O'Really?

February 18, 2013

Metadata is after-data: not just data “about” data

Filed under: Science,technology — Duncan Hull @ 11:08 am
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metacrap

Metadata gets many people hot under the collar, especially Cory Doctorow and Paul Downey.

Lots of people will tell you that metadata is “data about data”. While this definition is commonly used, it isn’t very helpful. Instead, it can be more useful to think of metadata as after-data. This, after all, is one the original meanings of the word meta (μετά) in the greek language.

Metadata makes the world go round

Ask any data wonk and they’ll probably tell you that metadata makes the world go round [1]. The internets is built on metadata. For example, the image in this blog post, you might search or browse to the data through it’s metadata not the data itself – the actual jpeg image file. The usual line is that the image metadata (title, author, date, tags etc) is data about data, e.g. it is data about the image.

Where you draw the line between data and metadata is a bit arbitrary. Is the author of the photograph data or metadata? It’s often difficult to tell because the defnition of metadata is so wishy-woshy, it’s almost meaningless. Some people call it metacrap, see picture on the right.

Afterdata: what comes afterwards

In Greek, μετά (meta) also means after. So metadata can be thought of as afterdata and this helps shed a little more light on what metadata actually is. Data is what you see or hear first. Metadata is everything that comes after. Simples!

It’s a small, possibly pedantic, distinction betweeen about-data and after-data but for my money, a potentially useful one. I wonder what the internets will make of it?

References

  1. Li Ding et al  (2004). Swoogle: a search and metadata engine for the semantic web Proceedings of the thirteenth ACM international conference on Information and knowledge management, 652-659 DOI: 10.1145/1031171.1031289

January 18, 2013

How to export, delete and move your Mendeley account and library #mendelete

Filed under: publishing,Science,technology — Duncan Hull @ 1:00 pm
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Deleteme

Delete. Creative Commons licensed picture by Vitor Sá – Virgu via Flickr.com

News that Reed Elsevier is in talks to buy Mendeley.com will have many scientists reaching for their “delete account” button. Mendeley has built an impressive user-base of scientists and other academics since they started, but the possibility of an Elsevier takeover has worried some of its users. Elsevier has a strained relationship with some groups in the scientific community [1,2], so it will be interesting to see how this plays out.

If you’ve built a personal library of scientific papers in Mendeley, you won’t just want to delete all the data, you’ll need to export your library first, delete your account and then import it into a different tool.

Disclaimer: I’m not advocating that you delete your mendeley account (aka #mendelete), just that if you do decide to, here’s how to do it, and some alternatives to consider. Update April 2013, it wasn’t just a rumour.

Exporting your Mendeley library

Open up Mendeley Desktop, on the File menu select Export. You have a choice of three export formats:

  1. BibTeX (*.bib)
  2. RIS – Research Information Systems (*.ris)
  3. EndNote XML (*.xml)

It is probably best to create a backup in all three formats just in case as this will give you more options for importing into whatever you replace Mendeley with. Another possibility is to use the Mendeley API to export your data which will give you more control over how and what you export, or trawl through the Mendeley forums for alternatives. [update: see also comments below from William Gunn on exporting via your local SQLite cache]

Deleting your Mendeley account #mendelete

Login to Mendeley.com, click on the My Account button (top right), Select Account details from the drop down menu and scroll down to the bottom of the page and click on the link delete your account. You’ll be see a message We’re sorry you want to go, but if you must… which you can either cancel or select Delete my account and all my data. [update] To completely delete your account you’ll need to send an email to privacy at mendeley dot com. (Thanks P.Chris for pointing this out in the comments below)

Alternatives to Mendeley

Once you have exported your data, you’ll need an alternative to import your data into. Fortunately, there are quite a few to choose from [3], some of which are shown in the list below. This is not a comprehensive list, so please add suggestions below in the comments if I missed any obvious ones. Wikipedia has an extensive article which compares all the different reference management software which is quite handy (if slightly bewildering). Otherwise you might consider trying the following software:

One last alternative, if you are fed up with trying to manage all those clunky pdf files, you could just switch to Google Scholar which is getting better all the time. If you decide that Mendeley isn’t your cup of tea, now might be a good time to investigate some alternatives, there are plenty of good candidates to choose from. But beware, you may run from the arms of one large publisher (Elsevier) into the arms of another (Springer or Macmillan which own Papers and ReadCube respectively).

