Highlight HEALTH 2.0

This article was written by Hope Leman.

This is the kind of story that gets me out of bed before I go to work in a few hours at our small medical library and to try to get the news out of what should be a fascinating development in search, Health 2.0, Science 2.0 and public discussion of patient empowerment and in neurologic science generally. I have been sent through my connections at AltSearchEngines and Next Generation Science a link to the Marketwire press release “PatientsLikeMe Launches Genetics Search Engine for ALS Patients“.

I spent many hours in the amyotrophic lateral sclerosis community of Patients Like Me in 2007 — 2008 and still visit it occasionally. It is a remarkable instance of a close-knit group of people united by the personal tragedy of either having Lou Gehrig’s Disease (ALS) or caring for and/or about someone with it. The founders of Patients Like Me (PLM), Jamie and Ben Heywood, have since created similar online patient communities for Parkinson’s Disease, Multiple Sclerosis and so on.

I briefly met Ben Heywood at a recent Health 2.0 conference. Both he and Jamie are frequent speakers at Health 2.0 and Web 2.0-related gatherings and have been the subject of books and documentaries. The documentary So Much So Fast shows the courageous struggle their brother Stephen made against the ravages of ALS. The solidarity and love his family showed in that struggle is one of the most moving films ever made and should be viewed by literally anyone in the health sciences, encompassing as it does the minutiae of confronting ever growing disability and the quest to obtain accurate medical information in the Internet Age. The Heywood brothers have accomplished remarkable things across a variety of fields, notably in prodding the research establish to fast track research in ALS and to engage in far more transparent practices. They and men like Augie Nieto are the kind of people that leave one feeling that individuals can make a difference even in the face of such frightening specters as a diagnosis of ALS.

But I do have my qualms and concerns about some of the PLM initiatives. Even when papers about it appear in the standard medical literature in PubMed, the authors of the papers are usually affiliated in some way with PLM. It would be nice if there were at least a few by neurologists who have no relationship at all to PLM that reported on advances made as a result of the data acquired from the self-reports that PLM members upload to PLM databases.

The search engine is simply called the Genetics Search Engine, although it covers only ALS and thus is likely to create some branding confusion.

The PLM blog post announcing the launch of the new search engine states: “Today, we’re announcing the launch of our Genetics Search Engine for people with ALS. Imagine finding other patients just like you, down to the genetic level. Patients in our ALS community can now do that. (For patients who don’t see their genetic mutation right now, that’s alright. They can be the first with that genetic mutation to join our community and share information about the disease.) What does sharing genetics mean for research? By capturing data on familial ALS patients’ known genetics, such as the A4V or D90A mutations in superoxide dismutase 1 (SOD1) and the P56S mutation in vesicle-associated membrane protein-associated protein B (VAPB), we can learn more about the cause and effects of every kind of ALS and better our chances of advancing research and finding new treatments. Our goal in launching the Genetics Search Engine (and other upgrades like it) is to help patients find others just like them and enhance our understanding of the phenotype of each genetic mutation (i.e., different causes of ALS have faster or slower disease progression).”

This brings up some interesting issues:

• As noted, there is the matter of branding. The wording “… other upgrades like it” suggests that this is the first of what may become a series of search engines for genetic mutations. That is an intriguing development and illustrates the genuine knack for marketing and technological innovation that has always made PLM a leader in the patient social networking space even as other such communities (such as Trusera) with much less drive to actually contribute to the medical science related to the travails of members have struggled to attract and retain users.

• The Genetics Search Engine does not appear particularly innovative or interesting vis-a-vis search technology. It seems, indeed, to be fairly unspectacular and rudimentary in that respect. But it is certainly more handsome and engaging than ALSOD: the Amyotrophic Lateral Sclerosis Online Database, which covers only SOD1 mutations and is designed for medical professionals, not patients and the general public.

