I am interested to know which online tool is the best for the classification of genetic variants according to ACMG standards and guidelines?
Free access for everyone and showing literature-based pieces of evidence are the most important criteria since the tool must be reliable.
Thank you all for your comments
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I've follwed recent DL's proceed from NIPS.
Although i haven't track what happens in Computational Neuroscience(CN)'s field.
But I wonder why so little work about general artificial intellgence(GAI)?for example, kinds of network of hebbian to build all supervised/unsupervised/Reinforcement learning.
Or Can anyone tell me what's the state of art about neural networks for GAI, from CN field?Or some review?
Thanks a lot!
All three fields, Deep Learning (e.g. what you read at NIPS), Artificial general intelligence (AGI) and Computational Neuroscience are separate fields with some overlap but not too much overlap.
A question like 'what is the state-of-the-art' cannot really be answered in such a general form and would also soon be outdated. You have to be more specific. But just check some sources in the specific field. Wikipedia might be a good start.
I'm trying to find a good high level explanation of how statistical machine translation works. That is, supposing I have a corpus of non-aligned English, French and German texts, how could I use that to translate any sentence from one language to another ? It's not that I'm looking to build a Google Translate myself, but I'd like to understand how it works in more detail.
I've seen searched Google but come across nothing good, it either quickly needs advanced mathematics knowledge to understand or is way too generalized. Wikipedia's article on SMT seems to be both, so it doesn't really help much. I'm skeptical that this is such a complex area that it's simply not possible to understand without all the mathematics.
Can anyone give, or know of, a general step-by-step explanation of how such a system works, targeted towards programmers (so code examples are fine) but without needing a mathematics degree to understand ? Or a book that's like this would be great too.
Edit: A perfect example of what I'm looking for would be an SMT equivalent to Peter Norvig's great article on spelling correction. That gives a good idea of what it's involved in writing a spell checker, without going into detailed maths on Levenshtein/soundex/smoothing algorithms etc...
Here is a nice video lecture (in 2 parts):
http://videolectures.net/aerfaiss08_koehn_pbfs/
For in-depth details, I highly advise this book:
http://www.amazon.com/Statistical-Machine-Translation-Philipp-Koehn/dp/0521874157
Both are from the guy who created the most widely used MT system in research. It covers all the fundamental stuff, is very well explained and accurate. This probably one of the de-facto standard books that any researcher beginning in this field should read.
The Atlantic Online had a very straightforward nontechnical description of statistical machine translation back in December 1998:
Lost in Translation by Stephen Budiansky
I've read nontechnical stuff on statistical MT before but always wondered "yeah but how does the statistical stuff know which words map to which when word orders vary and supposedly no dictionary and no grammar are used?" Well this article actually does answer that and it's simple and straightforward and I was quite surprised.
A Peter Norvig talk from Google Developer Day 2007, Theorizing from Data: Avoiding the Capital Mistake, contains some accessible high-level explanation of the principles of statstical machine translation (starting from about 21:20).
We all hear that math at least helps a little bit with programming. My question though, does English or other natural language skills help with programming? I know it has to help with technical documentation, but what about actual programming? Are certain constructs in a programming language also there in natural languages? Does knowing how to write a 20 page research paper help with writing a 20k loc programming project?
Dijkstra went so far as to say: "Besides a mathematical inclination, an exceptionally good mastery of one's native tongue is the most vital asset of a competent programmer."
Edit: yes, I'm reasonably certain he was talking about the programming part of the job. Here's a bit more complete quote:
The problems of business administration in general and database management in particular are much too difficult for people who think in IBMerese, compounded by sloppy English.
About the use of language: it is impossible to sharpen a pencil with a blunt axe. It is equally vain to try to do it with ten blunt axes instead.
Besides a mathematical inclination, an exceptionally good mastery of one's native tongue is the most vital asset of a competent programmer.
From EWD498.
I certainly can't speak for Dijkstra, but I think it's impossible to cleanly separate the part where you're doing actual programming from the part where you're interacting with people. Just for example, even when you're working alone, it's crucial that you're able to understand (clearly and unambiguously) notes you wrote down about what to do, the nature of a bug, etc. A good command of English is necessary even when nobody else is involved at all (and, of course, that's unusual except on trivial tasks).
I don't know about causality, but the skill set required to write well overlaps quite a bit with those required for programming: knowing how to plan, being able to keep a myriad of details consistent, being able to make things clear for a future reader, knowing how to organize your thoughts and the resultant product. That isn't to say that a successful author would make a good programmer, but a programmer with good language skills and the same logic/math/deductive skills is probably a better programmer than one with poor language skills -- at least the code has a greater chance of being understandable.
Yes. Strong natural language skills help you to organize your thoughts in a coherent way that can easily be understood by others. That can help improve your code in everything from naming variables, methods, classes, etc., to expressing the contexts of objects in your model. Practices such as pair programming require you to be able to communicate well with your partner in order to write good code. Techniques such as Domain Driving Design emphasize using the domain language of the business in your code. Natural language skills facilitate that. And there is a strong drive in the development industry toward more natural language-like tools, e.g. many of the newer testing tools like rspec, gherkin, etc., are moving toward more natural language-like syntax. One of the things many people like about dynamic languages like Ruby and Python are that the code tends to read more like a natural language.
Let me state what should be the obvious: every healthy person above 12 knows at least one natural language. Moreover, every healthy person above 12 is able to generate and parse natural language a complex and rich language, and express and understand an extremely large set of ideas. In general, people are not likely to be limited in their ability to discuss issues by their language, but by the type of things they experienced and learned.