References

  1. Whitfield, J. (2012). Elsevier boycott gathers pace Nature DOI: 10.1038/nature.2012.10010
  2. Van Noorden, R. (2013). Mathematicians aim to take publishers out of publishing Nature DOI: 10.1038/nature.2013.12243
  3. Hull, D., Pettifer, S., & Kell, D. (2008). Defrosting the Digital Library: Bibliographic Tools for the Next Generation Web PLoS Computational Biology, 4 (10) DOI: 10.1371/journal.pcbi.1000204
  4. Attwood, T., Kell, D., McDermott, P., Marsh, J., Pettifer, S., & Thorne, D. (2010). Utopia documents: linking scholarly literature with research data Bioinformatics, 26 (18) DOI: 10.1093/bioinformatics/btq383

July 1, 2011

Anything that calls itself a Science, probably isn’t…

Filed under: funny,Science — Duncan Hull @ 12:01 am
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Way Cool Science Stuff by Mark A. Hicks www.markix.netScience, is a big word that gets used and abused with reckless abandon. Virtually any discipline can award itself extra kudos by adding the magic S word at the end. For example, which sounds weightier, sports studies or sports science?

This phenomenon has been noticed many times before, for example, the philosopher John Searle once remarked that:

“Science has become something of an honorific term, and all sorts of disciplines that are quite unlike physics or chemistry are eager to call themselves ‘sciences‘.

A good rule of thumb to keep in mind is that anything that calls itself a science probably isn’t.” –see [1,2]

So let’s make a list. Starting with things that probably aren’t a Science because they call themselves one:

We could carry on for ages with this list and eventually include:

So are maths, physics, chemistry, biology etc real sciences™ too? Using Searle’s definition, it’s difficult to say. To avoid confusion, it might be a good idea to use a subjects non-scientific original name (“biology” rather than “life science”) that way, we know (paradoxically) they are real sciences. Probably.

References

  1. John R. Searle (1986). Minds, Brains and Science (1984 Reith Lectures) Harvard University Press ISBN:0674576330 (see also audio from the BBC Reith lecture archive) not  Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press. ISBN:052109626X (as originally stated in the first version of this post)
  2. Fuller quotation: “Science has become something of an honorific term, and all sorts of disciplines that are quite unlike physics and chemistry are eager to call themselves ‘sciences’. A good rule of thumb to keep in mind is that anything that calls itself ‘science’ probably isn’t — for example, Christian science, or military science, and possibly even cognitive science or social science. The word ‘science’ tends to suggest a lot of researchers in white coats waving test tubes and peering at instruments. To many minds it suggests an arcane infallibility. The rival picture I want to suggest is this: what we are all aiming at in intellectual disciplines is knowledge and understanding. There is only knowledge and understanding, whether we have it in mathematics, literary criticism, history, physics, or philosophy. Some disciplines are more systematic than others, and we might want to reserve the word ‘science’ for them.”
  3. Peter J. Denning (2005). Is computer science science? Communications of the ACM, 48 (4) DOI: 10.1145/1053291.1053309

June 28, 2011

Impact Factor Boxing 2011

Filed under: publishing — Duncan Hull @ 8:31 pm
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Khmer Boxing by lecercle, on Flickr[This post is part of an ongoing series about impact factors. See Impact Factor Boxing 2012 for the latest figures.]

Well it’s that time again. The annual sweaty fist-fight for supremacy between the scientific journals, as measured by impact factors, is upon us. Much ink (virtual and actual) has been spilt on the subject of impact factors, which we won’t add to here, other than to say:

Hey look, the “European” journals might be catching up with the “American” ones. [1]

So, love them, loathe them, use them, abuse them, ignore them or obsess over them… here’s a tiny selection of the 10,196 journals that are tracked in Journal Citation Reports (JCR) ordered by increasing impact.

