Oscar uses a Maximum Entropy Markov Model (MEMM) based on n-grams. Peter Corbett has written this up (doi:10.1186/1471-2105-9-S11-S4). So, it basically is statistics once more. If you really want a proper bioinformatics education, so do your PhD at a (proteo)chemometrics department. N-grams are word parts of n characters. For example, the trigrams of acetic acid include ace, cid, tic, eti, and aci.
The two earlier posts in this series showed screenshots of results of Oscar, but the title also promised results by Lezan’s ChemicalTagger. Sam helped with getting the HTML pages online via the Cambridge Hudson installation. Where Oscar find named entities (chemical compounds, processes, etc), ChemicalTagger finds roles, like solvent, acid, base, catalyst. Roles are properties of chemical compounds in certain situations.
Linus’ law: Rich of MetaMolecular works on Open Source and closed source cheminformatics solutions. ChemWriter is one product he is working on which uses JavaScript and SVG (two Open Standards), and recently asked feedback on the new version.
Say, you have your own dictionary of chemical compounds. For example, like your company’s list of yet-unpublished internal research codes. Still, you want to index your local listserv to make it easier for your employees to search for particular chemistry you are working on and perhaps related to something done at other company sites. This is what Oscar is for. But, it will need to understand things like UK-92,480.
One goal of my three month project is to take Oscar4 to the community. We want to get it used more, and we need a larger development community. Oscar4 and the related technologies do a good, sometimes excellent, job, but have to be maintained, just like any other piece of code. To make using it easier, we are developing new APIs, as well as two user-oriented applications: a Taverna 2 plugin , and command line utilities.
Last month I reported a few things I missed in CiteULike. One of them was support for CiTO (see doi:10.1186/2041-1480-1-S1-S6), a great Citation Typing Ontology. I promised the CiTO author, David, my use cases, but have been horribly busy in the past few weeks with my new position, wrapping up my past position, and thinking on my position after Cambridge.
Besides getting Oscar used by ChEBI (hopefully via Taverna ), my main task in my three month Oscar project is to refactor things to make it more modular, and remove some features no longer needed (e.g. an automatically created workspace environment). Clearly, I need to define a lot of new unit tests to ensure my assumptions on how to code works are valid. So, what are the API requirements set out?
One of the goals of my project in Cambridge is to make Oscar available as Taverna plugin (source code, Hudson build). I have progressed somewhat, but still struggling with getting the update site working.
As Peter announced in his blog, and I tweeted earlier, I have started as postdoctoral research associate in Peter’s group at the University of Cambridge, to work the next three months on Oscar, a chemical text mining tool.
AJCann posted a blog today about what he doesn’t like about Mendeley. Abhishek replied that he does not like people complain about one tool, instead of pointing out a good alternative. Mendeley has two alternatives, Zotero and CiteULike (there is also Connotea, but got behind in evolution). Agreeing with @citeulike and @abhishektiwari, as a service provider any bad news is good news too: they provide opportunities to improve.
I have blogged about two Molecular Chemometrics principles so far: McPrinciple #1: access to data McPrinciple #2: be clear in what you mean Peter’s post #solo10: Green Chain Reaction; where to store the data? DSR? IR? BioTorrent, OKF or ??? gives me enough basis to write up a third principle: Molecular Chemometrics Principles #3 : We make scientific progress if we build on past achievements. Sounds logical, right?