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  • richardmitnick 7:38 pm on October 2, 2014 Permalink | Reply
    Tags: , , , Mapping Cancer Markers,   

    From WCG: “Global PC network gives researchers supercomputer power” 

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    Sep 21 2014
    Joseph Hall

    Igor Jurisica wants you to help him conquer cancer.

    Oh, don’t worry, the Princess Margaret Cancer Centre scientist is not looking for money.

    But he would like to borrow your computer.

    In the age of molecular medicine, with its staggering genetic complexity, much cutting-edge cancer research has become a game of brute computational number crunching.

    And with access to laboratory supercomputers scarce and expensive, Jurisica has turned to a massive network of home and business PCs to run his research algorithms.

    “It’s basically a network of workstations around the globe,” says Jurisica, a computational biologist at the hospital and a University of Toronto professor.

    “When you’re not using your machine (it) can be donated for the project.”

    Known as the World Community Grid, the IBM-run network has gathered some 676,000 businesses and individuals globally who have volunteered about 2.9 million computers of varying capacities to help run scientific studies.

    Some 13,000 Canadian volunteers are currently donating time on about 67,000 devices.

    Begun last November, Jurisica’s Mapping Cancer Markers project has been granted access to about one-third of the machines worldwide, which gives him some 258 computer processing unit (CPU) years worth of power to run his data each day.

    jm
    Igor Jurisica is using global network of computers to discover more precise cancer treatments. Andrew Francis Wallace / Toronto Star

    That means a typical computer would have to run continuously for 258 years to process the data the network can work through in 24 hours.

    In aggregate, the full grid can generate more than 400 CPU years each day, which would rank it among the world’s 15 largest supercomputers, said Viktors Berstis, the senior IBM software engineer who runs the network.

    “When you have these big data problems, you have big processing problems to go with them,” Berstis said.

    “And so these kinds of projects that take many tens of thousands of years of CPU time are so massive that only the biggest supercomputers can handle them.”

    Again the problem, Berstis said, is that an institution with a supercomputer must typically divvy up access to it among hundreds or thousands of competing researchers.

    “So no one researcher gets that supercomputer 24/7 for several years on end which is the equivalent of what we’re giving (them),” he said.

    “They are getting something extremely rare and they are getting it for free.”

    The grid, which is eager for more volunteers, is run through a Toronto-based central processor that accesses home and business computers when the donors are not using them, Berstis said.

    It’s available for downloading to anyone who has a computer or Android device running Windows, Mac or Linux systems by going to the grid site and clicking the join link.

    That downloads a program to the home or business computer which will run in the device’s background at the lowest priority, Berstis said.

    “The instant your computer has nothing else left to do for you, then it can work a piece of this big research problem,” he said.

    “We try to make this software very unobtrusive so it doesn’t bother anything else.”

    Volunteers can donate their unused capacity in a number of ways, even allowing project computing to be done in the microseconds between key strokes.

    Member machines contact the central Toronto processor when they’re ready for work and are sent a tiny portion of a project problem.

    The worked information is then sent back to the server where it is checked for accuracy and cobbled together with all the other incoming data.

    Berstis said the grid code has been scoured line by line by IBM programmers for potential security problems and is likely to be the safest piece of software on any machine.

    He said the network also boasts environmental benefits.

    “When you have a supercomputer centre you have to have an air conditioning system that is almost as powerful as the computer to cool it back down so that the building doesn’t melt,” he said.

    The IBM grid is similar to one used by the earlier SETI — or Search for Extraterrestrial Intelligence — project, which linked millions of home computers to help scan the heavens for alien signals.

    Grid volunteers can also download screensavers that relate to the science project — there are currently three — that their computers are helping to crunch.

    Jurisica’s cancer marker project is the largest of these and is looking to discover the genetic and molecular signatures of lung, prostate, ovarian and sarcoma cancers — a search of stupefying complexity.

    When the Human Genome Project released its map of our species’ DNA more than a decade ago, it opened the door to the possibility of personalized medicine, where an individual’s cancer or heart disease could be diagnosed and treated according to its specific genetic signatures.

    Unfortunately, the genome project also opened a Pandora’s box of complexity in medicine with the realization that any single gene could be run or influenced by a mesmerizing array of other genetic materials and their protein products.

    And an individual’s complex cancer signatures, for example, would determine whether their disease could be detected early or would respond to given therapies.

    Jurisica said, however, that one cancer biopsy may now generate some 40,000 potentially involved variables. That means finding a set of signatures for any particular cancer — and there may be dozens across the patient population — could be a daunting exercise.

    In its search for such signatures — or markers — the Princess Margaret project has so far used up more than 81,000 CPU years of computation.

