Bug Throws Wrench into Google Image Searches

Computer users in many parts of the world trying to find images via Google for eight hours on 26 August 2014 were served up repeated images of a grisly Russian car accident and photos of NBA star Kevin Durant rather than those they sought. The problem affected Google encrypted searches, which are now the default search mode, across multiple Google domains. In a statement sent to ZDNet, the company said only that the issue was caused by an accident. A source familiar with the issue said it was the result of a bug in Google’s software. Google didn’t comment on exactly what precipitated the problem. (TIME)(ZDNet)

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Google Planning Satellite Constellation for Internet Access

Google is planning a comprehensive satellite network to provide Internet connectivity to areas without such access. The plan calls initially for 180 small, high-capacity satellites in low Earth orbit, with more possibly launched in the future. Previous attempts to launch similar projects were reportedly fraught with both financial and technical problems. Google hired Greg Wyler, founder and former CEO of satellite-communications startup O3b Networks, to lead the venture, estimated to cost between $1 and $3 billion, a price tag that could increase based on factors including the number of satellites ultimately used. (SlashDot)(MarketWatch)

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Google Exploring Expanding Fiber to More US Markets

Google has announced it is talking to officials in 34 different cities in nine US markets to determine whether it can expand its broadband Internet service. Google Fiber fiber-optic networks are now in Kansas City, Mo.; Austin, Texas; and Provo, Utah. The company is in discussions with officials in Portland, Ore.; San Jose, Calif.; Salt Lake City; Phoenix; San Antonio; Nashville, Tenn.; Atlanta; as well as Charlotte and Raleigh/Durham, N.C. Several states have or are considering legislation that would limit public broadband infrastructure growth following intensive lobbying by phone and cable TV company interests. Google Fiber reportedly is able to provide transmission speeds of 1 gigabit per second, which is 20 times faster than the 50 mbps top-end service sold by Verizon Communications and Comcast. Google says it should know by year’s end which areas might be actually receiving Google Fiber. “While we do want to bring Fiber to every one of these cities,” writes Milo Medin, vice president of Google Access Services, “it might not work out for everyone.” (Investor’s Business Daily)(USA Today)(Google Official Blog)
 

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Google Bans Words from Android

A list of 1,400 English-language words are banned from the latest version of the Android operating system. Rather than the Android Google Keyboard automatically completing words such as “geek” or “lovemaking” the system offers no help. The list, dissected by WIRED, includes various euphemisms for the sex act as well as “all seven of George Carlin’s dirty words, a frat party’s worth of homophobia and misogyny, and is peppered with pornographic sub genres and fetishistically obscure medical terms” as well as some words that make no sense to censor, such as “thud”  and “LSAT.” Some drug vocabulary and religious words are banned as are “AMD” and “Garmin.” Adding to the oddity: many Google products, including Chromebook, are missing from the dictionary white list. The filter can be disabled and users can manually add words to the dictionary. (Fox News)(WIRED)
 

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Is Google Behind the Mystery Afloat in San Francisco Bay?

A large structure being built on a barge docked in the San Francisco Bay appears to belong to Google, but its purpose is a mystery. Reporters following the paper trail say the firm that owns the barge, By and Large, has ties to Google. The structure consists of cargo containers stacked atop a barge, which pundits suspect may be a floating data center. Google holds a 2009 patent for such a structure. A similar structure has also been seen off the coast of Portland, Maine. That barge is owned by the same company. One report, from CBS San Francisco affiliate KPIX, contends it is a floating store for the Google Glass wearable computer that will be towed to San Francisco’s Fort Mason area and then float from city to city. Google has not commented on the matter. (CNET)(The Telegraph)

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France Seeks Sanctions against Google for Flaunting Privacy Laws

Google faces financial sanctions in France after failing to obey with an order to bring the way in which it stores and shares user data in compliance with the nation’s privacy laws. The company faced scrutiny by various European data-protection authorities after changing its privacy policy in 2012. Google was ordered on 20 June 2013 to comply with French privacy laws within three months, but it has reportedly did not do so by the time the deadline passed.  Google faces a maximum fine of €150,000 (US$202,562) for a first offense with an additional €300,000 for a second offense. It could also be ordered to change aspects of how it processes personal data for three months. (SlashDot)(CMO)(C.Nationale de L'Informatique et des Libertes)
 

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EU May Be Nearing Antitrust Settlement with Google

