Archive for the ‘PubMed Watcher’ Category

PubMed Watcher (beta)

Thursday, April 25th, 2013

PubMed Watcher (beta)

After logging it with a Google account:

Welcome on PubMed Watcher!

Thanks for registering, here is what you need to know to get quickly started:

Step 1 – Add a Key Article

Define your research topic by setting up to four Key Articles. For instance you can use your own work as input or the papers of the lab you are working in at the moment. Key Articles describe the science you care about. The articles must be referenced on PubMed.

Step 2 – Read relevant stuff

PubMed Watcher will provide you with a feed of related articles, sorted by relevance and similarity in regards to the Key Articles content. The more Key Articles you have, the more tailored the list will be. PubMed Watcher helps to abstract away from journals, impact factors and date of publishing. Spend time reading, not searching! Come back every now and then to monitor your field and to get relevant literature to read.

Ready? Add your first Key Article or learn more about PubMed Watcher machinery.

OK, so I picked four seed articles and then read the “about,” where a “pinch of heuristics” says:

Now the idea behind PubMed Watcher is to pool the feeds coming from each one of your Key Article. If an article is present in more than one feed, it means that this article seems to be even more interesting to you, that’s the heuristic. The redundant article then gets a new higher score which is the sum of all its indivual scores. Example, let’s say you have two Key Articles named A and B. A has two similar articles F and G with respective similarity scores of 4 and 2. The Key Article B has two similar articles too: M and G with scores 7 and 6. The feed presented to you by PubMed Watcher will then be: G first (score of 6+2=8), M (score of 7) and finally F (4). This score is standardised in percentages (relative relatedness, the blue bars in the application), so here we would get: G (100%), M (88%) and F (50%). This metrics is not perfect yet it’s intuitive and gives good enough results; plus it’s fast to compute.

Paper on the technique:

PubMed related articles: a probabilistic topic-based model for content similarity by Jimmy Lin and W John Wilbur.

Code on Github.

The interface is fairly “lite” and you can change your four articles easily.

One thing I like from the start is that all I need do it pick one to four articles and I’m setup.

Hard to imagine an easier setup process that comes close to matching your interests.