From something I saw in Facebook recently:
Q: Will NextBio do away with PubMed?
A: Absolutely not. In order to even have a chance at making PubMed irrelevant, a 3rd-party tool would have to be free. I believe I have played with the vast majority of 3rd-party PubMed/MEDLINE tools available (see this post category for details).
Q: …will Pubget do away with PubMed?
A: In some libraries for some users, PubGet will be a the preferred option. Will it make PubMed irrelevant? Good lord, no.
Suspect they use PubMed to get their lit content, esp since they say they include all the full text from PubMed Central.
K is absolutely right. Both PubGet and NextBio get their data through NCBI API tools.
However, PubMed makes the index of the world’s medical literature available to millions and it used worldwide as an essential healthcare tool. Ask yourself: Do you want to trust a private corporation to take good and ethical care of such an important public good? I don’t. I’d rather trust the NLM.
From the site:
Examples of usage
- To find out just how many papers have been indexed by PubMed every year, enter an empty query (simply press ‘Build Trend’);
- To find the history of a subject, enter a few keywords describing the subject. For example, clopidogrel will tell you that discussion about this drug first appeared in 1987, was ocasional (under one paper a month) by 1996 and really took off in after 2000;
- To make statistics of the languages of papers as indexed by PubMed and how they evolved in time enter something like fre[la] and you will see their number is geting reduced in time, despite the increase in the general number of papers, so the prevalence of papers in french in the database falls from about 10%, forty years ago, to less than 2% in 2004;
- To see how many papers have been published in journals published in a given country year by year enter something like france[pl] and one can see that the number of biomedical papers published in France, indexed in Medline, is quite constant over the years, despite the previous statistics;
- queries can be combined, for example:
and you will see that a progressive number of papers published in france, but in english, are indexed by PubMed every year;
- trying pitie-salpetriere[ad] will show you that, while the number of papers published from this famous hospital is increasing yearly, the fraction of these papers from all papers in PubMed in the respective year is relatively constant.
[via Mike G.]
(Previously mentioned here)
I just tried this again and I don’t think it works properly any longer. 🙁
I suspect I’m forgetting another tool or two that will do this. If you know what they are, let me know?
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.
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.
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?
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.
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.
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.
For more, see Quertle’s Help page.
Learned about MedlineRanker through this recent article:
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.
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.
(First started drafting this post on 10/27/2008, then posted an incomplete draft by accident on 2/2/2009. Sorry ’bout that.)
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.
novo|seek was unable to map “HRT” and produced no results.
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.
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?
So the book is getting some attention!
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.”
Dr. Shock (MD, PhD) gave it a very nice review.
Gosh- Brandi blogged about it way back in August– well before it as released!
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! 🙂
Got my hands on my copies of the book today! How exciting!
You can buy a copy from:
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.
My apologies to the awfully nice folks who attended the CE course I taught at UNYOC a couple of weeks ago! I’ve taken far too long to get these slides posted:
Also: I’ll be on a panel at NYLA tomorrow (Friday, 11/6/2008) afternoon at 4:00 PM- please say hello if you’re going to be there! As usual at these sorts of things, I’ll know almost nobody. But hey- I might get to meet Polly Farrington!
Last week I posted Rachel Walden’s readlly good idea for a useful 3rd-party PubMed/MEDLINE tool and received several exciting responses.
Martin Gerken was the first to make an attempt that you can try at:
…but Rachel got some error messages from it.
Martin and Rebecca both suggested using GoPubMed.
David (not David Rothman) confirmed that GoPubMed worked nicely but had some problems (which GoPubMed’s Dr. Liliana Barrio-Alvers later answered).
David’s (not Rothman) Tool
David (again, not David Rothman) also made an attempt at creating the tool that Rachel asked for that you can try here:
I threw in a list of PMIDs and got useful results presented in a pleasant manner:
Rajarshi also built a Ubiquity command (more on Ubiquity here) that functions reasonably well as an interface- though still not as well as a simple Web form- and without a simple Web form, the tool isn’t really available to a lot of potential users.
You people rule.
