The concept of personalized medicine is that every individual patient should be
treated differently according to his/her genetic makeup, clinical records, and personal health history. To make it possible, three prerequisite must be first satisfied: the whole human genome sequencing service as cheap as $1,000 per person; the electrical health record system; and the business models that would generate profits to companies advocating the personalized medicine. The rapid development on these aspects in the academia as well as in industry is strongly pushing forward the personalized medicine concept to become the reality.
The first wave comes from the ultra-high throughput sequencing technologies. The human genome project was entirely performed by the traditional Sanger sequencing. Despite its high accuracy, the major limitation of that sequencing method is its slow speed and high cost. The high demand for low-cost sequencing has given rise to a number of high-throughput sequencing technologies. High-throughput sequencing technologies are intended to lower the cost of sequencing DNA libraries beyond what is possible with the current method based on DNA separation by capillary electrophoresis. Many of the new high-throughput methods use methods that parallelize the sequencing process, producing thousands or millions of sequences at once. Several companies are the key players in that rush. For example, 454 Life Sciences, is a company specializing in high-throughput DNA sequencing using a sequencing-by-synthesis approach [1]. Their featured sequencing technology could
generate millions of sequences as long as hundreds bases. Another company, Illumina, provides the sequencing-by-synthesis technology that can generate more than 50 million short sequences in a run [2]. The cost of these services in a run is about $10,000, and it is anticipated that the cost will drop lower as the high-throughput sequencing technology grows mature.
Armed with these powerful sequencing techs, the academia communities are the active advocators of the personal genome and personalized medicine. For instance, the Personal Genome Project (PGP) aims to publish the complete genomes and medical records of several volunteers, in order to enable research into personalized medicine [3]. It was initiated by Harvard University's George Church and announced in January 2006. The project will publish the genome sequences of the volunteers, along with extensive information about their phenotype: medical records, various measurements, MRI images, etc. All data will be freely available over the Internet. Now they are recruiting volunteers participated in the project; they aim to enroll 100,000 informed participants from the general public. Lee Hood, the director of the Institute for Systems Biology, is another enthusiastic supporter of the personalized medicine.
The second wave of technology pushing the personalized medicine forward is the electrical health record system. Today, many of the health records are already stored in computers. However, the health records of a patient are usually scattered in computer systems of several health providers, and such records are difficult or even impossible to integrate in one place. A universal health record system cast significant advantage to today’s system for that it provides more information of the patient with uniform format to the physicians, enables better diagnosis and prognosis. Also, such integrated information will boost the researchers to make more discoveries and find out more effective ways to treat the complex diseases like cancers. Third, the electrical health record system gives the control of the medical information of patients back to the owner. The patients have more freedom to review their health records, educate themselves, thus making better life style choices. Several IT giants, such as Google, Microsoft, IBM et. al, are developing the personal health records systems. Microsoft has already launched such a system called ‘HealthVault’ [4]. Interestingly, the personal genome can be served as the central piece of information of an individual in the personal health record system. The personal genome is expected to be integrated into the personal health record systems as soon as the personal genomes are affordable and widely available.
The industry is jumping into this ‘Gold Rush’ long before the personal genomes are widely available. In fact, some companies are beginning to make profit by providing the genetic test to customers using the dense SNP chips. 23AndMe is a pioneer in this effort [5]. 23AndMe provide the customers permanent access to their gene-disease risk database (the gene-journal) for about $1000. They also let customers to trace their family tree, and make social networking based on their genetic similarities to others. Contrary to 23AndMe’s educational and entertaining services, the Navigenics company puts a great deal of emphasis on the utility of genotype data for useful medical insights [6]. They will charge an initial fee of $2,500 for a one year membership -- and then an annual fee of $250. With this charges, they will freeze your spit sample, allowing the company to re-test your DNA as more associations with different SNPs are discovered. The business models of 23AndMe and Navigenics are both solid, the exploration for entertaining use of the personal genome is a good trial. And the strategy to keep closer to the medical use of the genomic information makes Navigenics more welcome to physicians. However, the genetic test companies all suffered from the vague link between the genetic markers and the predisposition of diseases. It is too early to say they can thrive to the heyday of the personal genomics and personalized medicine.
Although there are doubts about how to interpret the personal genomes, high-techs such as ultra-high throughput sequencing techs and online personal health record systems, are propelling the personalized medicine forward. The dream of personalized medicine will come true not long before.
Reference
[1] http://www.454.com/
[2] http://www.illumina.com/
[3] http://www.personalgenomes.org/
[4] http://www.healthvault.com/
[5] http://www.23AndMe.com
[6] http://www.Navigenics.com
Wednesday, April 30, 2008
Tuesday, March 4, 2008
Personalized Search Primer - And Google's Approach
Wrtten by Greg Linden, founder of personalized news service Findory and author of Geeking with Greg.
Google has received much attention, not all of it positive, for its efforts to personalize search.
