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Medical Breakthrough in Spinal Cord Injuries Was Made by a Computer Program
- programnature 11y agoTopological data analysis is amazing, too bad all the hype around DL is leaving it in relative obscurity.
- mfoy_ 11y agoI think the second big take away is that this was only made possible because the scientists willingly shared their "dark data"-- data and lab notes from failed experiments. I wonder how much data is hoarded privately and never opened up and analyzed like this.
- MollyR 11y agoA lot of data is hoarded because scientists often have to compete for limited research funding (at least from what I've seen in the US).
- andreasklinger 11y agonext up: "let's depend funding on how much data is shared and other people use it" after that: "researchers overshare data and use each others data for no reason but to bump numbers" it's interesting how every system tends to sooner or later be gamified by its players
- valarauca1 11y agoWhat metrics you decide to keep are ultimately what you will try to optimize too. We are what we measure.
- edoloughlin 11y agoI was looking for an SMBC strip that captures this (there's got to be one) but this is the closest I could get: http://www.smbc-comics.com/index.php?id=1624 http://www.smbc-comics.com/index.php?id=1624
- pmiller2 11y agoI'm, unfortunately, very familiar with the LPU (Least Publishable Unit) after having been to grad school. It's actually a thing, especially among pre-tenure people.
- guelo 11y agoExample from the programming world: github profiles are used as a hiring tool, programmers start dumping thousands of undocumented useless "projects" into github.
- oldmanjay 11y agooccasionally, I wonder how much more advanced our world would be if scientific and government data were simply available as a default choice. then I smile at my own optimistic viewpoint and come back to reality.
- cryoshon 11y agoFrequently a lot of this hoarded data is flawed or defective due to improper setup or execution of the experiment. That isn't to say the information in this "dark data" is useless, but it needs to be taken in context. The cleanest data with the best results are put forward into a paper; the chaff is not.
- chris_wot 11y agoShouldn't this be noted? Isn't providing the "best" data risking making data fit your hypothesis?
- technofiend 11y agoNot if your dark data is "I forgot to autoclave an instrument and contaminated my samples." In that case without an unexpected positive result it's just error and not worth reporting.
- catshirt 11y agobiology is not my forte but- i imagine "dark data" (with context) is always more valuable than no data.
- dmd 11y agoLast time your build failed because you made a typo, did you package it up and release that version anyway?
- wyager 11y agoBad analogy. There's a lot more to learn from a failed medical experiment than a failed build.
- cryoshon 11y agoDepends on whether the lessons have already been learned or not. For stuff like "we fucked up our culture, therefore our cells couldn't do whatever we wanted" is well understood. In clinical trials where some patients may respond to a treatment and others not, there's definitely a lot more to learn there, if you have a large enough data set and a plurality of controls.
- cryoshon 11y ago"The process was outlined in a paper published today in Nature, and hints at the possibility of medical breakthroughs lurking in the data of failed experiments." If there was some way to make sense of data from negative-results experiments reliably, it would be absolutely revolutionary and certainly turn our ideas about what constitutes a successful experiment onto its head. I am very hopeful for fruitful results from the methods outlined in this article. I worry about the usage of "failed" experimental data here, though. I've "failed" a lot of experiments for reasons other than not finding the effect I was looking for in my data. Any exploration of data from negative-results experiments needs to be taken very narrowly, with a deep understanding of exactly what effect is being examined. Experiments are frequently designed incorrectly for studying the effect they want, and are almost always not suitably controlled for examining non-primary effects. Try to find a trend throughout non-primary effects over a large swath of experiments, and I'm sure you will-- but it may be noise.
- pmiller2 11y ago> Any exploration of data from negative-results experiments needs to be taken very narrowly, with a deep understanding of exactly what effect is being examined. I disagree. There's value in data mining previous experiments, just not conclusive value. As long as the results of such data mining are limited to generating new hypotheses (which are then tested by experiments explicitly designed to do so), I think this methodology can have great value. In this particular case, I don't think it's a surprise that perioperative hypertension is associated with worse outcomes, but the hypothesis that controlling BP with medication before surgery and on through recovery might produce better outcomes is worth investigating.
- chris_wot 11y agoSo my main take away is that scientist should publish their original data. Frankly, I'm amazed they don't already!