The EU and Google may be nearing an agreement in the antitrust case brought against the search giant. EU commissioner for competition Joaquin Almunia said in a speech before the European parliament that a set of commitments that Google recently proposed could result in a legally binding settlement between the parties by the spring of 2014. Without a settlement, Google faces a fine of up to 10 percent of its global revenue, which is about $5 billion. The EU has accused Google of unfair business practices, specifically using its market position to continue dominating the European search market. For example, European officials say Google gives preference to search results involving its own products, such as Google Maps and YouTube. The commission is seeking to end unequal treatment of third-party search engines, as well as advertising restrictions it places on other companies. Google now proposes that its rivals’ results will be prominently displayed with their logo and explanatory text. The page position of competitors’ results within the returned Google search results will be selected via an auction system still under development, which would allow competitors to bit for placement in search results. Google currently has about 90 percent of the European Internet search market. (The Guardian)(Information Week)(European Commission)

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Google Not Responsible for “Right to Be Forgotten”

A senior European judicial official issued a formal opinion stating that Google and other search providers are not responsible for third-party information  in their search results and that there is no general “right to be forgotten” in current data protection laws. The right to be forgotten addresses the storage of personal public data by organizations, including telecommunications providers, and places limits on the time the data is available. Under the EU’s Data Protection Directive, originally adopted in 1995, search engine service providers are not responsible for any personal data that may appear on the webpages they return in response to queries, stated European Court of Justice advocate general Niilo Jääskinen, in a formal opinion written to the court. National data protection authorities in Europe cannot require a search engine to remove third-party information from its index, such as a newspaper article, unless it is incomplete, inaccurate, libelous, or criminal. Jääskinen issued his opinion in response to a 2009 Spanish case in which an individual asked Google to remove old financial information about his debts that were originally published in a newspaper article from its index. Spain’s data-protection agency found in the individual’s favor and asked Google to remove the third-party information so that it wouldn’t appear again in search results. Google contested the ruling in court. Jääskinen’s opinion is not binding on the European Court of Justice, which is expected to issue a ruling later this year. (Financial Times)(BBC)(The Associated Press @ The San Jose Mercury-News)(PC World)(European Network and Information Security Agency)

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Google Seeking Adventurous Backpackers for Maps Expansion

Google Maps is going off-road and soliciting the assistance of intrepid hikers to record terrain for its Street View maps. The company created its camera-equipped Terrain backpack specifically to gather data on those areas inaccessible by paved roads. Google explained, “The Trekker is operated by an Android device and consists of 15 lenses angled in different directions so that the images can be stitched together into 360-degree panoramic views. As the operator walks, photos are taken roughly every 2.5 seconds. Our first collection using this camera technology was taken along the rough, rocky terrain of Arizona’s Grand Canyon.” The 42-pound backpack has also been used to map Japan’s Gunkanjima Island, a deserted island off the country’s west coast originally established for coal mining. Those interested in participating are asked to apply to use the backpack with a description of why they want to participate and whether they can obtain the permissions necessary to access the desired destinations. Google has previously used vehicles such as trolleys and snowmobiles to gather difficult-to-obtain images for Street View. (Ars Technica)(The Daily Mail)(NBC News)(Google)

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Google: Hacked Legitimate Websites Pose Rising Risk

Google has released information indicating that hacked, legitimate websites distributing malicious software are now more numerous than sites that hackers deliberately created to host malware. In its biannual Google Transparency Report, the company said that there are now about 3,891 deliberately malicious sites, compared to 39,247 sites made harmful via hacking. Google estimates that about 60 percent of all compromised websites host malware with 40 percent of all compromised websites used for phishing attacks. Google based its findings on its Safe Browsing service, which compiles provides lists of URLs for Web resources that contain malware or phishing-related content. The Apple Safari, Google Chrome, and Mozilla Firefox browsers use the lists to check pages against potential threats. (CNET)(Computerworld)(Google Transparency Report)

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Google Proposes Concessions in EU Antitrust Case

Google formally submitted a concession package to European Union regulators in hopes of ultimately settling antitrust allegations without incurring either formal charges or a fine. These concessions have not been made public, but industry observers say the Internet search giant has proposed labeling its own services in search results, such as results from YouTube, and easing restrictions on advertisers by allowing them to export analytical data and permitting them to move to competitors’ services. These concessions will reportedly be the first time Google has responded to any type of regulatory pressure. The EU has been investigating various complaints against Google for its business practices, such as allegedly manipulating search results, since 2010. (Reuters)(Mail Online)

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'Game-powered machine learning' opens door to Google for music

ScienceDaily (May 4, 2012) — Can a computer be taught to automatically label every song on the Internet using sets of examples provided by unpaid music fans? University of California, San Diego engineers have found that the answer is yes, and the results are as accurate as using paid music experts to provide the examples, saving considerable time and money. In results published in the April 24 issue of the Proceedings of the National Academy of Sciences, the researchers report that their solution, called "game-powered machine learning," would enable music lovers to search every song on the web well beyond popular hits, with a simple text search using key words like "funky" or "spooky electronica."

