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Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influenza

AlaskaDenise

AlaskaDenise

In Memoriam
1: Amino Acids. 2007 Nov 2; [Epub ahead of print]

Wu G, Yan S.
Computational Mutation Project, DreamSciTech Consulting, Shenzhen, Guangdong Province, China.

This is the continuation of our studies on the prediction of mutation engineered by randomness in proteins from influenza A virus. In our previous studies, we have demonstrated that randomness plays a role in engineering mutations because the measures of randomness in protein are different before and after mutations. Thus we built a cause-mutation relationship to count the mutation engineered by randomness, and conducted several concept-initiated studies to predict the mutations in proteins from influenza A virus, which demonstrated the possibility of prediction of mutations along this line of thought. On the other hand, these concept-initiated studies indicate the directions forwards the enhancement of predictability, of which we need to use the neural network instead of logistic regression that was used in those concept-initiated studies to enhance the predictability. In this proof-of-concept study, we attempt to apply the neural network to modeling the cause-mutation relationship to predict the possible mutation positions, and then we use the amino acid mutating probability to predict the would-be-mutated amino acids at predicted positions. The results confirm the possibility of use of internal cause-mutation relationship with neural network model to predict the mutation positions and use of amino acid mutating probability to predict the would-be-mutated amino acids.

PMID: 17973072 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/sites/e...Retrieve&list_uids=17973072&dopt=AbstractPlus

Hopefully "someone" can translate! :D

.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

"Hopefully "someone" can translate! "

Ditto!!!

What does this study get at? What does it mean? What does it say about the direction and quality of Chinese research in this area?

J.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Some of the statements seem contradictory to me. It looks like it could be a study to support a preconceived concept, while ignoring other possibilities.

.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Here is something like what I think they are doing:

http://www.msm.cam.ac.uk/phase-trans/2006/ga_html_files/ga.html

Basically, they isolate rules like restrictions on the location of a 'mutation' and its 'fitness', but including more than just these factors. Perhaps they have a rule like, "i before e except after c" that makes some kinds of mutations impossible. They then assign random weights and calculate all the possible combinations, then weed out the impossible ones, then figure out what rules turn out to be the essential ones.

Once they know what combinations of factors turn out to be the most important, then they can use that to predict, within limits, what the system will do.

This kind of analysis doesn't necessarily require that the mutations be random, only that the reasons for the mutations are obscure and the relevant drivers aren't known.

Do I need to add that if you don't include enough of the relevant factors, you won't get a reliable model?
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Do I need to add that if you don't include enough of the relevant factors, you won't get a reliable model?
Click to expand...

i.e., the whole unexplored field of epigenetics?


I'm less inclined to believe influenza operates on any mathematical model and more inclined to believe it responds to situations (unless the situations are the rules they are evaluating).

If the genetic sequences database is only about 100 years old, that may not be enough history to arrive at reliable conclusions. For instance, since man domesticated ducks about 4500 years ago, how can we evaluate that influence without a "before and after" data set?

.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Well, yes, in fact. But (without, of course, having actually read the article) I am thinking they will mainly discover that the physical shape of the surface proteins determines how will the 'key' will fit the 'lock', and that some mutations ruin the fit because they put a bend where it shouldn't be. So they are going to find out that there are only a few ways to make a key, that bad fits are rapidly eliminated, and that it's easier to keep a big pool of good subunits floating around than to constantly rewrite Shakespeare using monkeys. Ummmmm, but they will show it nicely with an expert system.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Dear All

It is by chance that we found that our recent paper is being discussed in this forum when we were searching through Yahoo.

We as authors ourselves are very happy to answer any question regarding this paper. From the discussion posted in this site, we would like to explain a little.

We use a cause-mutation relationship to predict the mutation, because the best way to predict the mutation is perhaps to find the cause for each mutation, as we could predict a mutation when its cause appears.

However, many causes, which led to historic mutations, left no trace because of huge changes in environments, thus we might have a relatively detailed record of mutations, but a poor record of their causes. Second, the current version of proteins may no longer be subject to the causes, which led to mutations in the past, because of evolution of proteins. Third, we have a great difficulty to find the microenvironment, under which the historic mutation causes functioned.

Therefore we consider using the randomness as a consistent cause engineering mutations, by which we build a cause-mutation relationship by coupling quantified randomness as cause and occurrence/non-occurrence of mutations as mutation to make the prediction.

We welcome any your questions on our approach, and we would like to invite you to visit our website, www.dreamscitech.com, which we just reconstructed, including our monitoring of evolution of hemagglutinins using our methods with the hope of predicting of mutations.

Regards

Guang Wu
Shaomin Yan
DreamSciTech
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Welcome Guang and Shaomin.

I have a question - a theoretical one.