    Berstis said IBM began building the service a decade ago as one of its “Good Citizen’s Projects” and that researchers are selected on the scientific value of their proposals.

    [Correct certain inaccuracies: First, SETI@home is still running. Second, no mention was made that all of WCG runs on BOINC software from the Space Science Lab at U.C. Berkeley. Most important, long past is the day when WCG ran only when a computer was idle or took last position in what was running. All of that was true when BOINC and WCG were much younger and home computers had little of today's power. While you can calibrate down how much CPU and memory are used, there is little need to with quad core and hyper threaded dual-core processors. Just know that the BOINC process develops a great deal of heat which must be dissipated.]

    See the full article here.

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.


    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
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    ScienceSprings relies on technology from

    MAINGEAR computers

    Lenovo
    Lenovo

    Dell
    Dell

     
  • richardmitnick 8:57 pm on July 20, 2014 Permalink | Reply
    Tags: , , , Mapping Cancer Markers, , ,   

    From Mapping Cancer Markers at WCG: “Project roadmap and first phase results from the Mapping Cancer Markers team” 

    Mapping Cancer Markers

    Mapping Cancer Markers Banner

    Mapping Cancer Markers

    By: The Mapping Cancer Markers research team
    10 Jul 2014

    Summary
    The lead researcher for Mapping Cancer Markers presents a roadmap for the project to analyze signatures for 4 types of cancer: lung, ovarian, prostate and sarcoma; an update on his team’s progress thus far, and an invitation to join the research team in an August cancer fundraiser.

    On behalf of the Mapping Cancer Markers team, we want to start by saying thank you! In just 7 months, World Community Grid members have donated over 60,000 years of processing time to support our research. As a result, we are nearly done with the “benchmarking” portion of the project, which determines the characteristics of our search space. Over the coming months and years, we will pursue more targeted approaches to discover relevant gene signatures. Today we want to give you both a high-level roadmap and some further detail about what is happening with the project.

    Project roadmap

    The project is anticipated to run for two years, and we plan to analyze signatures for 4 different types of cancer. At the moment, we’re enlisting your help to process research tasks for lung cancer, and will move on to ovarian cancer, prostate cancer and sarcoma.

    Currently, the Mapping Cancer Markers project has two phases:

    In the first phase we have been attempting to set a benchmark for further experiments.
    The second phase will be geared towards finding clinically useful molecular signatures, initially focusing on gene signatures that can predict the occurrence of various types of cancer.

    We expect a smooth transition between the two phases, with no interruption in work. The “benchmarking” phase of our project is important not only for our own research, but for other researchers around the world. Every year, numerous groups worldwide develop and publish interesting molecular signatures for various diseases, including multiple cancers. One of the challenges of interpreting these findings is that many of the reports are not directly comparable to each other. The benchmarking phase of our project is designed to set a standard benchmark so that we and other groups can estimate how well individual signatures perform.

    You can think of this benchmarking phase as a bit like designing an IQ test. By establishing a standard test and scoring system, we can evaluate any person’s intelligence. The results from the first phase of Mapping Cancer Markers will allow us to create such a test for existing and future gene signatures, so that we can tell which ones have the best predictive ability.

    Benchmarking

    Our preliminary analysis of the work units processed so far (roughly 26 billion gene signatures) is focused on the nature of genes in the signatures, measuring their quality by assessing how accurately they contribute to identifying patients with poor prognosis. On the analytics side, we have also been evaluating the use of a software package to aid with post-processing our results.

    One of the goals of the first project phase is to understand if some genes might have better predictive ability than others. To do this, we took the top 0.1% of the gene signatures and identified the individual genes that make up each signature. For each gene, we looked at how many times it occurred within top scoring signatures and plotted the scores of those signatures (see figure below). The blue line shows the average of all of the genes together. The red line highlights the worst-performing single gene while the green line indicates our best-performing gene. The average of all the genes is very similar to the worst single gene. This is not surprising, because most genes are likely to have poor predictive ability. However, we are looking for the few genes that stand out from the field. In other words, if we have 1 million potential gene signatures, and we look at the top 1,000 scoring signatures, we can find groups of genes such as the one shown in green, which have better predictive ability.

    This information is important because if we know which genes have the best predictive ability, it may help us and other researchers to evaluate the value of other signatures: if an unknown signature has one of the top genes in it, it is likely to be a useful signature for identifying, assessing, predicting or treating a disease.