Searching for specific multimedia content, including music, is a challenge because of the need to use text to search images, video and audio. The researchers, led by Gert Lanckriet, a professor of electrical engineering at the UC San Diego Jacobs School of Engineering, hope to create a text-based multimedia search engine that will make it far easier to access the explosion of multimedia content online. That's because humans working round the clock labeling songs with descriptive text could never keep up with the volume of content being uploaded to the Internet. For example, YouTube users upload 60 hours of video content per minute, according to the company.

In Lanckriet's solution, computers study the examples of music that have been provided by the music fans and labeled in categories such as "romantic," "jazz," "saxophone," or "happy." The computer then analyzes waveforms of recorded songs in these categories looking for acoustic patterns common to each. It can then automatically label millions of songs by recognizing these patterns. Training computers in this way is referred to as machine learning. "Game-powered" refers to the millions of people who are already online that Lanckriet's team is enticing to provide the sets of examples by labeling music through a Facebook-based online game called Herd It (http://apps.facebook.com/herd-it).

"This is a very promising mechanism to address large-scale music search in the future," said Lanckriet, whose research earned him a spot on MIT Technology Review's list of the world's top young innovators in 2011.

Another significant finding in the paper is that the machine can use what it has learned to design new games that elicit the most effective training data from the humans in the loop. "The question is if you have only extracted a little bit of knowledge from people and you only have a rudimentary machine learning system, can the computer use that rudimentary version to determine the most effective next questions to ask the people?" said Lanckriet. "It's like a baby. You teach it a little bit and the baby comes back and asks more questions." For example, the machine may be great at recognizing the music patterns in rock music but struggle with jazz. In that case, it might ask for more examples of jazz music to study.

It's the active feedback loop that combines human knowledge about music and the scalability of automated music tagging through machine learning that makes "Google for music" a real possibility. Although human knowledge about music is essential to the process, Lanckriet's solution requires relatively little human effort to achieve great gains. Through the active feedback loop, the computer automatically creates new Herd It games to collect the specific human input it needs to most effectively improve the auto-tagging algorithms, said Lanckriet. The game goes well beyond the two primary methods of categorizing music used today: paying experts in music theory to analyze songs -- the method used by Internet radio sites like Pandora -- and collaborative filtering, which online book and music sellers now use to recommend products by comparing a buyer's past purchases with those of people who made similar choices.

Both methods are effective up to a point. But paid music experts are expensive and can't possibly keep up with the vast expanse of music available online. Pandora has just 900,000 songs in its catalog after 12 years in operation. Meanwhile, collaborative filtering only really works with books and music that are already popular and selling well.

The big picture: Personalized radio

Lanckriet foresees a time when -- thanks to this massive database of cataloged music -- cell phone sensors will track the activities and moods of individual cell phone users and use that data to provide a personalized radio service -- the kind that matches music to one's activity and mood, without repeating the same songs over and over again.

"What I would like long-term is just one single radio station that starts in the morning and it adapts to you throughout the day. By that I mean the user doesn't have to tell the system, "Hey, it's afternoon now, I prefer to listen to hip hop in the afternoon. The system knows because it has learned the cell phone user's preferences."

This kind of personalized cell phone radio can only be made possible if the cell phone has a large database of accurately labeled songs from which to choose. That's where efforts to develop a music search engine are ultimately heading. The first step is figuring out how to label all the music online well beyond the most popular hits. As Lanckriet's team demonstrated in PNAS, game-powered machine learning is making that a real possibility.

Lanckriet's research is funded by the National Science Foundation, National Institutes of Health, the Alfred P. Sloan Foundation, Google, Yahoo!, Qualcomm, IBM and eHarmony. You can watch a video about the research and Lanckriet's auto-tagging algorithms to learn more.

