We are happy to announce that AppJet Inc. has been acquired by Google. The EtherPad team will continue its work on realtime collaboration by joining the Google Wave team.

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The EtherPad site will stay online through March 2010 with some restrictions.

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No new free public pads may be created. Your pads will no longer be accessible after March 31, 2010, at which time your pads and any associated personally identifiable information will be deleted.

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Etherpad was a nice little tool, very effective at what it offered, I’m sure the guys who developed it will bring a lot to the Wave party. But seriously, Wave is nowhere near functional yet, it’s confusing, and glacially slow. So is there a decent alternative to Etherpad that is usable – right now?

Posted via web from Simon’s posterous

I got the 1000HE because of the promised battery life (9.5hrs). No other comparable netbook comes close, and it is every bit as good as promised. 6hrs real world use is no problem at all. 8hrs should be realistically achievable on a daily basis. Overall the machine is surprisingly nippy too. Its not going to break any speed records, but the Intel Atom N280 drives it along at a reasonable clip under normal usage. The extra 1GB of RAM I installed helps out with the speed some too, this is an ‘underpowered’ laptop that can fire up OpenOffice.org in around 10 seconds.

Since I just can’t get along with Windows as a primary OS these days, I installed Eeebunutu. This is a version of Ubuntu customised for use on Asus netbooks, and as such all the hardware works out of the box. Power management is very impressive, with the battery life holding up about as well as it does under Windows. The only glitch I’ve noticed so far is that the Wifi card doesn’t turn on and off properly with the keyboard shortcut, but its a low power device, I think I can live with that (and the Eeebuntu forums seem very good if I really want to find a fix).

Eeebuntu is certainly a pretty distro

The standard desktop is very good looking, better than the default Ubuntu look and feel, and the configuration makes the most of the (admittedly limited) screen real-estate. A few tweaks to Firefox to make the most of the available room, and I’m very happy to work on it all day. Considering I’m used to 20-24″ widescreens, that’s pretty impressive.

I’d recommend the Eee PC to anyone looking to make a netbook purchase, and since Asus are phasing the 1000HE out pretty soon, if you are looking, I’d grab one while you still can.

I’ve noticed a few people that I work with using Mind Mapping software recently, and the approach intrigued me. So I looked through FriendFeed for what different people were using (useful as a fast, informal survey of the tools available), and downloaded a couple to tinker with (FreeMind and XMind, for the record). Of these, XMind is the more polished tool, and allows you to share your maps once you have created them. I decided to give it a thorough test run at a workshop meeting in Manchester today. I’ve embedded the results below.

I’m quite impressed. It allows me to record detailed notes, and also to have a visual representation of how the various topics fit together. The XMind upload & share facility allows me to embed the final map in my wiki (or blog, obv.), and the outline view of the map is actually a good textual representation of the meeting too.

XMind is not going to be a solution for every situation. Often, pen & paper will still be used in meetings, and transcription to some electronic format will still be required. The fastest way of recording these types of notes will probably still be directly on the wiki. In the end, a mixture of tools seems the best approach overall, but for all day meetings/conference sitations, XMind seems like a very good tool.

You know an article is going to be good when it starts with a sentence like ‘Due to the overwhelming increase in [sequence data/transcriptomics data/etc]…’. So an opening gambit of ‘With the overwhelming amount and exponential increase of biomedical literature[...]‘ filled me with the promise of things to come.

The aim of this PLoS One paper is to provide an online tool for mining human protein-protein interaction data from the literature, based on the co-occurance of protein names in PubMed abstracts, together with ‘interaction keywords’. This data, combined with PPI databases and shared GO terms, aims to provide a greatly expanded set of human protein-protein interactions.

The basic suggestion is that if a pair of protein names appear frequently in the same sentence, or paragraph, or even whole article then there may exist a biologically meaningful relationship between them. This may certainly be the case in some circumstances, but it certainly does not imply a physical interaction. However, this study also employs natural language processing (NLP) techniques to examine the semantic relationships between co-occuring entities. This NLP approach uses the sentence as the unit of analysis, so may potentially miss the relevant associative language. The authors suggest that the hybrid approach using both the statistical co-occurance of terms, and the semantic analysis, means they can recover more biologically meaningful relationships than other test mining methods.