If we can predict randomness then is it random? And if it is, then isn't it random because we do not yet know all the variables?
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

In prediction of mutation using mathematic tools, we must have an equation, say, ax + b = y, if y is the mutation, x must be something that have a value. We defined x with quantified randomness in order that x has some values.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

From your website:

"...The pattern in this figure highlights that a pandemic/epidemic generally follows the extreme point of quantified hemagglutinins....As can be seen in this figure, our current position is approaching to an extreme."

Interesting......
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Thanks!

In figure in www.dreamscitech.com, you can see the importance of quantifying a protein, that is, we need to use a single value to present a whole protein. This value should be subject to mutation, by such a value we can follow the historic path of hemagglutinins through a 2-dimensional graph, then we can approximately make prediction by pattern comparison, which we may call the dynamic approach.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Welcome Guang and Shaomin.

We appreciate you taking your valuable time to explain your research.

Your work sounds fascinating & should be a valuable contribution to the field.

It sounds as if it will provide valuable support the work of epigeneticists.

.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Neural Networks Versus Logistic Regression In Financial Modelling: A Methodological Comparison

Abstract: this paper is to very briefly examine two alternative methodologies which might be applied to decision making classification problems in corporate finance: logistic regression and neural networks. To achieve this end, the basic concepts underpinning each method are described. The nature of modelling the corporate financial decision making environment is then discussed. Suggested methodologies are then prescribed to aid those interested in using either or both techniques, and salient results...

http://citeseer.ist.psu.edu/73860.html
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Guang and Shaomin, thanks for taking the time to join FluTrackers and answer some questions about your recent article. Most of us here at FluTrackers do not have full access to journal articles, so I was wondering if you would briefly discuss your results regarding the timing of influenza outbreaks based on the research you published in 2005. Abstract:
Timing of mutation in hemagglutinins from influenza A virus by means of unpredictable portion of amino-acid pair and fast Fourier transform

Guang Wu, and Shaomin Yan
Computational Mutation Project, DreamSciTech Consulting, 301, Building 12, Nanyou A-zone, Jiannan Road, Shenzhen, Guangdong Province CN-518054, China
Received 22 April 2005. Available online 26 May 2005.


Abstract

In this study, we calculate the unpredictable portion of amino-acid pairs, which has been developed by us over the last several years, of 1201 hemagglutinins from influenza A viruses dated from 1918 to 2004 in order to compare them with respect to subtypes, species, and years. After noticing the fluctuations of unpredictable portion along the time course, we use the fast Fourier transform to find the mutation periodicity of hemagglutinins. Then we estimate our position at the current cycle of hemagglutinin evolutionary process to determine how many years remain before the next outbreak of influenza and bird flu. Finally, we use the trend line and channel to outlook the hemagglutinins for the next half a century. As our study covers almost all the full-length amino-acid sequences of hemagglutinins from various influenza A viruses, the conclusion will be valid for years until the number of hemagglutinins in protein databank will be significantly increased.


Keywords: Fast Fourier transform; Hemagglutinin; Influenza A virus; Mutation; Periodicity



Biochemical and Biophysical Research Communications
Volume 333, Issue 1, 22 July 2005, Pages 70-78
Click to expand...
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Fast Fourier transform

From Wikipedia, the free encyclopedia


Jump to: navigation, search
<!-- start content -->"FFT" redirects here. For other uses, see FFT (disambiguation).

A fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier transform (DFT) and its inverse. FFTs are of great importance to a wide variety of applications, from digital signal processing and solving partial differential equations to algorithms for quick multiplication of large integers. This article describes the algorithms, of which there are many; see discrete Fourier transform for properties and applications of the transform.

Let x<SUB>0</SUB>, ...., x<SUB>N-1</SUB> be complex numbers. The DFT is defined by the formula
<DL><DD></DD></DL>Evaluating these sums directly would take O(N<SUP>2</SUP>) arithmetical operations. An FFT is an algorithm to compute the same result in only O(N log N) operations. In general, such algorithms depend upon the factorization of N, but (contrary to popular misconception) there are FFTs with O(N log N) complexity for all N, even for prime N.

Many FFT algorithms only depend on the fact that is a primitive root of unity, and thus can be applied to analogous transforms over any finite field, such as number-theoretic transforms.

Since the inverse DFT is the same as the DFT, but with the opposite sign in the exponent and a 1/N factor, any FFT algorithm can easily be adapted for it as well.

<TABLE class=toc id=toc summary=Contents><TBODY><TR><TD>Contents

[hide]
</TD></TR></TBODY></TABLE><SCRIPT type=text/javascript>//<![CDATA[ if (window.showTocToggle) { var tocShowText = "show"; var tocHideText = "hide"; showTocToggle(); } //]]></SCRIPT>
The Cooley-Tukey algorithm

Main article: Cooley-Tukey FFT algorithm.
By far the most common FFT is the Cooley-Tukey algorithm. This is a divide and conquer algorithm that recursively breaks down a DFT of any composite size N = N<SUB>1</SUB>N<SUB>2</SUB> into many smaller DFTs of sizes N<SUB>1</SUB> and N<SUB>2</SUB>, along with O(N) multiplications by complex roots of unity traditionally called twiddle factors (after Gentleman and Sande, 1966).