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The above story is reprinted from materials provided by University of California - San Diego.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

L. Barrington, D. Turnbull, G. Lanckriet. Game-powered machine learning. Proceedings of the National Academy of Sciences, 2012; 109 (17): 6411 DOI: 10.1073/pnas.1014748109

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

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Google goes cancer: Search engine algorithm finds cancer biomarkers

ScienceDaily (May 17, 2012) — The strategy used by Google to decide which pages are relevant for a search query can also be used to determine which proteins in a patient's cancer are relevant for the disease progression. Researchers from Dresden University of Technology, Germany, have used a modified version of Google's PageRank algorithm to rank about 20,000 proteins by their genetic relevance to the progression of pancreatic cancer. In their study, published in PLoS Computational Biology, they found seven proteins that can help to assess how aggressive a patient's tumor is and guide the clinician to decide if that patient should receive chemotherapy or not.

The researcher's own version of the Google algorithm has been used in this study to find new cancer biomarkers, which are molecules produced by cancer cells. Biomarkers can help to detect cancer earlier in body fluids or directly in the cancer tissue obtained in an operation or biopsy. Finding these biomarkers is often difficult and time consuming. Another problem is that markers found in different studies for the same types of cancer almost never overlap.

This problem has been circumvented using the Google strategy, which takes into account the content of a web page and also how these pages are connected via hyperlinks. With this strategy as the model, the authors made use of the fact that proteins in a cell are connected through a network of physical and regulatory interactions; the 'protein Facebook' so to speak.

"Once we added the network information in our analysis, our biomarkers became more reproducible," said Christof Winter, the paper's first author. Using this network information and the Google Algorithm, a significant overlap was found with an earlier study from the University of North Carolina. There, a connection was made with a protein which can assess aggressiveness in pancreatic cancer.

Although the new biomarkers seem to mark an improvement over currently used diagnostic tools, they are far from perfect and still need to be validated in a larger follow-up study before they can be used in clinical practice. It remains an open problem to turn these insights into novel drugs which slow down cancer progression. A first step in this direction is the group's cooperation with the Dresden-based biotech company RESprotect, who are running a clinical trial on a pancreas cancer drug.

TU Dresden is a leading German university, whose Center for Regenerative Therapies was awarded excellence status in the national excellence initiative. The work was a cooperation between the bioinformatics group of Prof. Dr. Michael Schroeder and the medical groups of Dr. Christian Pilarsky and Prof. Robert Grützmann.

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The above story is reprinted from materials provided by Public Library of Science.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Christof Winter, Glen Kristiansen, Stephan Kersting, Janine Roy, Daniela Aust, Thomas Knösel, Petra Rümmele, Beatrix Jahnke, Vera Hentrich, Felix Rückert, Marco Niedergethmann, Wilko Weichert, Marcus Bahra, Hans J. Schlitt, Utz Settmacher, Helmut Friess, Markus Büchler, Hans-Detlev Saeger, Michael Schroeder, Christian Pilarsky, Robert Grützmann. Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Computational Biology, 2012; 8 (5): e1002511 DOI: 10.1371/journal.pcbi.1002511

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

'Game-powered machine learning' opens door to Google for music

ScienceDaily (May 4, 2012) — Can a computer be taught to automatically label every song on the Internet using sets of examples provided by unpaid music fans? University of California, San Diego engineers have found that the answer is yes, and the results are as accurate as using paid music experts to provide the examples, saving considerable time and money. In results published in the April 24 issue of the Proceedings of the National Academy of Sciences, the researchers report that their solution, called "game-powered machine learning," would enable music lovers to search every song on the web well beyond popular hits, with a simple text search using key words like "funky" or "spooky electronica."

Searching for specific multimedia content, including music, is a challenge because of the need to use text to search images, video and audio. The researchers, led by Gert Lanckriet, a professor of electrical engineering at the UC San Diego Jacobs School of Engineering, hope to create a text-based multimedia search engine that will make it far easier to access the explosion of multimedia content online. That's because humans working round the clock labeling songs with descriptive text could never keep up with the volume of content being uploaded to the Internet. For example, YouTube users upload 60 hours of video content per minute, according to the company.

In Lanckriet's solution, computers study the examples of music that have been provided by the music fans and labeled in categories such as "romantic," "jazz," "saxophone," or "happy." The computer then analyzes waveforms of recorded songs in these categories looking for acoustic patterns common to each. It can then automatically label millions of songs by recognizing these patterns. Training computers in this way is referred to as machine learning. "Game-powered" refers to the millions of people who are already online that Lanckriet's team is enticing to provide the sets of examples by labeling music through a Facebook-based online game called Herd It (http://apps.facebook.com/herd-it).