- drcross 11y agoCould someone please offer a TLDR; The article seems very clickbait and full of teases.
- harveywi 11y agoFrom the article: "In the case of the spinal cord injury data, Ayasdi’s TDA-driven approach mostly confirmed what researchers already knew: The drugs didn’t work." How this "Ayasdi" company's analysis probably works (based on "Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival" and the original "Mapper" paper "Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition"): They take point cloud data and connect each point with its neighbors (the distance metric that is used is probably domain-specific) to build a proximity graph that approximates a simplicial complex. As input to their algorithm, they also have one or more scalar functions defined on the point cloud data that contain information which is relative to the problem at hand. For example, each point could be a gene, and maybe the scalar function value at that gene could be probability of association with some disease, and the distance between two genes might be the Levenshtein distance between their genetic codes. With data in this form, they approximate the Reeb graph of one of the scalar functions, which is a sort of "data skeleton." They can do potentially interesting/useful things with it. The approximation of the Reeb graph reveals zero-cycles (connected components of the simplicial complex) and some one-cycles (handles/tunnels in the graph, sort of like holes in a donut). This "skeleton" of the data allows them to do a variety of things, such as segment the data into components that are (approximately) topologically "simple" (they do not contain any 1-cycles), identify local maxima/minima, find saddle points where forks in the data merge together, and locate "essential saddles" which constitute the high points and low points of handles/tunnels. They can also remove "topological noise", which helps them to separate spurious topological thingies from features that might be important. Their technique doesn't necessarily recover "true" topological information since a lot of what they do is approximate. There are actually more accurate techniques (e.g., simplicial homology, or fast Reeb graph algorithms) for getting an exact answer, albeit with potentially higher computational cost. Topological data analysis is a big field, and this Ayasdi company appears to mainly use this one approach (but I could be wrong). I think they are trying to lay claim to the term "topological data analysis" and get people with money excited about it.
- skywhopper 11y agoThe substance of the article is quite interesting, but the headline and premise--that it was a computer program and not humans who found the result--is ridiculous. The computer program did not collate and index the raw data and notes. The computer program did not choose the relevant inputs from the sum of all knowledge. And most importantly, the computer program did not write itself. Software is a tool that humans create and use, not an entity in itself. Even if you think true AI is near at hand, this article describes nothing of the sort. Houses are far easier to build with saws, hammers, and nails than by manipulating wood, earth, and metal with our bare hands, but that does mean the tools built the house.
- cousin_it 11y ago> And most importantly, the computer program did not write itself. Perhaps even more importantly, you didn't create yourself either :-)
- deleted 11y ago[deleted]
- Retra 11y agoWhether or not the program wrote itself is the least relevant aspect of its ingenuity.
- Dn_Ab 11y agoI agree that this wasn't done by the computer (did computers uncover the Higgs Boson?) but I also do not believe humans can take most of the credit: this was the result of a Man Machine System team up—trying to disentangle credit assignment is not a worthwhile activity. Roughly and from a quick reading of a paper thickly frosted with jargon I am unfamiliar with, the method works by creating networks—which highlight key relationships—for visualization by searching for stable clusters in a reduced dimensionality space of the variables. Humans are there to explore the visualizations, interpret the network structures and understand the clusters and variables. The machines are intelligent too; they do the heavy work of comparing large numbers of points in a high dimensional space, factorization and searching for a way to express the data in a manner that makes it easier to uncover promising research directions and hypotheses. Scanning this, it seems the most valuable contribution are their network visualization and exploratory tools. I think they should be proud of those and see no need to stretch so mightily to connect this to Stronger AI. As Vinge notes, "I am suggesting that we recognize that in network and interface research there is something as profound (and potential wild) as Artificial Intelligence." http://www.nature.com/ncomms/2015/151014/ncomms9581/full/ncomms9581.html http://www.nature.com/ncomms/2015/151014/ncomms9581/full/nco...