By further combining this data with PPI data from established databases (to filter the ‘known’ PPIs) and information about shared GO terms (to provide some kind of qualitative backup for some predicted interactions), the authors provide a tool of real promise for identifying ‘new’ protein-protein interactions (or at least those not present in the existing databases).

29 genes were used in PPI Finder, finding a total of 944 interactions. Of these, only 28% are already found in trusted protein-protein interaction databases. Is PPI finder really capable of enriching our knowledge of protein interactions to this degree, or is it merely finding genes which are biologically related, maybe by a transcription factor-target type relationship or similar, but don’t physically interact?

Of 100 trusted interactions studied, 69 were recovered by PPI finder. Is this demonstrating that the databases can be over zealous in certain circumstances, or that the reporting of PPIs in the literature, especially in the abstracts of high-throughput studies, is woefully inadequate?

Interaction network of DTNBP1. Red relations exist in established PPI databases, cyan relations inferred from the literature by PPI Finder.

We’re in danger of reaching saturation point here. Many more methods of predicting or defining PPIs and we’ll have a complete set, everything will be able to be shown by some method to interact with everything else. So it’s a question of confidence, and I really don’t have a great deal of confidence in these results. A quick scan of the PPI finder database online (no mean feat, believe me, the interface is not great) reveals that a large number of the interactions are defined by the protein names co-occuring in just one publication. However much NLP you do, this is no great measure of whether 2 proteins interact or not.

I think my figure shows how much ‘noise’ there is in this dataset. Whether this is productive noise or not is irrelevant without some kind of validation procedure external to the PPI Finder scoring mechanism.

My final crisicism I have already alluded to… it is woefully difficult to retrieve data from PPI Finder. In constructing the above figure I had to do some 35 or so searches of the database, and page through the results of each 10 at a time. Even an option to display all results on a single page would have made this procedure considerably less painful.

Right, enough complaining, because this was actually quite an enjoyable read. I don’t know a huge amount about text mining, and it does seem obvious to use it for this sort of purpose. All of my reservations shouldn’t distract from the fact that the dataset produced for this paper will have its uses, and the authors are correct in the opening hyperbole I opened this post with: we do need good predictive tools to keep up with data deposition in the protein domain, because no experimental methodology is going to do it yet.

He, M., Wang, Y., & Li, W. (2009). PPI Finder: A Mining Tool for Human Protein-Protein Interactions PLoS ONE, 4 (2) DOI: 10.1371/journal.pone.0004554

I have been a Papers user since v1.0. I bought my license in April ’07 and have not looked back, it makes it so much easier to find that particular paper you are after, to organise papers by subject or project, and it even converted me to reading articles on the screen, rather than printing everything out.

I haven’t had an iPhone for very long, and it is safe to say it is by far the most impressive gadget I have ever owned. I am aware of its imperfections, but I don’t care, they are either in areas I don’t use anyway (I’ve never sent an MMS in my life), or they haven’t affected me yet (though I probably will curse the lack of Copy-Paste at some point). I have been thinking about the impending release of Papers for the iPhone ever since I got it, and wondering if it would (a) be any good and (b) add anything to the already high value of Papers for Mac.

The answer to both questions is a resounding yes so far. The app is lovely, very intuitive and smooth. Syncing my Papers library to my phone was a doddle, and my (not inconsiderable) library was copied across in less than 10 minutes. Now my article collection is quite literally at my fingertips. Searching PubMed on the phone and retrieving papers also works really well. Find the paper you want, click import, and you’re done. The viewer is good too, pdfs render quickly and clearly. My only criticism is that zooming in can be a little cumbersome, but I suspect this is a hardware, rather than software, limitation.

Although I have yet to spend a lot of time with it, I don’t regret my purchase of Papers for iPhone for a second (and £5.99 is a very reasonable price btw), I think that I will get so many more articles read now I can summon them at the press of a button on my bus ride home every evening. All I need now is the full range of my institution’s journal subscriptions available to me over my 3G connection.