"This is a very promising mechanism to address large-scale music search in the future," said Lanckriet, whose research earned him a spot on MIT Technology Review's list of the world's top young innovators in 2011.

Another significant finding in the paper is that the machine can use what it has learned to design new games that elicit the most effective training data from the humans in the loop. "The question is if you have only extracted a little bit of knowledge from people and you only have a rudimentary machine learning system, can the computer use that rudimentary version to determine the most effective next questions to ask the people?" said Lanckriet. "It's like a baby. You teach it a little bit and the baby comes back and asks more questions." For example, the machine may be great at recognizing the music patterns in rock music but struggle with jazz. In that case, it might ask for more examples of jazz music to study.

It's the active feedback loop that combines human knowledge about music and the scalability of automated music tagging through machine learning that makes "Google for music" a real possibility. Although human knowledge about music is essential to the process, Lanckriet's solution requires relatively little human effort to achieve great gains. Through the active feedback loop, the computer automatically creates new Herd It games to collect the specific human input it needs to most effectively improve the auto-tagging algorithms, said Lanckriet. The game goes well beyond the two primary methods of categorizing music used today: paying experts in music theory to analyze songs -- the method used by Internet radio sites like Pandora -- and collaborative filtering, which online book and music sellers now use to recommend products by comparing a buyer's past purchases with those of people who made similar choices.

Both methods are effective up to a point. But paid music experts are expensive and can't possibly keep up with the vast expanse of music available online. Pandora has just 900,000 songs in its catalog after 12 years in operation. Meanwhile, collaborative filtering only really works with books and music that are already popular and selling well.

The big picture: Personalized radio

Lanckriet foresees a time when -- thanks to this massive database of cataloged music -- cell phone sensors will track the activities and moods of individual cell phone users and use that data to provide a personalized radio service -- the kind that matches music to one's activity and mood, without repeating the same songs over and over again.

"What I would like long-term is just one single radio station that starts in the morning and it adapts to you throughout the day. By that I mean the user doesn't have to tell the system, "Hey, it's afternoon now, I prefer to listen to hip hop in the afternoon. The system knows because it has learned the cell phone user's preferences."

This kind of personalized cell phone radio can only be made possible if the cell phone has a large database of accurately labeled songs from which to choose. That's where efforts to develop a music search engine are ultimately heading. The first step is figuring out how to label all the music online well beyond the most popular hits. As Lanckriet's team demonstrated in PNAS, game-powered machine learning is making that a real possibility.

Lanckriet's research is funded by the National Science Foundation, National Institutes of Health, the Alfred P. Sloan Foundation, Google, Yahoo!, Qualcomm, IBM and eHarmony. You can watch a video about the research and Lanckriet's auto-tagging algorithms to learn more.

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Story Source:

The above story is reprinted from materials provided by University of California - San Diego.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

L. Barrington, D. Turnbull, G. Lanckriet. Game-powered machine learning. Proceedings of the National Academy of Sciences, 2012; 109 (17): 6411 DOI: 10.1073/pnas.1014748109

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Chemist applies Google software to webs of the molecular world

ScienceDaily (Feb. 13, 2012) — The technology that Google uses to analyze trillions of Web pages is being brought to bear on the way molecules are shaped and organized. Aurora Clark, an associate professor of chemistry at Washington State University, has adapted Google's PageRank software to create moleculaRnetworks, which scientists can use to determine molecular shapes and chemical reactions without the expense, logistics and occasional danger of lab experiments.

"What's most cool about this work is we can take technology from a totally separate realm of science, computer science, and apply it to understanding our natural world," says Clark.

Clark and colleagues from the University of Arizona discuss the software in a recent online article in The Journal of Computational Chemistry. Their work is funded by the U.S. Department of Energy's Basic Energy Sciences program.

The software focuses on hydrogen bonds in water, earth's most abundant solvent and a major player in most every biological process.

"From a biological or chemical standpoint, water is where it's at," says Clark.

In living things, water can perform key functions like helping proteins fold or organizing itself around the things it dissolves so molecules stay apart in a fluid state. But the processes are dazzlingly complex, changing in fractions of a second and in myriad possible forms.

Much like the trillion-plus Web domains on the Internet.