Rachel Walden writes:
What I’d like to do is to be able to enter the PMIDs of several citations and have the tool search MEDLINE via PubMed for the assigned MeSH terms, and return a single list of the terms used by any of the entered citations with a measurement of frequency. For example, if I input PMIDs 16234728, 15674923, and 17443536, the tool would return results telling me that 100% or 3 of 3 use the term “Catheters, Indwelling”, 2 of 3 use “Time Factors,” 1 of the 3 uses “Urination Disorders,” and so on. Although this example uses 3 PMIDs, I’d like to be able to input at least 10, just based on personal experience.
This would be useful in situations where a single “gold standard” search strategy is needed for the purposes of a systematic review or other process – for example, we may find a number of great articles on a topic by using multiple approaches to the search, but have difficulty developing a single strategy that captures them all due to differences in indexing. In effect, it would inform reverse-engineering a search strategy from a pool of relevant citations. It might also be helpful as a teaching tool for medical librarianship students and those new to the profession.
No, it wouldn’t change my medical librarian life, but it would make it easier from time to time!
This is a really great idea and I don’t think it’d be too difficult to implement for a Web applications developer who knows how to work with NCBI’s API tools. Any takers? – David
[EDIT: Sandy Swanson notes that “It appears that foreign-language articles are not included in Semantic Medline.” I guess that isn’t surprising. After all, its NLP has to be language-specific and there are more articles in English than any other language.]
I knew that an interface for MEDLINE using Natural Language Processing was being developed at Lister Hill, and PubFocus has been called “semantic MEDLINE” too, but I heard a couple of days ago about a tool from Cognition Technologies called (appropriately enough) SemanticMEDLINE.
I’m going to need to play with it a bit more before having any idea if it’ll be useful to me, but it is interesting.
I searched for “Are probiotics an effective therapy for Crohn’s disease or ulcerative colitis?” and got the following results:
The most interesting part of this is how the panel on the right has drop-down menus for the terms it recognizes, allowing the user to make sure the search is using the correct terms/definitions.
What I don’t understand yet is how these definitions are utilized in performing the PubMed/MEDLINE search.
Be sure to check out the HELP page for notes on the way it uses AND, OR, WITH, and WITHIN operators, the way it uses quotation marks, and how to work with capitalization.
I’ve previously posted about commercial applications for managing PDF files that access PubMed for article metadata (including iPapers, Papers, Sente, BibDesk, and Librarian) but I just stumbled across a new (to me) open source option called PubMedPDF.
Built on the open-source content management system XOOPS (XOOPS Cube fork), PubMedPDF “…is a Document Management System which provides various useful functions. This uses ID which is used in the PubMed Database to automatically generate paper information. If the paper you want to register has that ID, you don’t have to input any information.”
Also of interest to Mac users is the BioMed Lab Portal Server Package.
“e-LiSe (e-Literature Searcher) is an easy-to-use web-based application which finds biomedical information truly related to English words provided by the user. The program uses PubMed database of scientific abstracts as the source of data and a novel bio-linguistic statistical method (based on Z-score), to discover true correlations, even when they are low-frequency associations.
e-LiSe is also capable of finding names of researchers correlated to the information searched by the user. It can function as a name reference engine, answering questions like “who is working on specified subject?” or “what are the coworkers/collaborators of a certain person?”. For the latter the software uses the list of co-authors of each publication a researcher has written to display connections between scientists.”
PostGenomic’s PubMed Faceoff is the first 3rd Party PubMed/MEDLINE Tool I’ve looked at that really made me chuckle.
This site applies a simple, photorealistic variant of the Chernoff Faces visualization technique to impact factor data for papers in the PubMed database of biomedical literature.
Basically it allows you to search PubMed and have the results represented as a set of human faces.
Each paper is represented as a face. The ethnicity and gender of the face is selected at random for visual interest – you can turn this feature off if you so choose.
The age of a face correlates with the publication date of the paper. Younger faces are more recent papers.
A smile means that the paper has been cited more times than expected (based on its age). Larger smiles mean more citations.
A frown means that the paper has been cited far less than you might expect.
The raised eyebrows correlate with the impact factor (sort of – actually the Eigenfactor) of the journal in which the paper was published.
Some example search results:
I absolutely appreciate the concept (potentially being able to estimate several properties of an article at a glance)- it’s just that some of the facial expressions crack me up.