- meeper16 11y agoHidden relationship mining has taken a few different paths from TDA to LDA graphical modelling (Michael Jordan, David Blei) to Vector Space driven (Berkeley Lab). Extracting hidden relationships in datasets and using these to form new hypothesis and enable or even make new discoveries is certainly the future... Lawrence Berkeley National Laboratory vector space for hidden relationships: http://newscenter.lbl.gov/news-releases/2008/07/09/berkeley-lab-wins-four-2008-rd-100-awards/ http://newscenter.lbl.gov/news-releases/2008/07/09/berkeley-... A Search Engine that Thinks http://newscenter.lbl.gov/feature-stories/2005/03/31/a-search-engine-that-thinks-almost http://newscenter.lbl.gov/feature-stories/2005/03/31/a-searc... Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span - Blei DM1, Franks K, Jordan MI, Mian IS. - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1533868 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1533868
- Herodotus38 11y agoHere is the actual paper: http://www.nature.com/ncomms/2015/151014/ncomms9581/full/ncomms9581.html http://www.nature.com/ncomms/2015/151014/ncomms9581/full/nco... Coming from medicine I think the title of "medical breakthrough" is too generous. It's a great proof of concept but all this says is that in rats, high Bp in thoracic spinal cord injuries was associated with worse outcomes. Id like to see a follow up on human data from perioperative Bp recordings next. If it still holds true, then you can research whether an intervention in Bp control makes a difference. I'm not a neurosurgeon but I'm sure the correlation btw Bp and sci outcomes has been looked at before
- mtdewcmu 11y agoThe Nature paper uses the phrase "data-driven hypothesis generation," which sounds pretty accurate and consistent with what you're saying.
- jlnielson 11y agoPreliminary evidence has been found in humans, looking at the other end of the spectrum with hypotension. http://online.liebertpub.com/doi/10.1089/neu.2014.3778 http://online.liebertpub.com/doi/10.1089/neu.2014.3778 We are now looking at the hypertension relationship in humans, as well as mechanistic studies in rats.
- deleted 11y ago[deleted]
- fanofyan 11y agoI'm a frontend developer at Ayasdi and we're hiring!!! http://www.ayasdi.com/company/careers/ http://www.ayasdi.com/company/careers/ Come be a part of future breakthroughs.
- jrowley 11y agoI can't see your opportunities because the iframe embedded is having too many redirects. Just a heads up. :/
- xixi77 11y agoOne immediate question is whether this might be a result of overfitting -- when enough hypotheses are tested, some will surely be confirmed at any given significance level. Still quite interesting of course; now what is needed is a follow-up study on other datasets (preferrably human), or an experiment to confirm. A better (but less exciting) title would be "A computer program suggests a promising avenue of research".
- topologix 11y agoHey HN folks - I am the co-founder and CEO of Ayasdi. If you have questions about the math/CS aspects of this, happy to answer.
- pvnick 11y agoI'd love to read a couple of journal articles that you recommend to learn about TDA. I do large scale data analysis on health care data at my university and am always on the look-out for interesting techniques.
- topologix 11y agoGunnar wrote a review article a few years ago called Topology and Data (http://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01249-X/ http://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01...). It is an amazingly well written and accessible paper for a technical audience. Pair it with Afra's book (http://www.amazon.com/Computing-Cambridge-Monographs-Computational-Mathematics/dp/0521136091/ref=sr_1_1?ie=UTF8&qid=1444973590&sr=8-1&keywords=topology+for+computing http://www.amazon.com/Computing-Cambridge-Monographs-Computa...)
- pvnick 11y agoThank you!
- steamer25 11y agoDo you recommend any good primers on topology? I thought this (https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/) was an interesting article and I see what looks like some great papers and videos available at http://www.ayasdi.com/approach/data-scientist/ http://www.ayasdi.com/approach/data-scientist/, but I don't know the difference between homotopy and homology (yet) :) . What kinds of infrastructure/tech do you think will have the most utility for topological data analysis in the near future? E.g., GPUs, Apache Spark, FPGAs, etc. Any thoughts on an Ayasdi public offering? I'd like to consider investing but I don't have millions of dollars (yet) :) . Thanks for your time.
- findjashua 11y agomade possible
- jmpeax 11y ago"The process was outlined in a paper published today in Nature Communications". NO LINK TO THE PAPER ARGGGHH!!!!!
- xbmcuser 11y agoThings like this why I feel something like google deepmind can be a game changer for the sciences if the research data of all human research data was available to them. They might never reach the point of true AI but they would still beat all humans and finding relationships between data humans cant even remember.