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  • richardmitnick 4:51 pm on October 27, 2014 Permalink | Reply
    Tags: , , , , Public Distributed Computing,   

    From Mapping Cancer Markers at WCG: “Early-stage results from the Mapping Cancer Markers team” 

    New WCG Logo

    27 Oct 2014
    The Mapping Cancer Markers research team

    The Princess Margaret Cancer Foundation Mapping Cancer Markers team has nearly finished establishing their benchmarks – a crucial step for their research and other related medical research around the world. See their in-depth update for the latest news about their efforts to help predict, identify and treat cancer.

    Summary
    Thanks to your help, the Mapping Cancer Markers team is nearly finished with benchmarking their first set of genetic markers. In this update, the team presents an in-depth review of what they’ve accomplished thus far, and what significance this early work will have for cancer research at their lab and elsewhere.

    The Mapping Cancer Markers (MCM) team would like to extend a huge thank you to World Community Grid members everywhere. As of October 27, 2014, we have surpassed 89,000 years of computation, a goal that simply would not be possible without your help.

    We are happy to report that we have begun to analyze the results using a high-throughput analytics package to assess the fitness and landscape of gene signature sizes between 5 and 25 genes. This analysis has shown that smaller signatures usually comprise different genes compared to larger signatures (i.e., you cannot “build” a larger signature from small ones), and that those genes are targeting many different signaling cascades and biological processes.

    Analytics

    To get a better understanding of how much data our team is receiving, we’d like to briefly introduce one of the tools that we have adopted to analyze the incoming results. From the very beginning of the project, it was clear that analyzing such a large, ongoing flow of data would be a challenge. Thus, we started to use the IBM® InfoSphere® Streams real-time analytics platform to streamline the analysis pipeline. When complete, our Streams application will run continuously, processing members’ work units in real time as we receive them. We currently have the core analysis framework implemented and running on a subset of the MCM results. We will continue to add additional layers of analysis, and fine-tune our system until it is running at full capacity. For that reason, we have dedicated one of our main compute servers (IBM Power® 780) to analyzing MCM results.

    Results

    Pictured below is a sampling (a very small fraction) of some of the ongoing work that will establish a benchmark for further experiments. Each dot in both of the graphs is a potential lung-cancer biomarker. These graphics are distilled from thousands of MCM results sent back by World Community Grid members.

    mar

    mar2

    Most of the dots have very little significance; this is expected because not everything shuts down or is activated in cancer. In other words, the graphics show differences between the disease state and the non-disease state, so we expect some things to be different, but not everything. For those reasons, most biomarkers cannot significantly differentiate cancer from non-cancer samples – this is represented by the haze of dots along the zero line. We show two graphs to illustrate the difference between shorter and longer gene signatures. Some genes that are more predictive in the shorter signature sizes do not necessarily hold their predictive power when considering more genes per signature. Most importantly, in each analysis, a few biomarkers frequently appear in high-scoring signatures. Our analysis wades through massive amounts of data to recognize those few markers that stand out.

    The first half of the “benchmarking” experiment involves determining the performance of markers as the size of the signature changes. For instance, when we compare successful 5-marker signatures against 20-marker signatures, which markers are consistently useful? Which ones increase or diminish in predictive power? Is there an optimum size for signatures? And most importantly, can we identify seemingly minor players that are critical, but not yet in clinical use that can discriminate between normal and disease states?

    graph

    After surveying the first several billion signatures, we have identified the highest-ranking combinations and underlying single genes. After separating those genes by signature size, we can see how some genes remain important regardless of the size, and how other genes “appear” to be important but are only showing up as single events. Considering we have not yet analyzed the complete data set, we have identified the genes by their known functions rather than names, to eliminate any bias towards known markers. However, even by their functions, we can see that many important signaling cascades and biological processes are affected. The most notable of these is “Cellular Fate and Organization”, which makes sense. Sometimes, when an organism loses the ability to naturally kill defective cells, it leads to uncontrolled growth, one of the hallmarks of cancer.