This method (and the general idea of an FFT) was popularized by a publication of J. W. Cooley and J. W. Tukey in 1965, but it was later discovered that those two authors had independently re-invented an algorithm known to Carl Friedrich Gauss around 1805 (and subsequently rediscovered several times in limited forms).

The most well-known use of the Cooley-Tukey algorithm is to divide the transform into two pieces of size N / 2 at each step, and is therefore limited to power-of-two sizes, but any factorization can be used in general (as was known to both Gauss and Cooley/Tukey). These are called the radix-2 and mixed-radix cases, respectively (and other variants such as the split-radix FFT have their own names as well). Although the basic idea is recursive, most traditional implementations rearrange the algorithm to avoid explicit recursion. Also, because the Cooley-Tukey algorithm breaks the DFT into smaller DFTs, it can be combined arbitrarily with any other algorithm for the DFT, such as those described below.


(The article continues....)
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Dear All

Many thanks for your warm words and encouragements. Sorry for the delay in answering you because we were busy all the day, and until now we have time to see this website.

At first, let us talk several words regarding the logistic regression and neural network. As we wrote in our previous posts, assume that we have an equation ax + b = y for describing the cause-mutation relationship. We have said that y is the mutation, in fact, it is the occurrence/non-occurrence of mutation, which can be classified as unity and zero, a binary event. In such a case, on the left-hand side of equation we have continuous value, x, and on the right-hand side we have a binary event. This type of problem is the problem of classification, which can be solved using either logistic regression or neural network. At the beginning of our study on prediction of mutation, we used the logistic regression, which has an explicit form and works very fast. However, the logistic regression is not powerful enough, which is more suitable for concept-initiated study, and now we began to use the neural network, which is very time-consuming and does not have any explicit form. This is our current stage of studies.

With respect to the timing of mutation, we consider that the prediction of mutation should include (i) the prediction of mutation position, which we are trying to use the neural network to solve, (ii) the prediction of would-be-mutated amino acids at predicted positions, which we are trying to use the amino-acid mutating probability to solve, (iii) the timing of mutation, which we are trying to use the fast Fourier transform to solve, and (iv) the prediction of new function in mutant proteins.

The fast Fourier transform, in plain words, is a mathematic tool, which can find the periodicity in chaotic data, for example, we at first hope to find if there are periodicities in the figure in www.dreamscitech.com, whose periodicity is not easily to be seen in figure. When using the fast Fourier transform to treat the data from in figure, we can find that there are many periodicities. With the clear periodicities, we can time the mutations although there is still a long way to go.

Good weekend

Guang and Shaomin
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Hi, Laidback Al

We just realized that our explanation of timing of mutations is less understandable in our previous post, thus during the weekend we made two figures in our website, www.dreamscitech.com, to show the results of fast Fourier transform on the historical data of hemagglutinins, and how we stratify the historical data into several segments to time the mutations. Hopefully, these figures would be more understandable for timing of mutation.

Have a great week

Guang and Shaomin
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Thank you for posting the chart. I will spend some time studying it as will others who read this thread.
 
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Dear All

Happy Thanksgiving Day! Now we made the data for constructing the figure of evolution of hemagglutinins downloadable. You are kindly invited to download the data at www.dreamscitech.com to conduct your analysis to time the possible outbreak of influenza.

All the best

Guang and Shaomin
 
Last edited by a moderator:
Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Re: Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influe

Merry Christmas and Happy New Year! Everyone


This week, we found that we have finished the calculations of H5N1 for 2006, and found that there are only 5 new H5N1 sequences in influenza database in 2007. After finished the calculation of these five new sequences, the total H5N1 sequences in 2007 reached 50.

However, an important difference appears between the five new H5N1 sequences and previous 45 ones, that is, the mean +/- standard deviation are 39.03 +/- 0.96 for five new sequences but 37.60 +/- 1.08 for the previous 45 H5N1 sequences, p = 0.007, Mann-Whitney test.

Suggestive here is that the curve began to change its direction slowly, and direct to upwards. If the historical observation holds on, say, the outbreak occurred after extreme point (see the red cycles in figure), then a bird flu related to H5N1 appears beyond horizon. Of course, there is an important difference in historical figure, where we calculated all subtypes of hemagglutinins, but now we only calculated H5N1 hemagglutinins recently.

Predictable.GIF

pattern.GIF


Could the third pair be matched in the lower figure (the filled cycles are outbreaks)?

Guang and Shaomin:)
 
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