Google's PageRank software, developed by its founders at Stanford University, uses an algorithm -- a set of mathematical formulas -- to measure and prioritize the relevance of various Web pages to a user's search. Clark and her colleagues realized that the interactions between molecules are a lot like links between Web pages. Some links between some molecules will be stronger and more likely than others.

"So the same algorithm that is used to understand how Web pages are connected can be used to understand how molecules interact," says Clark.

The PageRank algorithm is particularly efficient because it can look at a massive amount of the Web at once. Similarly, it can quickly characterize the interactions of millions of molecules and help researchers predict how various chemicals will react with one another.

Ultimately, researchers can use the software to design drugs, investigate the roles of misfolded proteins in disease and analyze radioactive pollutants, Clark says.

"Computational chemistry is becoming the third leg in the stool of chemistry," the other two being experimental and analytical chemistry, says Clark. "You can call it the ultimate green chemistry. We don't produce any waste. No one gets exposed to anything harmful."

Clark, who uses Pacific Northwest National Laboratories supercomputers and a computer cluster on WSU's Pullman campus, specializes in the remediation and separation of radioactive materials. With computational chemistry and her Google-based software, she says, she "can learn about all those really nasty things without ever touching them."

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The above story is reprinted from materials provided by Washington State University. The original article was written by Eric Sorensen.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Barbara Logan Mooney, L.René Corrales, Aurora E. Clark. MoleculaRnetworks: An integrated graph theoretic and data mining tool to explore solvent organization in molecular simulation. Journal of Computational Chemistry, 2012; DOI: 10.1002/jcc.22917

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Google goes cancer: Search engine algorithm finds cancer biomarkers

ScienceDaily (May 17, 2012) — The strategy used by Google to decide which pages are relevant for a search query can also be used to determine which proteins in a patient's cancer are relevant for the disease progression. Researchers from Dresden University of Technology, Germany, have used a modified version of Google's PageRank algorithm to rank about 20,000 proteins by their genetic relevance to the progression of pancreatic cancer. In their study, published in PLoS Computational Biology, they found seven proteins that can help to assess how aggressive a patient's tumor is and guide the clinician to decide if that patient should receive chemotherapy or not.

The researcher's own version of the Google algorithm has been used in this study to find new cancer biomarkers, which are molecules produced by cancer cells. Biomarkers can help to detect cancer earlier in body fluids or directly in the cancer tissue obtained in an operation or biopsy. Finding these biomarkers is often difficult and time consuming. Another problem is that markers found in different studies for the same types of cancer almost never overlap.

This problem has been circumvented using the Google strategy, which takes into account the content of a web page and also how these pages are connected via hyperlinks. With this strategy as the model, the authors made use of the fact that proteins in a cell are connected through a network of physical and regulatory interactions; the 'protein Facebook' so to speak.

"Once we added the network information in our analysis, our biomarkers became more reproducible," said Christof Winter, the paper's first author. Using this network information and the Google Algorithm, a significant overlap was found with an earlier study from the University of North Carolina. There, a connection was made with a protein which can assess aggressiveness in pancreatic cancer.

Although the new biomarkers seem to mark an improvement over currently used diagnostic tools, they are far from perfect and still need to be validated in a larger follow-up study before they can be used in clinical practice. It remains an open problem to turn these insights into novel drugs which slow down cancer progression. A first step in this direction is the group's cooperation with the Dresden-based biotech company RESprotect, who are running a clinical trial on a pancreas cancer drug.

TU Dresden is a leading German university, whose Center for Regenerative Therapies was awarded excellence status in the national excellence initiative. The work was a cooperation between the bioinformatics group of Prof. Dr. Michael Schroeder and the medical groups of Dr. Christian Pilarsky and Prof. Robert Grützmann.

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Story Source:

The above story is reprinted from materials provided by Public Library of Science.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Christof Winter, Glen Kristiansen, Stephan Kersting, Janine Roy, Daniela Aust, Thomas Knösel, Petra Rümmele, Beatrix Jahnke, Vera Hentrich, Felix Rückert, Marco Niedergethmann, Wilko Weichert, Marcus Bahra, Hans J. Schlitt, Utz Settmacher, Helmut Friess, Markus Büchler, Hans-Detlev Saeger, Michael Schroeder, Christian Pilarsky, Robert Grützmann. Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Computational Biology, 2012; 8 (5): e1002511 DOI: 10.1371/journal.pcbi.1002511

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here