    Network Analysis of Major Genes:

    To further analyze the nature of our top-performing genes, we can identify their inter-relations in biological networks. We currently maintain one of the largest curated protein-protein interaction databases, which enables us to determine whether our genes (when converted to proteins) are known to interact with other important biomarkers, and in turn, what biological processes may be involved. The graph below shows one such network; nodes in the graph represent genes, edges are physical protein interactions. Node color highlights biological function as described in the legend. Use of biological networks can reveal very small subtleties of how the mechanisms of disease function and elucidate how our proteins may be causing problems; thus, eventually leading to understanding how cancer starts, progresses and how can we treat it.

    tre

    In the above network, 20 out of 24 important proteins we have identified on World Community Grid (right hand side) can be linked through known protein interactions and 56 other proteins (left hand side). We have also conducted a short analysis of the 4 proteins not yet identified using a software prediction package and found those to have significant partners. Those interactions will be evaluated in the near future. The 20 proteins noted above, strikingly, do not interact directly, however, 4 of them show very high interactivity, and can be considered as hubs. From other analyses we know that “hub proteins” are often critical, as they affect many signaling cascades and biological processes. When such proteins malfunction, catastrophic changes often result. On the other hand, proteins with low interactivity could be useful as clinical biomarkers. If they are known to only interact with a few other proteins, then their activity may help to identify particular states of cancer, while having less background “noise”. As a whole we can see that for the most part, our genes of interest are targeting mostly “genome maintenance” and “cellular fate and organization” proteins, which make up about 70% of the interacting proteins (left hand side). This is a good indication that most of the pathways affected are in those major categories, which is consistent with how we understand lung cancer to progress.

    Funding & Fundraising:

    This past August, we completed our 4th successful Team Ian Ride for Cancer Informatics Research. We were able to raise over $80,000 for cancer research in the name of a former Jurisica student, Ian Van Toch.

    Part of this funding is used for the best student paper award at the ISMB conference, and for supporting Cancer Informatics interns.

    We also support a special seminar series at Princess Margaret Cancer Center, and the recent presentation by Dr. Wan Lam from BC Cancer Agency discussed “Multi-dimensional Analysis of Lung Cancer Genomes”.

    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.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “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-

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    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
    sp

    ScienceSprings relies on technology from

    MAINGEAR computers

    Lenovo
    Lenovo

    Dell
    Dell

     
  • richardmitnick 5:38 am on July 26, 2014 Permalink | Reply
    Tags: , , OProject@home, Public Distributed Computing   

    OProject@home
    OProject@home

    OProject@Home is a research project that uses Internet-connected computers. You can participate by downloading and running a free program on your computer.

    The main idea of this project is the analysis of algorithms.

    project
    The sub-project Shor’s Algorithm is slowly going to an end

    The sub-project Shor’s Algorithm is slowly going to an end. Tasks will be still available for some time, so it will be possible to download them, calculate and return for the validation.

    Many users have complained about the lack of apps for Windows. Therefore, at the end we decided to add an Shor’s Algorithm application for Windows. The application is not yet available for this platform (because of some technical problems), but this should change soon.

    Thank you all for your help in the calculation. Also, thanks to your support we were able to create a paper: Simulation of a functional quantum system on parallel, classical IV generation computers (English title).

    Bachelor of Science thesis
    It was published my Bachelor of Science thesis: “Symulacja funkcjonalnego systemu kwantowego na równoległych komputerach klasycznych IV generacji”.

    English title: “Simulation of a functional quantum system on parallel, classical IV generation computers”.

    The thesis can be downloaded from the CEON repository – URL:
    https://depot.ceon.pl/handle/123456789/1923

    Thesis available only in Polish. 22 Jun 2013 | 10:52:01 UTC · Comment

    OProject@Home based at OLib Library. The library is open and available in the code.google.com SVN repository.

    If you are interested in helping with this project, visit BOINC, download and install the software on which the project is running. While you are at BOINC, look over some of the other projects to see what else you might find of interest.

    BOINC


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  • richardmitnick 10:16 pm on July 21, 2014 Permalink | Reply
    Tags: , , , , , Public Distributed Computing,   

    From WCG: “Pioneering a Molecular Approach to Fighting AIDS” 

    World Community Grid

    Dr. Arthur Olson
    Professor, The Scripps Research Institute
    21 Jul 2014

    Summary
    World Community Grid is being featured at the 20th International AIDS Conference which begins today in Melbourne, Australia. Dr. Arthur Olson, FightAIDS@Home principal investigator, shares his perspective on how World Community Grid is helping his team develop therapies and a potential cure for AIDS.

    The Scripps Research Institute’s FightAIDS@Home initiative is a large-scale computational research project whose goal is to use our knowledge of the molecular biology of the AIDS virus HIV to help defeat the AIDS epidemic. We rely on World Community Grid to provide massive computational power donated by people around the world to speed our research. The “virtual supercomputer” of World Community Grid enables us to model the known atomic structures of HIV molecules to help us design new drugs that could disrupt the function of these molecules. World Community Grid is an essential tool in our quest to understand and subvert the HIV virus’s ability to infect, spread and develop resistance to drug therapies.