WARNING: Abusing these figures can seriously damage your Science – you have been warned! (normal caveats apply)

Journal 2010 data from isiknowledge.com/JCR Eigenfactor™ Metrics
Total Cites Impact Factor 5-Year Impact Factor Immediacy Index Articles Cited Half-life Eigenfactor™ Score Article Influence™ Score
The Naval Architect* 16 0.005 0.004 0.005 189 0.00002 0.001
BMC Bioinformatics 12653 3.028 3.786 0.475 690 3.9 0.08086 1.495
PLoS ONE 42795 4.411 4.610 0.515 6714 2.1 0.32121 1.943
OUP Bioinformatics 40659 4.877 6.325 0.707 700 5.7 0.17973 2.649
PLoS Computational Biololgy 6849 5.515 6.251 0.727 406 2.8 0.06075 2.984
Genome Biology 14194 6.885 7.353 1.295 173 4.9 0.07688 3.585
Nucleic Acids Research 100444 7.836 7.314 1.755 1101 7.0 0.32867 3.016
Briefings in Bioinformatics 2886 9.283 7.395 1.204 49 5.8 0.01013 2.737
PLoS Biology 18453 12.469 14.375 2.706 214 4.1 0.16084 8.225
Science 469704 31.364 31.769 6.789 862 9.0 1.46485 16.859
Nature 511145 36.101 35.241 8.791 862 9.1 1.74466 19.334
New England Journal of Medicine 227674 53.484 52.362 10.675 345 7.5 0.69167 21.366
CA – A Cancer Journal for Clinicians ** 9801 94.262 70.216 8.667 18 3.8 0.04923 24.782

* The Naval Architect is included here for reference as it has the lowest non-zero impact factor of any science journal. A rather dubious honour…

** The Cancer Journal for Clinicians is the highest ranked journal in science, is included here for reference.

[Creative Commons licensed picture of Khmer boxing picture by lecercle]

References

  1. Karageorgopoulos, D., Lamnatou, V., Sardi, T., Gkegkes, I., & Falagas, M. (2011). Temporal Trends in the Impact Factor of European versus USA Biomedical Journals PLoS ONE, 6 (2) DOI: 10.1371/journal.pone.0016300

April 1, 2011

Circular logic is the best type of logic, because it’s circular

Filed under: education — Duncan Hull @ 6:53 am
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Me Fish!One of the great things about logic is there are so many different flavours to choose from. If you thought that logic came in just one flavour (vanilla), then think again. Now, I Am Not A Logician, but I can’t help marvelling at the bewildering array of  logical flavours on offer including, but not limited to:

So if you’ve ever wondered which logic was the “best” kind of logic, then maybe Antonio Cangiano can provide an answer. Antonio recently stated that:

Easy, you see? Circular logic, is logically the best…

References

  1. Lucas Laursen (2009). Computational biology: Biological logic Nature, 462 (7272), 408-410 DOI: 10.1038/462408a
  2. Critical Reasoning for Beginners, Oxford University iTunesU

[Creative Commons licensed circular shot by Hamed Saber]

June 22, 2010

Impact Factor Boxing 2010

Filed under: data mining,informatics,publishing — Duncan Hull @ 7:39 am
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Golden Gloves Prelim Bouts by Kate Gardiner[This post is part of an ongoing series about impact factors. See this post for the latest impact factors published in 2012.]

Roll up, roll up, ladies and gentlemen, Impact Factor Boxing is here again. As with last year (2009), the metrics used in this combat sport are already a year out of date. But this doesn’t stop many people from writing about impact factors and it’s been an interesting year [1] for the metrics used by many to judge the relative value of scientific work. The Public Library of Science (PLoS) launched their article level metrics within the last year following the example of BioMedCentral’s “most viewed” articles feature. Next to these new style metrics, the traditional impact factors live on, despite their limitations. Critics like Harold Varmus have recently pointed out that (quote):

“The impact factor is a completely flawed metric and it’s a source of a lot of unhappiness in the scientific community. Evaluating someone’s scientific productivity by looking at the number of papers they published in journals with impact factors over a certain level is poisonous to the system. A couple of folks are acting as gatekeepers to the distribution of information, and this is a very bad system. It really slows progress by keeping ideas and experiments out of the public domain until reviewers have been satisfied and authors are allowed to get their paper into the journal that they feel will advance their career.”

To be fair though, it’s not the metric that is flawed, more the way it is used (and abused) – a subject covered in much detail in a special issue of Nature at http://nature.com/metrics [2,3,4,5]. It’s much harder than it should be to get hold of these metrics, so I’ve reproduced some data below (fair use? I don’t know I am not a lawyer…) to minimise the considerable frustrations of using Journal Citation Reports (JCR).

