, just couldn't get anything to happen. He literally talked about getting "rid of all the assholes" by which he meant insurance and doctors and hospitals and middlemen and pharma and all the other "assholes" of that nature in his own words, but leave "only one asshole in the middle--us [paraphrased]." It's in a book. That book also talks about guys going on airplanes and chasing goats off cliffs, saying "Some people do this."
Well the real structure of medicine isn't designed around the human body, it's designed around cornering the market. Market dominance. So of course it has this immune system against cost reduction and efficiencies--efficiencies especially--and you do know it lobbies, don't you? And can bribe the FDA like the Sacklers did? Or lobby the FDA, and then bribe underneath so when people see favoritism they think it's the over-the-counter placebo causing a placebo effect without suspecting an additional more potent under-the-table dosage of money. In case the administration has built up a tolerance to the over-the-counter stuff.
If America has dysfunctional healthcare there is still the rest of the world. Which might be good for Americans eventually as the tech will come across one way or another.
Which raises the question - why does the vast majority of healthcare tech development come from the US? (I included "development" to get around the out-sourcing of testing to China.)
Similar to silicon valley’s tech scene, you get migration of people who want to work at a level where that sort of capital is required.
Modern treatments are hugely expensive to develop, and they also tend to be very specific (ie. only 1 in 5,000 people might get the exact right kind of brain tumour for your treatment to be an option).
With only about 1 million all-cause deaths per year in the UK, that means your treatment for a specific terminal brain tumour might only have 200 patients per year.
The 'we just saved 4 hours of clinician time by using fancy AI' just isn't worth it if it only saves 800 hours of clinician time per year, yet costs millions to develop.
The fix is to roll this out somewhere with more patients (eg. China) and where trials are cheaper (ie. China).
No don't be like coolwulf, be coolwulf. Be him, do what he did, make the algorithms from your brain protect the algorithms that make up your brain. You know they're symbiotic right?
Transistor and neuron fighting together against destruction. Symbiosis.
EDIT: coolwulf might as well be John von Neumann, the last line of defense against brain cancer and brain surgery, those neurons reaching beyond the skull to make a change in the World for some part of that World to then reach inside that skull and protect it. The chip as the final garrison of the biological human brain. Ghost in the Shell. Ghost retreating from the Shell and regrouping, no ruling out whether it can triumph when it returns to reconquer its damaged Shell.
Money moves too many times, like the pebbles under the shells. So that's a deflection so people don't see who's serving whom. Every time someone thinks they can remember what's under a shell before it's shuffled, they move it really fast, and like with feints. And of course this doesn't work for real, hands aren't fast enough, so you gotta layer it with like something to injure their eye with a flash while they're looking, say a laser to the pupil, or maybe...
You could make a little trapdoor for the pebble provided the guy will not ask which shell the pebble was actually in. So deflect all questions.
But the end outcome is servitude comes from all over the world to an American banker, an American doctor, and an American executive. There's a few others. But the long tail of the American middle class? That was only necessary because those bankers, doctors and executives would never themselves (or with their sons) man the nuclear ballistic submarines, dogfight jets, aircraft carriers, CIA undercover agents, and especially infantrymen--never never never infantrymen. I've only heard of a single lonely guy becoming an enlisted man in a high-bullets-flying environment, when he could have gone anywhere--from Philips Exeter in fact. Anecdotal.
Yeah undercover agents get wrapped up and tortured, but infantrymen are bullet sponges. Riflemen, holy shit. WW2 riflemen, no let's not talk about that. At least spies there's a whole song and dance to the whole thing, part two of 1984, and speaking as someone who's stood up to torture, there's not the immediacy of the infantry where the brain surgery is much messier. Well on second thought it's not that different from getting rocks thrown at you, like I have this year...
Listen. So this is what's up. There's these guys, they eat all this work and don't do anything. Doctors don't do anything. They only use 5% of what they learned, the rest being a masochistic filter the elders imposed on the youngers to keep them from bringing them down to human reality with competition. So morally a doctor should only study for 1 year, not 20 (if not less, because of retention). That's five percent! Don't argue with that part, argue with something else!
And then plus surgeons, spesh brain surgeons which is what every medical student "wants to be when he grows up" as elder doctors put it, so the thing there is younger surgeons have much better manual dexterity than older surgeons, you can't do it past your fifties generally. What does that mean? Learn it faster! But no, they have to say "I studied for 20 years to tell you my bullshit" like come on, that's masochism. You spent 1 year learning worthwhile things and 19 years in the medical school "slammer" learning to be a good bitch, that's the whole game in medicine, hazing. Because otherwise people would say hey just pump out more doctors then the doctors have to say "nonono, you can't because it's soooooo expensive to make a doctor and America loses money [pretends to lose money] on training doctors and you have to be the absolute best of the best of the best to learn how to multiply 3-digit numbers".
It's so so stupid, especially because of this: it's all multiple choice. All of it. It's all multiple choice. No essays or explanations, just filling in black circles. I have contempt for using that form of testing alone because I'm so good at it with no effort. Like not even doing the reading. Show up "naked", a good enough mathematician should be able to learn from the test itself, from NOTHING, NOWHERE, and NOBODY. Destroy the integrity of these measurements. Get the test to leak the answers. Like I could do it in Chinese [I don't read Kanji, I can only make out Oracle Bones from 2600 BC] and be measurably better than a monkey. I don't generally brag about it because I have no respect for it, like not having respect for torture. But if that's the underlying reason some doctor claims to be better than me? If such a doctor says he's better than me, then let us agree we are not equal.
This post makes a lot of points, but in general I think they boil down to the above statement : the belief that large, complex systems are just run by stupid and/or malicious people and that a sufficiently clever 'hack' will fix all the problems. I think that is an attitude that is common on HN, but wrong.
Most big problems are not technology problems, they are People problems with a capital P. Technology problems can be fixed with 'one simple hack they don't want you to know about!!!' People problems are complex and messy and cause and effect can be intermingled vertically and horizontally with other seemingly unrelated factors as well as temporally with things that don't even exist yet or used to exist but don't anymore!
The way we fix these messy, complex People problems is by respecting that they are real problems, that the people acting on those systems are (mostly) reasonable people just doing what reasonable people do, and slogging through solutions a day at a time with the oldest technology around - political power. These problems resolve if you can get enough people to agree they need solved.
Marty Makary wrote a book a couple years back, The Price We Pay on the wicked knot of a problem that is US health care. For those mildly interested, there was a Peter Attia Podcast inteview a while back that covers the gist of it: https://peterattiamd.com/martymakary/
For those that want to get some color from some of the biggest problems from a clinician/practioner's perpective, I found some of these podcast episodes to be pretty great/eye opening: https://zdoggmd.com/podcasts/
There, it may content errors, since it's a machine translated copy what had to be telegraphed by the operator. Thank him, he's a brilliant guy.