    FightAidsOlsonLab@home

    Since the early 1980s – when AIDS was first recognized as a new epidemic and a serious threat to human health – our ability to combat the HIV virus has evolved. Using what we call “structure-based drug discovery,” researchers have been able to use information about HIV’s molecular component to design drugs to defeat it. Critical to this process has been our ability to develop and deploy advanced computational models to help us predict how certain chemical compounds could affect the HIV virus. The development of our AutoDock modelling application – combined with the computational power of World Community Grid – represents a significant breakthrough in our ability to fight HIV.

    By the mid 1990s, the first structure-based HIV protease inhibitors were approved for the treatment of AIDS. These inhibitors enabled the development of highly active antiretroviral therapy (HAART), which in turn resulted in a rapid decline of AIDS deaths where such treatment was available. In the intervening years, thanks in part to the U.S. National Institute of General Medical Sciences AIDS-related Structural Biology Program, we have learned a lot about the molecular structure of HIV. But the more we understand the structure of the virus, the more complex our computational models need to be to unlock the secrets of HIV.

    World Community Grid has enabled our research to progress well beyond what we could have dreamed of when we started our HIV research in the early 1990s. Through our FightAIDS@Home project, we can screen millions of chemical compounds to evaluate their effectiveness against HIV target proteins – including those known to be drug-resistant. By deploying these and other methods, we have significantly increased our understanding of HIV and its ability to evolve to resist treatment. Using these computational capabilities, we have just begun working with an HIV Cure researcher to help us move beyond treatment in search of a cure.

    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.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “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

    Computing for Sustainable Water

    Mapping Cancer Markers
    Mapping Cancer Markers Banner

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

    IBM – Smarter Planet
    sp


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 8:57 pm on July 20, 2014 Permalink | Reply
    Tags: , , , , , Public Distributed Computing,   

    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


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  • richardmitnick 8:17 pm on July 20, 2014 Permalink | Reply
    Tags: , , , CAS@home, Public Distributed Computing   

    From CAS@home 

    CAS@home
    CAS@home is hosted by the Computing Centre of the Institute of High Energy Physics (IHEP), Chinese Academy of Sciences. CAS@home is a volunteer computing platform for Chinese scientists based on the BOINC volunteer computing software. CAS@home collects the volunteer contributions to computing resources for scientists at the Chinese Academy of Sciences and other Chinese research institutions, to provide massive free computing resources that help the scientists complete major scientific computing tasks. Therefore, CAS@home supports multiple applications. The first application to be launched on CAS@home was developed by scientists at the Institute of Computing Technology (ICT), Chinese Academy of Sciences. It focused on protein structure prediction application (software called SCThread). In addition, Tsinghua University’s Centre for Micro and Nano Mechanics (CNMM) an interdisciplinary innovation research center, has prepared an application for simulating flow of fluids and motion of solids on the nanoscale. Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, is preparing an application for gene sequencing for applications in cancer research. And physicists of the Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, are preparing an application for simulating particle collisions at the Beijing Electron Positron Collider, based on software called BOSS.

    If you wish to participate in this project, download and install the BOINC software upon which it runs. Then attach to the project. While you are at BOINC, look over some of the other projects. You might find them to be of interest.

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 7:46 pm on July 20, 2014 Permalink | Reply
    Tags: Asteroids@home, , , , , , Public Distributed Computing   

    From Asteroids@home: “New worlds in our solar system” 

    Asteroids@home

    Asteroids@home is a research project that uses Internet-connected computers to do research in Asteroids@home. You can participate by downloading and running a free program on your computer.

    Asteroids are the most numerous objects in the solar system. So far, hundreds of thousands of asteroids are known, with hundreds of new discoveries every day. Although the total number of known asteroids is large, very little is known about the physical properties of individual objects. For a significant part of the population, only the size of the bodies is known. Other physical parameters (the shape, the rotation period, direction of the rotation axis,…) are known only for hundreds of objects.

    Because asteroids have in general irregular shapes and they rotate, the amount of sunlight they scatter towards the observer varies with time. This variation of brightness with time is called a lightcurve. The shape of a lightcurve depends on the shape of asteroid and also on the viewing and illumination geometry. If a sufficient number of lightcurves observed under various geometries is collected, a unique physical model of the asteroid can be reconstructed by the lightcurve inversion method.

    The project Asteroids@home was started with the aim to significantly enlarge our knowledge of physical properties of asteroids. The BOINC application uses photometric measurements of asteroids observed by professional big all-sky surveys as well as ‘backyard’ astronomers. The data is processed using the lightcurve inversion method and a 3D shape model of an asteroid together with the rotation period and the direction of the spin axis are derived.