In this article, I will briefly describe personalized search, why Google and other search engines are trying to do personalized search, the approach Google is taking toward personalized search, and other approaches to personalized search.
What is personalized search?
Personalized search is showing different search results to different people. Personalized search uses each searcher's past behavior to try to understand intent and what is relevant to that searcher.
If I search for [java] and you search for [java], and we see different results because of what we did in the past, that is personalized search. The search results are individualized, different for each of us.
It is true that a search for [java] is ambiguous. What do you want when you search for [java]? Are you a programmer looking for the Java documentation from Sun? Are you looking for a summary of the Java programming language from Wikipedia? Are you someone who wants the Java download so you can run a Java applet? Or maybe you are planning a trip to Indonesia?
Your past behavior may help the search engine figure out what you want. If you previously searched about Indonesia, that tells it one thing. If you searched for [java sdk] two days ago, that indicates something else.
Personalized search shows different results to different people based on their past behavior. Personalized search tries to disambiguate intent by using information not only about what you are doing now, but also what you did in the past.
Why do personalized search?
Search engines are trying to make search results more useful. They want to help people find the information they want faster.
Search engines help searchers find what we need faster by trying to put the most relevant results for our searches at the top of the page, a process known as relevance ranking.
However, different people are interested in different results. What a geek likes is quite a bit different than what that geek's mother considers relevant.
Right now, geek and geek mother see the same results when we search on most search engines. The relevance rank is generic, trying to order the results to what is most useful to the average user - ignoring individual needs.
The generic relevance rank continues to improve, but each improvement seems to be getting harder and harder to find. At some point, the only way get further improvements, to help people find what they need faster, is to individualize the relevance rank.
Google gets serious about personalization; Pic by christophercarfi
Even early steps toward personalized search could make a substantial difference. Search engines currently treat each search as independent, so what you just searched for does not matter in terms of what you see on your next search.
But, someone who searches for [indonesia] and then [java] likely has different interests than someone who searches for [applet] and then [java]. What you just wanted is often helpful to determine what you want now.
While concerns about the privacy implications of storing and using past behavior are real, personalized search likely is inevitable. Different people have different perceptions of relevance. To help searchers find what they need, to deal with differing intent, different searchers will need to see different search results.
Google Personalized Search
Google Personalized Search uses technology acquired in 2003 from a small startup named Kaltix. A 2002 paper, "Scaling Personalized Web Search", describes the technique invented by Kaltix.
The basic idea is to create many different relevance ranks, each tailored to the interests of a group of people. When executing a search, Google uses the shards of the index organized for the tastes of people like me to rank my results.
How it works was easily visible in an early version of Google Personalized Search. Users checked off a boxes corresponding to interests (e.g. "computers" and "architecture") and then Google would bias all future searches toward those interests. An early version of Google Custom Search (previously known as "site-flavored search") also was based on Kaltix technology and allowed people to put a search box on their site that would be biased towards a specific category (e.g. "Computers/Internet").
The current version of Google Personalized Search learns from your search queries. Searchers do not have to do anything explicitly to use it; it is all implicit. The current Google Personalized Search likely is using the same Kaltix technology, building a high-level profile of you, then biasing all of your search results based on your long-term behavior.
Other ways to do personalized search
Google's personalized search is not the only way to do personalized search. Google uses high-level profiles learned implicitly from your long-term search history.
Rather than use a high-level profile (e.g. an interest in "computers"), personalized search could be fine-grained - based on your specific actions. For example, specific results you have seen in the past could be featured (known as re-finding). Results related to results you have clicked on in the past could be featured. Results you have seen before on other queries could be hidden. Results for similar or related searches to your past searches could influence your current results.
Rather than learning implicitly, personalized search could be explicit. For example, you could specify categories of interest, much like the old personalized search. Or, you could explicitly rate web pages. Or, you could explicitly share search results or favorites with friends (like on Yahoo MyWeb).
Rather than using long-term history, personalized search could focus on what you are doing right now. For example, if you refine a search, starting with [indonesia], then [java], the first could influence the second without keeping any long-term summary of your overall interests.
There may be some disadvantages to the approach Google is using for personalized search. For example, using long-term, high-level profiles means that the search engine can shift results slightly toward general preferences, but it cannot make immediate changes based on what a searcher is doing right now. In particular, it cannot help much when searchers are on a mission, doing a series of related searches, but not finding what they want.
Conclusion
Personalized search allows a search engine to show different people different search results based on their past behavior. It disambiguates intent using information from the past, allowing the engine to cater to differing perceptions of relevance.
Personalized search is an early step from generic search tools towards individualized assistants. Personalized search is part of a shift from information retrieval to information discovery.
One day perhaps, we will have a search engine that not only helps us find the information we seek, but also helps us discover information we could not have found on our own. One day perhaps, a search engine will not only help us find information, but also help us process and understand it.
Google has received much attention, not all of it positive, for its efforts to personalize search.
In this article, I will briefly describe personalized search, why Google and other search engines are trying to do personalized search, the approach Google is taking toward personalized search, and other approaches to personalized search.
