The stream

http://www.flickr.com/photos/hamed/
 CC BY 2.0

Google unveiled recently Yet Another Try at social networking in the form of Google Buzz. It is a social network borrowing heavily from Friendfeed, a website build by ex-googlers. If you are not familiar with Friendfeed here is a post that goes through some of its features.

One interesting thing about all this proliferation of social networks and feed aggregators is seeing their evolution over time. Over the past couple of years some of their features became somewhat standard. You could say that this is just because some websites keep stealing ideas from others but it also says which features seam to be useful and which implementations are intuitive to theirs users.

One idea that is central and common to all of these social websites is the concept of the stream. A list of updates from your contacts in the network typically ordered by time that you can interact with either by commenting or more simply by stating that you find that interesting. These actions are in turn propagated to your own contacts and so on.

It is impressive to see how this simple idea became so widespread in so little time. Facebook estimates that it has over 400 million active users. If Facebook was a country it would the 3rd most populous after China and India. We had plenty of ways to interact with friends and colleagues online before these social networks arrived (Email and instant messaging among others) so why did they become so popular ? The first few iterations of the stream reminded me a lot of those mass emails and chain emails from a few years back. It is also somewhat similar to how people were using their status in instant messaging tools to broadcast news about themselves. These two examples show that when given the tools people enjoy telling their contacts what their up to.
Status in instant messaging have no history and broadcasting jokes by email is very impolite as most people use email for work. So broadcasting to your social network in an non-intrusive way fills a need that previous tools could not solve well before.

Its clear that the stream is here to stay but where is it heading to ?

The stream localized
GPS enabled phones let us track our position and share it with the world. I am personally not comfortable with this but plenty of people are using tools like Foursquare and now Google Buzz to share their coordinates. In Foursquare users can play games where they "check-in" to places to unlock tips and badges. For business owners this could be used to give rewards for loyalty to their costumers.
It is easy to imagine how interesting it would be to get tips on what to eat when "checking in" to a restaurant or finding out that your friend is just around the corner in a cafe you like. Still, you don't have to be too paranoid to start thinking about the implications of telling the world where you are. "Please Rob Me" is the name of a website that, as the name implies, was created exactly to raise awareness to these privacy concerns.

Most likely these tools will iterate through changes in their privacy settings. For example, Google Latitude lets you share your location only to a select group of people or applications as well as letting you set the level of detail shared (ex. exact position versus area/city).  Given the many business opportunities around location based advertisement companies will certainly try to make location sharing a standard property of the stream. The advertisement system in the movie Minority Report comes to mind.

Social Searching
After releasing Google Buzz the company also announced that they had acquired the company Aardvark. If you use sites like twitter or many of the other social networks you probably tried to broadcast a question. If you are not sure who exactly knows the answer  there is no harm and casting a wide (and non-intrusive) net to try to find an answer. The term "lazy web" describes this sort of question broadcasting. In twitter there are even simple services organized around these "lazyweb" questions (see Lazytweet as exanple).

Aardvark tries to take this concept a bit further by targeting your questions to people that are more likely to known the answer instead of simply broadcasting to all your network. When you sign up to the service you tell it what subjects you might be able to answer and how often you mind getting some questions. In return you can ask Aardvark any question you want and it will try to route it to an "expert". This sort of social searches are a useful complement to current search engines. Your not supposed to ask questions that are easy to find with Google and it will take longer to get a reply but you can ask more subjective questions and hopefully get very knowledgeable answers.

I have tried asking questions in different social networks and a few times in Aardvark. Predictably the quantity and quality of the replies depends mostly on how specific the question is. Very broad and subjective questions get many useful replies while questions on very specialized topics will probably go unanswered.
The success of such an approach depends on many different factors but it looks like an interesting direction for search.


What do you think ?
In what other ways will we be using the stream ?

Review - You are not a gadget

I just finished reading "You are not a gadget" by Jaron Lanier. The book is very much in the same tone as an article he recently wrote the Edge called "DIGITAL MAOISM:
The Hazards of the New Online Collectivism". Very few other books made me want to say "No!" out loud so many times while reading it. I enjoy reading opinions that run contrary to my own because I think it is important to challenge our ideas. This is why I like reading Rough Type. This book, however, was extremely confusing too me. It reads mostly as a collection of essays and often deviates from the path. I still think it was an interesting book to read because of the importance of the topic.

If you read the essay linked above you will get the general feeling conveyed in the book. As Lanier writes in the end of the first chapter:
"So, in this book, I have spun a long tale of belief in the opposites of computationalism, the noosphere, the Singularity, web 2.0, the long tail, and all the rest. I hope the volume of my contrarianism will foster an alternative mental environment, where the exciting opportunity to start creating a new digital humanism can begin".

