In a recent comment, Creaky (Kathleen Crea) made me aware of LigerCat, a 3rd-Party PubMed/MEDLINE tool that is new to me. I’m really enjoying working with it.¹

I’m sure that more experienced Medical Libraryfolk don’t have to do this, but as I start putting together a lit search, I often start by going to the MeSH Browser http://www.nlm.nih.gov/mesh/MBrowser.html to begin working out what MeSH terms I might be working with. Alternately, I might go to Novo|Seek or GoPubMed with a few key words to get a frequency analysis of MeSH terms. In these examples, I’m doing some preliminary searching on Acute Disseminated Encephalomyelitis.

LigerCat isn’t necessarily *better* at this, but its presentation is simpler. Rather than putting the frequency analysis of MeSH terms in a left sidebar, it gives a cloud of MeSH terms:

Seeing the biggest, most obvious tag item in the cloud (see above) is delightful. If one clicks on the tags in the MeSH cloud, they’re added to the search. When you’re done adding terms, you can click “Go to PubMed” to run the search there.

In this example, the query run in PubMed is:
(”encephalomyelitis, acute disseminated”[MeSH Terms] OR (”encephalomyelitis”[All Fields] AND “acute”[All Fields] AND “disseminated”[All Fields]) OR “acute disseminated encephalomyelitis”[All Fields] OR (”acute”[All Fields] AND “disseminated”[All Fields] AND “encephalomyelitis”[All Fields])) AND (”Encephalomyelitis, Acute Disseminated”[mh] AND “Humans”[mh] AND “Treatment Outcome”[mh])

…and the results aren’t bad.

If I was caught up in Google Reader (I’m not, and haven’t been for about 15 months), I would have noticed Creaky’s post on LigerCat a couple of days ago. This reminds me to move Kathleen’s feed into my “High Priorities” folder. You may want to do the same.

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CiteSmart is a citation software specifically developed for PubMed users to faciliate the writing of manuscripts and other academic documents. With CiteSmart, retrieving references from PubMed is just a click away. This revolutionary software has many new features not found anywhere else. You will be able to:

* Search PubMed from your Word document.
* Insert a citation directly into your document from Internet Explorer.

These two features will save an enormous amount of time. It reduces extraneous clicking and the need to create a database of references. CiteSmart handles it all!

Anyone care to try it and write up a proper review? Perhaps for the JMLA?

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Over at Up to the Waves, Lin shares her observations.

Lin also writes, however:

Pubget is only one of the 3rd party life science search engines that tries to create shortcut to search PubMed. If you are a serious researcher, my advise is using the 3rd party search engines with caution or as a pre-search. Getting comfortable and familiar using PubMed itself is your goal. If you need assistance using PubMed, contact your medical librarians.

I can’t wholly agree with this. Not all 3rd-Party PubMed/Medline tools are meant to replace PubMed, and some can simply do things that PubMed itself cannot. If you are a serious researcher, my advice is to make yourself aware of all the tools at your disposal, and use the best ones for the purpose at hand.

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PubMed-EX is a browser extension that marks up PubMed search results with additional information retrieved from IISR & IASL text-mining services. PubMed-EX’s page mark-up includes section categorization, gene/disease name, and relation.

The mark-ups of PubMed-EX can help researchers quickly focus on key information in retrieved abstracts and can provide additional background information on key terms. Furthermore, our text-mining server carries out all text-mining processing, freeing up users’ resources.

Try this- it’s way cool.

[PubMed-EX]

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What New Users Should Know
(How is Quertle® different?)

1. Find true relationships, not simple co-occurrences
On Quertle, if you search for two or more terms, you will find documents in which those terms occur in a conceptual relationship, not simply scattered within the same document. You won’t always find as many, but you weren’t really going to read 14,578 documents, were you?

2. Quertle understands biology and chemistry
Quertle understands the difference between “TWIST”, the helix-loop-helix transcription factor, and “twist”, the verb. So, use proper capitalization in your query, and you won’t be lost in a sea of irrelevant results.

3. Power Terms™ enable you to query for categories of objects
Use Power Terms™ to query for categories of objects, such as any protein or chemical (not simply the occurrence of the terms). See the Power Terms™ link under the query box for further instructions and the list of currently-supported Power Terms™. Use them; we’ll know what they mean. Want other Power Terms™? Let us know.

4. Useful help
Throughout the site, mouse over the (?) to see helpful hints. To answer many of your other questions, such as why there appear to be duplicate results, please read the Help and FAQ documents (links at the bottom of the page).

Things to look for on the Results page (check the (?) hints on that page):
a. More relevant results
b. Easy filtering and breadcrumb tracking
c. Key concepts automatically identified for you, including members of any Power Term™ categories used in your query

I definitely like the highlighting of search terms and the terms Quertle sees as synonymous:

I like the refinement tools to the right of search results:

It bothers me a bit that Quertle doesn’t actually identify who created or maintains it:

Who is behind Quertle?
Quertle has been created by biomedical scientists, chemists, and linguistic experts, who have many decades of experience with research and finding relevant information to support that research.

Since Quertle is essentially doing keyword searches, its power would be significantly improved if it supported Boolean operators.