Love them, loathe them, use them, abuse them, ignore them or obsess over them … here’s a small selection of the 7347 journals that are tracked in JCR  ordered by increasing impact.

Journal Title 2009 data from isiknowledge.com/JCR Eigenfactor™ Metrics
Total Cites Impact Factor 5-Year Impact Factor Immediacy Index Articles Cited Half-life Eigenfactor™  Score Article Influence™ Score
RSC Integrative Biology 34 0.596 57 0.00000
Communications of the ACM 13853 2.346 3.050 0.350 177 >10.0 0.01411 0.866
IEEE Intelligent Systems 2214 3.144 3.594 0.333 33 6.5 0.00447 0.763
Journal of Web Semantics 651 3.412 0.107 28 4.6 0.00222
BMC Bionformatics 10850 3.428 4.108 0.581 651 3.4 0.07335 1.516
Journal of Molecular Biology 69710 3.871 4.303 0.993 916 9.2 0.21679 2.051
Journal of Chemical Information and Modeling 8973 3.882 3.631 0.695 266 5.9 0.01943 0.772
Journal of the American Medical Informatics Association (JAMIA) 4183 3.974 5.199 0.705 105 5.7 0.01366 1.585
PLoS ONE 20466 4.351 4.383 0.582 4263 1.7 0.16373 1.918
OUP Bioinformatics 36932 4.926 6.271 0.733 677 5.2 0.16661 2.370
Biochemical Journal 50632 5.155 4.365 1.262 455 >10.0 0.10896 1.787
BMC Biology 1152 5.636 0.702 84 2.7 0.00997
PLoS Computational Biology 4674 5.759 6.429 0.786 365 2.5 0.04369 3.080
Genome Biology 12688 6.626 7.593 1.075 186 4.8 0.08005 3.586
Trends in Biotechnology 8118 6.909 8.588 1.407 81 6.4 0.02402 2.665
Briefings in Bioinformatics 2898 7.329 16.146 1.109 55 5.3 0.01928 5.887
Nucleic Acids Research 95799 7.479 7.279 1.635 1070 6.5 0.37108 2.963
PNAS 451386 9.432 10.312 1.805 3765 7.6 1.68111 4.857
PLoS Biology 15699 12.916 14.798 2.692 195 3.5 0.17630 8.623
Nature Biotechnology 31564 29.495 27.620 5.408 103 5.7 0.14503 11.803
Science 444643 29.747 31.052 6.531 897 8.8 1.52580 16.570
Cell 153972 31.152 32.628 6.825 359 8.7 0.70117 20.150
Nature 483039 34.480 32.906 8.209 866 8.9 1.74951 18.054
New England Journal of Medicine 216752 47.050 51.410 14.557 352 7.5 0.67401 19.870

Maybe next year Thomson Reuters, who publish this data, could start attaching large government health warnings (like on cigarette packets) and long disclaimers to this data? WARNING: Abusing these figures can seriously damage your Science – you have been warned!

References

  1. Rizkallah, J., & Sin, D. (2010). Integrative Approach to Quality Assessment of Medical Journals Using Impact Factor, Eigenfactor, and Article Influence Scores PLoS ONE, 5 (4) DOI: 10.1371/journal.pone.0010204
  2. Abbott, A., Cyranoski, D., Jones, N., Maher, B., Schiermeier, Q., & Van Noorden, R. (2010). Metrics: Do metrics matter? Nature, 465 (7300), 860-862 DOI: 10.1038/465860a
  3. Van Noorden, R. (2010). Metrics: A profusion of measures Nature, 465 (7300), 864-866 DOI: 10.1038/465864a
  4. Braun, T., Osterloh, M., West, J., Rohn, J., Pendlebury, D., Bergstrom, C., & Frey, B. (2010). How to improve the use of metrics Nature, 465 (7300), 870-872 DOI: 10.1038/465870a
  5. Lane, J. (2010). Let’s make science metrics more scientific Nature, 464 (7288), 488-489 DOI: 10.1038/464488a

[Creative Commons licensed picture of Golden Gloves Prelim Bouts by Kate Gardiner ]