What is personalized search?
Personalized search is showing different search results to different people. Personalized search uses each searcher's past behavior to try to understand intent and what is relevant to that searcher.
If I search for [java] and you search for [java], and we see different results because of what we did in the past, that is personalized search. The search results are individualized, different for each of us.
It is true that a search for [java] is ambiguous. What do you want when you search for [java]? Are you a programmer looking for the Java documentation from Sun? Are you looking for a summary of the Java programming language from Wikipedia? Are you someone who wants the Java download so you can run a Java applet? Or maybe you are planning a trip to Indonesia?
Your past behavior may help the search engine figure out what you want. If you previously searched about Indonesia, that tells it one thing. If you searched for [java sdk] two days ago, that indicates something else.
Personalized search shows different results to different people based on their past behavior. Personalized search tries to disambiguate intent by using information not only about what you are doing now, but also what you did in the past.
Why do personalized search?
Search engines are trying to make search results more useful. They want to help people find the information they want faster.
Search engines help searchers find what we need faster by trying to put the most relevant results for our searches at the top of the page, a process known as relevance ranking.
However, different people are interested in different results. What a geek likes is quite a bit different than what that geek's mother considers relevant.
Right now, geek and geek mother see the same results when we search on most search engines. The relevance rank is generic, trying to order the results to what is most useful to the average user - ignoring individual needs.
The generic relevance rank continues to improve, but each improvement seems to be getting harder and harder to find. At some point, the only way get further improvements, to help people find what they need faster, is to individualize the relevance rank.
Google gets serious about personalization; Pic by christophercarfi
Even early steps toward personalized search could make a substantial difference. Search engines currently treat each search as independent, so what you just searched for does not matter in terms of what you see on your next search.
But, someone who searches for [indonesia] and then [java] likely has different interests than someone who searches for [applet] and then [java]. What you just wanted is often helpful to determine what you want now.
While concerns about the privacy implications of storing and using past behavior are real, personalized search likely is inevitable. Different people have different perceptions of relevance. To help searchers find what they need, to deal with differing intent, different searchers will need to see different search results.
Google Personalized Search
Google Personalized Search uses technology acquired in 2003 from a small startup named Kaltix. A 2002 paper, "Scaling Personalized Web Search", describes the technique invented by Kaltix.
The basic idea is to create many different relevance ranks, each tailored to the interests of a group of people. When executing a search, Google uses the shards of the index organized for the tastes of people like me to rank my results.
How it works was easily visible in an early version of Google Personalized Search. Users checked off a boxes corresponding to interests (e.g. "computers" and "architecture") and then Google would bias all future searches toward those interests. An early version of Google Custom Search (previously known as "site-flavored search") also was based on Kaltix technology and allowed people to put a search box on their site that would be biased towards a specific category (e.g. "Computers/Internet").
The current version of Google Personalized Search learns from your search queries. Searchers do not have to do anything explicitly to use it; it is all implicit. The current Google Personalized Search likely is using the same Kaltix technology, building a high-level profile of you, then biasing all of your search results based on your long-term behavior.
Other ways to do personalized search
Google's personalized search is not the only way to do personalized search. Google uses high-level profiles learned implicitly from your long-term search history.
Rather than use a high-level profile (e.g. an interest in "computers"), personalized search could be fine-grained - based on your specific actions. For example, specific results you have seen in the past could be featured (known as re-finding). Results related to results you have clicked on in the past could be featured. Results you have seen before on other queries could be hidden. Results for similar or related searches to your past searches could influence your current results.
Rather than learning implicitly, personalized search could be explicit. For example, you could specify categories of interest, much like the old personalized search. Or, you could explicitly rate web pages. Or, you could explicitly share search results or favorites with friends (like on Yahoo MyWeb).
Rather than using long-term history, personalized search could focus on what you are doing right now. For example, if you refine a search, starting with [indonesia], then [java], the first could influence the second without keeping any long-term summary of your overall interests.
There may be some disadvantages to the approach Google is using for personalized search. For example, using long-term, high-level profiles means that the search engine can shift results slightly toward general preferences, but it cannot make immediate changes based on what a searcher is doing right now. In particular, it cannot help much when searchers are on a mission, doing a series of related searches, but not finding what they want.
Conclusion
Personalized search allows a search engine to show different people different search results based on their past behavior. It disambiguates intent using information from the past, allowing the engine to cater to differing perceptions of relevance.
Personalized search is an early step from generic search tools towards individualized assistants. Personalized search is part of a shift from information retrieval to information discovery.
One day perhaps, we will have a search engine that not only helps us find the information we seek, but also helps us discover information we could not have found on our own. One day perhaps, a search engine will not only help us find information, but also help us process and understand it.
Saturday, March 1, 2008
Monday, February 4, 2008
The oline advertising
Advertisers value both reach and effectiveness. That means they will pay higher rates for their ads to be shown to the most targets.
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