Librarians, be sure to check out the Power Terms™. Currently-supported terms are listed here- what others would you like to see?

For more, see Quertle’s Help page.

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The biomedical literature is represented by millions of abstracts available in the Medline database. These abstracts can be queried with the PubMed interface, which provides a keyword-based Boolean search engine. This approach shows limitations in the retrieval of abstracts related to very specific topics, as it is difficult for a non-expert user to find all of the most relevant keywords related to a biomedical topic. Additionally, when searching for more general topics, the same approach may return hundreds of unranked references. To address these issues, text mining tools have been developed to help scientists focus on relevant abstracts. We have implemented the MedlineRanker webserver, which allows a flexible ranking of Medline for a topic of interest without expert knowledge. Given some abstracts related to a topic, the program deduces automatically the most discriminative words in comparison to a random selection. These words are used to score other abstracts, including those from not yet annotated recent publications, which can be then ranked by relevance. We show that our tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time. MedlineRanker is free for use and is available at http://cbdm.mdc-berlin.de/tools/medlineranker.

[PubMed]
Free Full Text: [HTML] [PDF]
Nucleic Acids Res. 2009 July 1; 37: W141–W146.
Published online 2009 July 1. doi: 10.1093/nar/gkp353.
PMCID: PMC2703945

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Pubget’s head developer, Ian Connor, keeps me updated on new developments. I was delighted to hear that Pubget now offers RSS feeds with links to the full-text PDFs via one’s organization’s access. The example in the embedded video below uses an open access journal, but gives a good idea what the new feature looks like.

So the idea is that if you click on the link in the RSS feed, Pubget scrolls down the list of the results, highlights the right paper, and displays that PDF.

Pubget also has a new Firefox extension (available at http://pubget.com/pubget.xpi) for registered users at that will allow them “…to download all papers from a search or latest issue to their local hard drive.” See embedded video below.

If your organization uses Pubget, how about writing a review for the JMLA? Everything I see and hear from Ian looks insanely cool, but I’d love to hear what a medical librarian thinks after a road test.

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I received an email recently which invited me to try the beta version of novo|seek.

Novo|seek is the product of a Madrid-based company called bioalma (a.k.a. Alma Bioinformatics).

novo|seek is an information extraction system for searching the published knowledge in biomedical literature.

novo|seek index the biomedical literature with a text mining technology that enables identify uniquely the key biomedical terms. To do this unambigous identification the technology takes into account external available data and contextual term information. As a result of this indexing technology novo|seek is able to retrive every document where a term is mention no matter the synonym used and discard those documents where the term is use with an unwanted meaning.

So…that means better term mapping? I wonder how the mapping compares to ReleMed.

Since “HRT” is something we know PubMed has had trouble mapping in the past, we’ll try looking for “HRT” in both ReleMed and novo|seek.

ReleMed mapped “HRT” quite nicely:

novo|seek was unable to map “HRT” and produced no results.

Moving on.

With novo|seek you can:

• Extract precise information for over 3 million key biomedical concepts, no matter whether they are diseases, drugs, chemicals compounds, symptoms or genes.
• Retrive key biomedical concepts and bibliographic information to your query.
• Highlight relevant biomedical concepts in the text.
• Filter your results fast and easy.
• Review key information derived from over thousands of documents in a single screen.
• Search for an author and find key research concepts based on the analysis of his or her research.
• Link to relevant external chemical and biological information.

I like the ability to filter by related concepts appearing in the left sidebar, but GoPubMed seems to do this in a similarly useful manner.

Click here for larger image

Click here for larger image

For that matter, PubMed PubReMiner does much the same thing.

Questions for novo|seek:

• How does the relevance sorting work?
• What does novo|seek do that none of the other 3rd-Party PubMed/MEDLINE tools do?
• How does the mapping of search strings to “over 3 million key biomedical concepts” work?
• Could you give an example where novo|seek’s mapping demonstrates superiority to PubMed’s or ReleMed’s?

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Internet Cool Tools for Physicians is in Google Book Search

Stephen Francoeur made this little video:

The Mid-Atlantic Chapter of the MLA mentioned it on their blog.

The MLA’s Taskforce on Social Networking Software posted about it, calling it “…an accessible, illustrated and contemporary guide to online tools in medicine.”

Laika, whose blog has quickly become one of my favorite MedLib blogs, mentioned it, as did Creaky.

I’m watching WorldCat.org with interest to see which libraries are getting it (though Duke’s copy doesn’t show up yet).

Dr. Shock (MD, PhD) gave it a very nice review.

I’m lucky to count as friends people like Meredith Farkas and Michael Stephens, both of whom thought the book worthy of mention on their very popular blogs.

Gosh- Brandi blogged about it way back in August- well before it as released!

I’m pleased to see mention of it in languages other than English.

The President and CEO of Community General Hospital blogged about it.

It has gotten some buzz on Twitter.

We’re anxious to hear any feedback you have about the book- please let us know what you think….and what you think needs to be added or changed for the second edition!

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Yay!

You can buy a copy from:
Springer Publishing

or here:

I’m looking forward to eventually seeing it in WorldCat.

Congratulations to Melissa Rethlefsen (who wrote a heck of a lot more than I did)! You should really go email Melissa now and tell her how much she rocks.

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