March 16, 2010

DNA, Diversity and You at Cambridge Science Festival

Filed under: communication,data mining — Duncan Hull @ 8:40 am
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Sequence BraceletsAs part of Cambridge Science festival last weekend, I joined a group of about 40 volunteers from The Sanger and EBI at an event “DNA, diversity and you”. This was a series of education and outreach events designed to explore how differences in your genetic code make you different from other individuals, and what makes the humans different from other living things –  with a bit of computational biology thrown in for good measure.  Here are some notes on a selection of the activities, in case you ever find yourself trying to explain biology, computer science or bioinformatics to anyone aged 4-18 and beyond. These resources are all tried, tested and fun to work with, for students and teachers alike:

  1. DNA origami create your own origami DNA molecule, and hands on way of learning abou tthe double helix structure of DNA
  2. DNA sequence bracelets (see picture right). Thread coloured beads according to sequence sections from a range of organisms including trout, chimpanzee, butterfly, a flesh-eating microbe and rotting corpse flower.
  3. Yummy gummy DNA (under 5’s) build your own DNA helix out of sweets and cocktail sticks. Then scoff it all afterwards.
  4. What’s my name in DNA? find out what your name is in DNA, and what the corresponding (hypothetical) protein is using software from deCODE.
  5. Function Finders translate DNA into a sequence of amino acids using wooden translator blocks, then find out which organism the amino acid sequence is from.
  6. Genome sizes (with seatbelts) Rank organisms (inc. human, zebrafish, mosquito, sugar cane and yeast) and find out if they are in the right order. Results are often not what you would expect.
  7. Play your genes right. A card-based guessing game which compares the number of genes in the human genome with the number of genes from a range of different organisms include the flu virus, E. coli bacteria, armadillo, rice plant and others.
  8. Genome Jigsaws for illustrating the process of finishing supposedly “finished” genomes, by putting together a square sequence jigsaw following base pairing rules to end up with a complete finished square.
  9. DNA Time Team examines of aspects ancestry and evolution. The activity encourages people to work out the sequence of a common ancestor by filling in the gaps on a simple evolutionary tree.
  10. Spot the difference with proteins. Comparing Heat Shock Protein (HSP) in human and other organisms to illustrate how different regions of the protein vary between different organisms and how this affects function.
  11. Ready, steady sort: a sorting network that demonstrates one technique that computers use to sort through large amounts of information like sequence data. This comes straight from Computer Science Unplugged by Tim Bell, Mike Fellows and Ian Witten. This activity can be done either as a smaller board game, or as a larger floor game. Either way, it’s a lot of fun, especially if you time people for an added competitive element (see video below)

There were a whole bunch of new activities at the festival this year, maybe these will appear on the your genome website in the future. Anyway, it was great fun to get involved, there is nothing quite like the challenge of explaining parallel computing to young kids, teenagers and their parents – actually much easier than you’d think if you’ve got access to great teaching materials.

Thanks to Francesca Gale and Louisa Wright for all the hard work that went into organising this fun and successful event.

November 5, 2009

Artemether: Entity of the Month

Filed under: biocuration — Duncan Hull @ 1:27 pm
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ArtemetherNovember’s entity of the month at ChEBI is the antimalarial drug Artemether. This accompanies release 62 of ChEBI, not just yet another incremental release but an increase of more than twentyfold in the number of entities in ChEBI, thanks to merging of data between an updated ChEBI [1] and ChEMBL [2]. ChEBI now (as of release 62) has over 455,000 total entities, compared to just under 19,000 in the previous version (release 61), see ChEBI news for details. The text below on Artemether is reproduced from the ChEBI website, where content is available under a Creative Commons license:

Artemether (CHEBI:195280) is a lipid-soluble antimalarial for the treatment of multi-drug resistant strains of Plasmodium falciparum malaria. First prepared in 1979 [3], it is a methyl ether of the naturally occurring sesquiterpene lactone (+)-artemisinin, which is isolated from the leaves of Artemisia annua L. (sweet wormwood), the traditional Chinese medicinal herb known as Qinghao. However, because of artemether’s extremely rapid mode of action (it has an elimination half-life of only 2 hours, being metabolized to dihydroartemisinin which then undergoes rapid clearance), it is used in combination with other, longer-acting, drugs. One such combination, licensed in April of this year by the WHO, is Coartem in which the artemether is mixed with lumefantrine – a racemic mixture of a synthetic fluorene derivative known formerly as benflumetol – which has a much longer and pharmacologically complementary terminal half-life of 3–6 days, allowing the two drugs to act synergistically against Plasmodium.

