I’ve now implemented such a system as an extension to Catalyst, the open source Perl web framework. The system isn’t yet ready for general distribution, but I’d like to share my approach.

First, I’ve gathered ten years of web access logs from WormBase, a generic model organism database where I work as the project manager.

Next, I correlated IP addresses with requests and tried to trace browsing patterns from one object to the next. This isn’t an exact science since we haven’t historically tried to uniquely identify users.

Data is loaded into a simple MySQL schema with object and object2related tables. Expediently simple.

Recommender systems [Wikipedia] seek to provide users with information related to what they are currently browsing. These are now ubiquitous in e-commerce sites such as Amazon, where each page contains a list of items viewed or purchased by other users.

I’ve long felt that a recommender system could revolutionize the browsing and mining of biological data. The idea would be to provide users with a list of related objects based on browsing history of cadres. See this post for some preliminary implementation notes.

I am worried that a recommender system won’t be received with open arms. Given that my current implementation is based on web log analysis it presents serious privacy issues. It’s conceivable that it’s use could inadvertently reveal the identity of uncloned and unpublished loci.

Several years ago, WormBase began collecting detailed intellectual pedigrees for members of the community. To display the large degree of collaboration within the community, I created a directed acyclic graph for the 2005 C. elegans meeting that displays the following relationships:

• PI <-> Post doc
• PI <-> Grad student
• PI <-> Masters
• PI <-> Undergraduate
• Worked with
• Collaborated with

About the image

The Y-axis displays years, with the apex of the graph positioned at 1970. Only individuals that have bibliographic or lineage data contained in WormBase as of 5/2005 are displayed on the graph. Individuals are placed on the Y-axis according to the year the first entered the community. This is determined as the earliest year of either the first paper or meeting abstract published or drawn from
lineage data, if it exists. The position on the X-axis is arbitrary, calculated using an energy minimization algorithm to restrict the number of intersecting lines.

Key

Viewing the image

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