Having said that, there are several language-related skills that you might have thought about.
Writing style. You mentioned those specifically. Written language is different from spoken language. Way less intuitive. This is one reason people have to get coached in writing through their years in the education system.
Coding doesn't really involve writing. I mean, there's comments, but they can be rather laconic. Of course the work of a programmer usually involves at least some writing of documents, and writing abilities to make a difference there.
Analytical skills. Analytical skills are a complicated (not to say fuzzy) concept. Analytical skills aren't really about language, but insomuch they are taught and tested at all, it's in the context of writing essays.
Analytical skills are obviously very important in programming. I am not sure that these are exactly the same skills required to write a good essay about Euthanasia or whatever, but as was previously suggested, they may be related.
Foreign language. For people whose native language isn't English, a certain command of English may be needed. Not in the coding itself (knowing what "while" means in English isn't really critical to understanding what it does in Java), but because much training and support material is available mainly in English (did anyone mention Stack Overflow?). The English requirement may differ on the country you are in, and the company you work for, though.
Communication Skills. Ahhm. I was never exactly sure what this means exactly. Maybe it's a cultural thing. I do suspect it's less about knowing a language and more about knowing people.
So to some up, Dijkstra is a venerable computer scientist, but I am not sure he knew that much about language.
Programming isn't just about writing code. On any programming project of any size there will be the need for:
initial project proposal documents
design and architectural documents
programmers manual
users manual
training materials
communication with third party suppliers
etc.
On every big project I've worked on I'd guess I spent at least 50% of my time on the English language documents. So yes, an ability to explain and express yourself well is extremely important. Does it lead to writing better code? Once again, I would say yes - the need to provide clear documentation spills over into the need to write better code, itnerfaces et al.
Has anybody seen a GP implemented with online learning rather than the standard offline learning? I've done some stuff with genetic programs and I simply can't figure out what would be a good way to make the learning process online.
Please let me know if you have any ideas, seen any implementations, or have any references that I can look at.
Per the Wikipedia link, online learning "learns one instance at a time." The online/offline labels usually refer to how training data is feed to a supervised regression or classification algorithm. Since genetic programming is a heuristic search that uses an evaluation function to evaluate the fitness of its solutions, and not a training set with labels, those terms don't really apply.
If what you're asking is if the output of the GP algorithm (i.e. the best phenotype), can be used while it's still "searching" for better solutions, I see no reason why not, assuming it makes sense for your domain/application. Once the fitness of your GA/GP's population reaches a certain threshold, you can apply that solution to your application, and continue to run the GP, switching to a new solution when a better one becomes available.
One approach along this line is an algorithm called rtNEAT, which attempts to use a genetic algorithm to generate and update a neural network in real time.
I found a few examples by doing a Google scholar search for online Genetic Programming.
An On-Line Method to Evolve Behavior and to Control a Miniature Robot in Real Time with Genetic Programming
It actually looks like they found a way to make GP modify the machine code of the robot's control system during actual activities - pretty cool!
Those same authors went on to produce more related work, such as this improvement:
Evolution of a world model for a miniature robot using genetic programming
Hopefully their work will be enough to get you started - I don't have enough experience with genetic programming to be able to give you any specific advice.
It actually looks like they found a way to make GP modify the machine code of the robot's control system during actual activities - pretty cool!
Yes, the department at Uni Dortmund was heavily into linear GP :-)
Direct execution of GP programs vs. interpreted code has some advantages, though in these days you'd probably rather want to go with dynamic languages such as Java, C# or Obj-C that allow you to write classes/methods at runtime while still you can still benefit from some runtime rather than run on the raw CPU.
The online-learning approach doesn't seem like anything absolutely novel or different from 'classic GP' to me.
From my understanding it's just a case of extending the set of training/fitness/test cases during runtime?
Cheers,
Jay
On stack overflow, I see that there is referred to Wikipedia a lot. However, I'm often not sure whether they are the definite authority for very specific software development related concepts. For example, I have recently looked for definitions of the terms web server/service and RPC/IPC, and the responses I get very often refer to Wikipedia (directly and indirectly).
Hence my question: which sources do you trust the most for definitions of software development jargon?
http://www.google.com
And no, this isn't being tongue-in-cheek.
Personally I used to trust Wikipedia, and I still read it to get an idea about the subject. But definitely books are better choice. Because they not only have a "compressed" explanation but also provide an examples and give broader description. As professors of my university say, don't trust wikipedia, search for an authorized source. For example a huge information about web service technology you can find in the book Building Web Services with Java - Making Sense of XML, SOAP, WSDL, UDDI - 2nd Edition 2005. It contains information you'll never find in Wikipedia or even in Google (Unless you'll find this book using it ;) ).
Hope this helps.
Google and technical & non-technical software development books.
"A Story Culture" may be a useful read for you as you want something other than a dictionary, IMO. You want something with the knowledge and wisdom of the topic rather than simply what does this mean. For example, there are a couple of blog posts about Technical Debt that I really like to use for reference about the subject, one from Steve McConnell and one from Martin Fowler.
While I can generally suggest going to the source for the term, there is something to be said for a term getting overloaded or overused so that it can have little meaning. There are a few folks' blogs that I can say I trust to get some understanding on a subject including Joels and Jeffs, but don't forget that each of us has a brain and we shouldn't be afraid to use it.