The molecule of artemether is interesting because of its extreme rigidity, with very few rotational bonds. Unlike quinine class antimalarial drugs, it has no nitrogen atom in its skeleton. However, an important chemical feature (and unique in drugs) is the presence of an O–O endoperoxide bridge which is essential for its antimalarial activity, as it is this bridge which is split in an interaction with heme, blocking the conversion into hemozoin and thus releasing into the parasite heme and a host of free radicals which attack the cell membrane.

Artemether is fully Rule-of-Five compliant and has recently also been under investigation as a possible candidate for cancer treatment [4,5].

GO ChEBI!

References

  1. de Matos, P., Alcantara, R., Dekker, A., Ennis, M., Hastings, J., Haug, K., Spiteri, I., Turner, S., & Steinbeck, C. (2009). Chemical Entities of Biological Interest: an update Nucleic Acids Research DOI: 10.1093/nar/gkp886
  2. Warr, W. (2009). ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI) Journal of Computer-Aided Molecular Design, 23 (4), 195-198 DOI: 10.1007/s10822-009-9260-9
  3. Li, Y. et al. (1979) K’o Hsueh T’ung Pao, 24, 667 [Chem. Abstr., 91, 211376u].
  4. Singh, N., & Panwar, V. (2006). Case Report of a Pituitary Macroadenoma Treated With Artemether Integrative Cancer Therapies, 5 (4), 391-394 DOI: 10.1177/1534735406295311
  5. Wu, Z., Gao, C., Wu, Y., Zhu, Q., Yan Chen, ., Xin Liu, ., & Chuen Liu, . (2009). Inhibitive Effect of Artemether on Tumor Growth and Angiogenesis in the Rat C6 Orthotopic Brain Gliomas Model Integrative Cancer Therapies, 8 (1), 88-92 DOI: 10.1177/1534735408330714

July 24, 2009

Escape from the impact factor: The Great Escape?

Filed under: communication,publishing,Science — Duncan Hull @ 11:40 am
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The Great Escape with Steve McQueenQuite by chance, I stumbled on this interesting paper [1] yesterday by Philip Campbell who is the Editor-in-Chief of the scientific über-journal Nature [2]. Here is the abstract:

As Editor-in-Chief of the journal Nature, I am concerned by the tendency within academic administrations to focus on a journal’s impact factor when judging the worth of scientific contributions by researchers, affecting promotions, recruitment and, in some countries, financial bonuses for each paper. Our own internal research demonstrates how a high journal impact factor can be the skewed result of many citations of a few papers rather than the average level of the majority, reducing its value as an objective measure of an individual paper. Proposed alternative indices have their own drawbacks. Many researchers say that their important work has been published in low-impact journals. Focusing on the citations of individual papers is a more reliable indicator of an individual’s impact. A positive development is the increasing ability to track the contributions of individuals by means of author-contribution statements and perhaps, in the future, citability of components of papers rather than the whole. There are attempts to escape the hierarchy of high-impact-factor journals by means of undifferentiated databases of peer-reviewed papers such as PLoS One. It remains to be seen whether that model will help outstanding work to rise to due recognition regardless of editorial selectivity. Although the current system may be effective at measuring merit on national and institutional scales, the most effective and fair analysis of a person’s contribution derives from a direct assessment of individual papers, regardless of where they were published.

It’s well worth reading the views of the editor of an important closed-access journal like Nature, a world champion heavyweight of Impact Factor Boxing. So their view on article-level bibliometrics and novel models of scientific publishing on the Web like PLoS ONE is enlightening. There are some interesting papers in the same issue, which has a special theme on the use and misuse of bibliometric indices in evaluating scholarly performance. Oh, and the article is published in an Open Access Journal too. Is it just me, or is there a strong smell of irony in here?

References

  1. Philip Campbell (2008). Escape from the impact factor Ethics in Science and Environmental Politics, 8, 5-7 DOI: 10.3354/esep00078
  2. Philip Campbell (1995). Postscript from a new hand Nature, 378 (6558), 649-649 DOI: 10.1038/378649b0
  3. John Sturges (1963) The Great Escape

June 19, 2009

Nettab 2009 Day Three: Semantic Integration

Filed under: conferences — Duncan Hull @ 11:26 am
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Catania ElephantA brief report (well just some scribbled notes, bullet points and links really) on the third and final day of Network Applications and Tools in Biology (NETTAB) 2009 in Catania, Sicily. There was a special section on Methods and Tools for RNA Structure and Functional Analysis. Disclaimer: RNA mania isn’t really my thing – so the RNA presentations and papers are grossly under-represented in this mini-report (sorry).