    Because the photometric data from all-sky surveys are typically sparse in time, the rotation period is not directly ‘visible’ in the data and the huge parameter space has to be scanned to find the best solution. In such cases, the lightcurve inversion is very time-consuming and the distributed computation is the only way how to efficiently deal with photometry of hundreds of thousands of asteroids. Moreover, in order to reveal biases in the method and reconstruct the real distribution of physical parameters in the asteroid population, it is necessary to process large data sets of ‘synthetic’ populations.

    To participate in this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects running on this software. You might find some of them to be of interest.

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 11:18 am on July 19, 2014 Permalink | Reply
    Tags: , , , , , , , Public Distributed Computing,   

    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

     
  • richardmitnick 11:05 am on July 18, 2014 Permalink | Reply
    Tags: , , , , Leiden Classical, Public Distributed Computing   

    From Leiden Classical 

    Leiden

    Leiden Classical

    Join in and help to build a Desktop Computer Grid dedicated to general Classical Dynamics for any scientist or science student!

    Leiden Classical is a distributed computing project run by the Theoretical Chemistry Department of the Leiden Institute of Chemistry at Leiden University. Leiden Classical is part of the BOINC system, and enables scientists or science students to submit their own test simulations of various molecules and atoms in a classical mechanics environment. ClassicalDynamics is a program (and with it a library) completely written in C++. The library is covered by the LGPL license and the main program is covered by the GPL.

    To join this project, download and install the BOINC software. Then attach to the project. While you are at BOINC, look at the other projects to see if you might find any others of interest to you.


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  • richardmitnick 9:00 am on July 18, 2014 Permalink | Reply
    Tags: , , , Malariacontrol.net, , Public Distributed Computing   

    From Malariacontrol.net 

    Malariaconrtrol

    Malariacontrol.net

    What is malariacontrol.net?

    The malariacontrol.net project is an application that makes use of network computing for stochastic modelling of the clinical epidemiology and natural history of Plasmodium falciparum malaria.

    Simulation modeling of malaria

    The fight against malaria was given a new impetus by the call for eradication at the Gates Malaria Forum in October 2007, making more but still limited resources available for research, development, and combating malaria. To inform decisions on which new or existing tools to prioritize, we have developed a general platform for comparing, fitting, and evaluating stochastic simulation models of Plasmodium falciparum malaria, programmed in C++ (openmalaria).

    We use this to inform the target product profiles for novel interventions like vaccines, addressing questions such as minimal efficacy and duration of effects needed for a vaccine to be worthwhile, and also to optimize deployment of established interventions and integrated strategies. Field trials of interventions consider effects over 1-2 years at most, but the dynamics of immunity and human demography also lead to longer term effects. We consider many different outcomes, including transmission reduction or interruption, illness, hospitalization, or death, as well as economic aspects.

    Malaria occurs in an enormous variety of ecological settings, and interventions are not always universally applicable. For instance, indoor residual spraying works only with indoor-resting mosquitoes, and insecticide treated mosquito nets only with nocturnal vectors. The best combinations of interventions vary, as do optimal delivery approaches and their health system implications. There are trade-offs between high coverage and costs or feasibility of deployment. Indiscriminate deployment may lead to evolution of drug resistance or insensitivity to other interventions. To support the analysis of these elements we are assembling databases of health system descriptions, intervention costing, and vector bionomics across different malaria ecotypes.

    Uncertainties inherent in simulations of complex systems are addressed using probabilistic sensitivity analyses, fitting multiple different models, and basing predictions on model ensembles not single simulations. This requires super-computing, both for statistical fitting (which must simultaneously reproduce a wide range of outcomes across different settings), and for exploring predictions. We obtain this computing power over the internet from spare capacity on the computers of volunteers (malariacontrol.net).

    Meetings with potential users of these predicitons are used to promote the models and their predictions to wider communities of malariologists, planners, and policy specialists. We are also developing web-based job submission and analysis systems to increase internet access to models.

    To help in this fight, download and install BOINC software, attach to the project. While you are at BOINC, look over the other projects to see where else you might find interest and be of service.

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 8:05 am on July 18, 2014 Permalink | Reply
    Tags: , , GPUgrid.net, , Public Distributed Computing   

    From GPUGRID.net 

    GPUgrid.net
    GPUgrid.net

    Do real science, at home.

    Volunteer computing for biomedicine

    GPUGRID.net is a distributed computing infrastructure devoted to biomedical research. Thanks to the contribution of volunteers, GPUGRID scientists can perform molecular simulations to understand the function of proteins in health and disease.

    GPUGRID processes data on nVidia and AMD GPU processors. To join this project, download and install the BOINC software and attach to the project. While you are at BOINC, look over the other projects to find some of interest to you.

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
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