I think these sentences summarize well what he set out to do in this book. To counter the rising open culture / web 2.0 movement and create some "alternative mental environment" for the future of the web culture. Some things he talks about I fully subscribe. If you believe that the singularity is near and that we are about to merge with the machines in the next couple of years you are about as bonkers as the rapture people. The wisdom of the crowds can do a great job at annotating images but it will not cure cancer. Also, the rise of the open culture (free content, mash-ups, etc) is hurting content producers and we can't just say that they are the dinosaurs and let them figure it out while we pirate their goods. Journalism is fundamental to democracy and we need to figure a way to make it work.

What I dislike about the book is the overly negative tone. How many people really believe that "wisdom of the crowds" can solve the worlds problems ? How many people have even heard of the term ? I would risk saying that Lanier spends too much time around silicon valley geeks. Sure, there is an open culture on the web but I pay more for content today that I ever did before (The Economist, Nature Reviews Genetics, Netflix, iTunes, Amazon on  Demand, Pandora One, etc). The web 2.0 mash-up craze peaked when the Times nominated "You" as the person of the year (twitter is not content ;). Also, I like youtube clips like anyone else, some of them can be just amazing (ex. Kutiman's mash-ups) but I still want to pay to see Avatar again in glorious 3D IMAX.

One idea that he mentions often is that of the technological lock-in. As media formats might get locked in with use by the majority Lanier argues that concepts and ideas can be equally locked-in. An example he gives is the concept of files on the computers. That we are no longer free to experiment with the way information is stored in a computer system because this has been locked in.

What I guess Laniear was trying to say with this warning about technological lock-ins is that we run the risk of getting trapped in a set of ideas of the web that decrease the value of humanity and the content we produce and give too much value to the cloud of computers that underly the net. Even if I was to agree that current web culture tends to devalue content and humanity I don't think these lock-ins can be that powerful. We see net culture changing everyday before us and we have so far gained much more than we lost.

In summary, I would say that the problems he talks about are important but the book is overly pessimist about our current web culture.

Predicting and explaining drug-drug interactions

I am generally interested in chemogenomic studies and drug interaction studies as a complement to what we work on in the Krogan lab (genetic interactions). Much like in genetic interaction screening, where the fitness of double mutant strains is compared with that of the individual single mutants, chemogenomics tries to identify drug-gene interactions while drug-drug interaction screening attempts to find cases where the combined effect of two compounds on fitness is different from the expected from the combination of the single independent effects.

I read two recent papers that I found interesting regarding drug-drug interactions. One was by Bollenbach and colleagues from the Kishony lab (published in Cell) and the other was by Jansen and colleagues (published in MSB). In the first, the authors present an explanation for a previously observed drug-drug interaction. It had been previously shown that the combination of DNA and protein synthesis inhibitors results in lower reduction of fitness than expected by a neutral combination model (termed antagonist interaction). The authors show in this paper that, in the presence of DNA synthesis inhibitors, ribosomal genes are not optimally expressed. This imbalance between ribosomal production and cell growth is detrimental to the cell and can be, at least in part, corrected by protein synthesis inhibitors, explaining why these can suppress the effects of the DNA synthesis inhibitors.

Although it is a relatively simple idea (once described), I think it shows how complex these drug-drug interactions can be and to some extent also how these can provide information about a cell.

In the second paper I mentioned, Jansen and colleagues try to develop an approach to predict drug-drug interactions based on chemogenomic data. There are many obvious reasons why this would be very useful and I find this line of research extremely interesting. What I was surprised with was the simplicity of the approach and the disappointing benchmarks.

The end-result from a chemogenomic screen is a vector of drug-gene interaction scores that tell us how the combination of a drug with each mutant (normally KO strains) affect growth when compared to neutral expectation from the combined effect of the individual perturbations. It had been previously shown that drugs that have a similar drug-gene vectors tend to have similar mechanisms of action (Parsons et al. 2006 Cell). What Jansen and colleagues now claim is that the similarity of drug-gene vectors are predictive not only of similar mode of action but also of drug-drug interactions. Specifically, they try to show that drugs with similar profiles are more likely to be synergistic, such that the combined effect of both drugs is expected to be more detrimental to the cell  that the expected neutral combination.

Although the authors show experimental validation of their predictions with an accuracy of 56% they also benchmark their predictions using drug pairs  previously known to be synergistic. This benchmark is somewhat disappointing since they only see a significant enrichment of these true-positive pairs for a narrow range of cut-offs and with 2 out of 3 ways of calculating drug-profile similarity. I wish the authors had comment on this difference between the relatively poor performance based on benchmark and the very high accuracy observed in their experimental tests. They also show that these predicted synergistic pairs are well conserved from S. cerevisiae to C. albicans which is contradictory to a previous Nature Biotech paper that I mentioned in previous post.

Are drug-synergies this easy to predict and so well conserved across species? I am personally not convinced based on the data from this paper alone so I am holding off for further validation by other groups or additional larger datasets/benchmarks.