  • Keynote: Semantically Integrated eCommunities in Biomedicine: Next-Generation Models of Biomedical Communication, Tim Clark Massachusetts General Hospital and Harvard Medical School, Boston. His presentation opened by asking: What do the following have in common?

    1. Alzheimer’s Disease
    2. Huntington’s Disease
    3. Nicotine Addiction
    4. Schizophrenia
    5. Bipolar Disorder
    6. Autism
    7. Parkinson’s Disease
    8. ALS (Amyotrophic lateral sclerosis)
    9. Neuropathic Pain
    10. Major Depressive Disorder
    11. Cancer (multiple forms)

    Answer:

    1. Highly complex disorders
    2. Much information, incomplete understanding
    3. Inadequate treatment options
    4. Huge cost in human suffering
    5. Multi-factorial causality
    6. Require multi-disciplinary collaboration for progress to understanding and cure

    Tim discussed using The Science Collaboration Framework (SCF) a reusable, semantically-aware toolkit for building on-line communities. These make heavy use of Open Linked Data, controlled vocabularies and  Drupal to build websites to tackle the above disorders. For example pdonlineresearch.org (Parkinson’s Disease), StemBook.org (Harvard Stem Cell Institute) and alzforum.org (Alzheimers) [1]. The controlled vocabulary and ontology approach works well for understood stuff (where named entities are known) but not so good at the outer boundaries of our knowledge. Reusable framework for building web communities, Uses shared ontologies/vocabularies, Open source, freely available.

  • Michaela Guendel (Leaf Bioscience) presented DC-THERA Directory: A Knowledge Management System to Support Collaboration on Dendritic Cell and Immunology Research,  using cell type ontology, dendritic cell ontology, chebi, obi. Project involves Andrea Splendiani, Ciro Scognamiglio and Marco Brandizi
  • GePh-CARD: an information exchange application for an Hub & Spoke Network for Skeletal Dysplasias was presented by M. Mordenti & L. Sangiorgi
  • Panel Discussion: Collaborative and Social Bioinformatics Research and Development: Why, When, Who and How? Alex Bateman, Tim Clark, Duncan Hull and all participants. This panel discussion concentrated on Who? (experts vs. non experts, crowds vs. individuals, how to motivate and reward people to contribute to online communities. community annotation of data only possible when curators cede control of data) and then Where? (open wikis vs. closed ones, private vs. public data, wikis often not suitable for highly structured data, centralised vs. distributed systems)
  • Keynote: Bacterial Phylogeny and Taxonomy in the High-Throughput Sequencing World, Gabriel Valiente
  • Magdalena Musielak (has worked with Piotr Byzia) presented RNA tertiary structure prediction with ModeRNA,
  • Olivier Perriquet presented Improved heuristic for pairwise RNA secondary structure prediction,
  • Giampaolo Bella talked about Analysing microRNA by Theorem Proving. qualitative logic proving before quantitative experimental measures e.g. “shall we go to restaurant” before “how much does it cost”?
  • Mapping miRNA genes on human fragile sites and translocation breakpoints Alfredo Ferro et al.
  • Keynote: Computational challenages in the study of small RNAs Doron Betel, memorial sloan-kettering cancer center
  • microrna.gr. a suite of web based tools for elucidating microrna function was presented by Giorgo L. Papadopoulous, DIANA bioinformatics lab, biomedical Science research center, Alexander Fleming, Vari, Athens, Greece
  • Last but not least there was miRScape: a cytoscape plugin to annotate biological networks with microRNAs

The Tenth NETTAB (2010) Workshop will be in Rome, where the theme will be Oncology Bioinformatics and will be held at the end of  May or beginning of  June 2010.

References

  1. Das, S., Girard, L., Green, T., Weitzman, L., Lewis-Bowen, A., & Clark, T. (2009). Building biomedical web communities using a semantically aware content management system Briefings in Bioinformatics, 10 (2), 129-138 DOI: 10.1093/bib/bbn052
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