    As a side note, this benchmarking process is why members may have experienced shorter or longer than usual runtimes over the past several months. The core algorithm of the Mapping Cancer Markers engine, used to evaluate each potential gene signature, has a processing time that is highly dependent on the statistical characteristics of each signature. The search space targeted by a single work unit can sometimes contain time-consuming signatures, which together lead to a longer total runtime. This also means variability with the size of Mapping Cancer Markers results. A typical work unit will evaluate tens of thousands of potential gene signatures, many of which are of low quality. Signatures below a certain quality threshold are removed from the returned results. However, the search space targeted by a single work unit can sometimes contain a high proportion of high-quality gene signatures. If this happens, the result file is larger than usual.

    Funding & Fundraising

    We’re happy to report that there are several potential sources for further funding. Applications are in progress with the Ontario Research Fund, the Canada Foundation for Innovation, and the US Department of Defense. Of course, the free computing power provided by World Community Grid volunteers is absolutely essential to our research. However, additional funding will help us to both leverage contributions from volunteers, and fully utilize findings of the Mapping Cancer Markers computations, with a primary focus on lung and ovarian cancer.

    Finally, if you will be in Ontario between 15-17 August, please consider donating to, or cheering on the Team Ian Ride from Kingston to Montreal, which raises money for the Ian Lawson Van Toch Cancer Informatics Fund at the Princess Margaret Cancer Centre (if you are interested, please contact us about joining the Team Ian ride this or next year). If you can join us, it will give you the chance to meet some of the research team, as well as raise money for a worthy cause and participate in an outstanding event. For more details visit:http://www.team-ian.org/

    Cancers, one of the leading causes of death worldwide, come in many different types and forms in which uncontrolled cell growth can spread to other parts of the body. Unchecked and untreated, cancer can spread from an initial site to other parts of the body and ultimately lead to death. The disease is caused by genetic or environmental changes that interfere with biological mechanisms that control cell growth. These changes, as well as normal cell activities, can be detected in tissue samples through the presence of their unique chemical indicators, such as DNA and proteins, which together are known as “markers.” Specific combinations of these markers may be associated with a given type of cancer.

    The pattern of markers can determine whether an individual is susceptible to developing a specific form of cancer, and may also predict the progression of the disease, helping to suggest the best treatment for a given individual. For example, two patients with the same form of cancer may have different outcomes and react differently to the same treatment due to a different genetic profile. While several markers are already known to be associated with certain cancers, there are many more to be discovered, as cancer is highly heterogeneous.

    Mapping Cancer Markers on World Community Grid aims to identify the markers associated with various types of cancer. The project is analyzing millions of data points collected from thousands of healthy and cancerous patient tissue samples. These include tissues with lung, ovarian, prostate, pancreatic and breast cancers. By comparing these different data points, researchers aim to identify patterns of markers for different cancers and correlate them with different outcomes, including responsiveness to various treatment options.

    This project runs on BOINC software. Visit BOINC or WCG, download and install the software and attach to the project. While you are at BOINC and WCG, look over the other projects for some that you might find of interest.

    WCG

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 11:18 am on July 19, 2014 Permalink | Reply
    Tags: , , , , , Mapping Cancer Markers, , ,   

    Mapping Cancer Markers From WCG 

    Mapping Cancer Markers

    Mapping Cancer Markers Banner

    Mapping Cancer Markers

    Cancers, one of the leading causes of death worldwide, come in many different types and forms in which uncontrolled cell growth can spread to other parts of the body. Unchecked and untreated, cancer can spread from an initial site to other parts of the body and ultimately lead to death. The disease is caused by genetic or environmental changes that interfere with biological mechanisms that control cell growth. These changes, as well as normal cell activities, can be detected in tissue samples through the presence of their unique chemical indicators, such as DNA and proteins, which together are known as “markers.” Specific combinations of these markers may be associated with a given type of cancer.

    The pattern of markers can determine whether an individual is susceptible to developing a specific form of cancer, and may also predict the progression of the disease, helping to suggest the best treatment for a given individual. For example, two patients with the same form of cancer may have different outcomes and react differently to the same treatment due to a different genetic profile. While several markers are already known to be associated with certain cancers, there are many more to be discovered, as cancer is highly heterogeneous.

    Mapping Cancer Markers on World Community Grid aims to identify the markers associated with various types of cancer. The project is analyzing millions of data points collected from thousands of healthy and cancerous patient tissue samples. These include tissues with lung, ovarian, prostate, pancreatic and breast cancers. By comparing these different data points, researchers aim to identify patterns of markers for different cancers and correlate them with different outcomes, including responsiveness to various treatment options.

    This project runs on BOINC software. Visit BOINC or WCG, download and install the software and attach to the project. While you are at BOINC and WCG, look over the other projects for some that you might find of interest.

    WCG

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
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