• The wording “Imagine finding other patients just like you, down to the genetic level” is quite fascinating. ALS is a rare disorder and now patients can find in that small group an even smaller group and, now, so can researchers. This has all kinds of implications for organizing patients into lobbying groups for research into that particular mutation. That might actually advance research, given that the intense study of one gene or one mutation could benefit the entire ALS population. Alternatively, it could hinder advancement if a vocal group successfully wins earmarks for what turn out to be blind alleys. In any case, it is a brave new world in which people can form political, emotional and social bonds on the basis of genetic mutations. This is a good thing for public education in medical matters and public discussion of important scientific and bioethical matters.

• The development of the search engine and what becomes of the data is something that all those intrigued by the era of personalized medicine will watch with intense interest. Some argue that such data will prove of limited utility for decades and that it is cruel and deceptive to suggest to patients that hours spent mastering the arcana of genetics will improve their short-term prospects and day-to-day quality of life. Indeed, part of the marketing muddle is that the press release does not make the distinction clear between familial ALS and the sporadic type, the former being much rarer. Jamie Heywood is quoted as saying:

  Beyond the research implications of what this means today, if you’re a patient with familial ALS, you can now use this information to make better decisions about your healthcare and learn from the experiences of others like you.

  But that group is a small subset of the greater ALS patient population as a whole and the press release makes it sound like the Genetics Search Engine is a potential boon for all ALS patients. It could be, but that isn’t made clear.

But one should not complain too much given the obvious solace and genuine enjoyment members find in PLM and the impressive amount of useful information exchanged there on matters of nutrition, respiratory care, assistive technology and such, necessary in the care of patients with this illness. The fact that PLM has 3,400 members worldwide alone is impressive given that there only an estimated 30,000 people with the disease in the U.S. in any given year.

It will be interesting to see what public comment discussion is engendered by this development vis-a-vis patient education and genomics, and the ethical and health information management issues raised by patients taking an ever more intense interest in the building blocks of their very beings and expecting frontline clinicians to treat them by utilizing such data.

This not just an ALS story. This is a new era in medicine.

About the author: Hope Leman writes about Health 2.0 and the e-patient movement at Significant Science. She is also a writer for AltSearchEngines, which covers hundreds of alternative / niche search engines. Hope is a research information technologist for a health network in Oregon and is also Web administrator of the grants and scholarship listing service ScanGrants.

As Highlight HEALTH 2.0 celebrates its’ first year following the use of Web 2.0 in health and medicine, I would like to take this opportunity to thank you for your readership.

Three websites make up the Highlight HEALTH Network:

• Highlight HEALTH 2.0
• Highlight HEALTH
• The Highlight HEALTH Web Directory

Each of these sites has a different purpose. Highlight HEALTH 2.0 is a group effort to follow the use of Web 2.0 in health and medicine (if you’re interested in writing a review about a social health network or co-blogging about the integration of Web 2.0 technologies into health and medicine, please let us know). Highlight HEALTH promotes advances in biomedical research to encourage health literacy. Lastly, the Highlight HEALTH Web Directory is an online reference guide for reliable health and medical information.

There are more ways than ever to connect with the Highlight HEALTH Network, including email/RSS, Twitter and Facebook. If you have an internet-enabled cell phone, Highlight HEALTH 2.0 can be accessed via the mobile web.

Top 5 most popular articles

Here are the most popular articles for 2008 (top 5 based on the number of page views/number of days posted):

1. Online Patient Community Battle for Survival: MDJunction
2. iMedix: Reliable Health Search and Patient-to-patient Social Network
3. Mednar Search … and Hope said, “It is good.”
4. Following the Tweets of Health
5. Core Biomedical Research Software and Web 2.0 Tools

Thank you and Best of Health in the coming year!

Following a discussion on Twitter, Dr. Chris blogged about essential medical software and invited others to share their “core apps”. I thought I would add to the discussion with my own essential programs, albeit with a genomics/pre-clinical research bias. Since a number are Web 2.0 apps, I’m posting this on Highlight HEALTH 2.0.

First: a little about me. I’m a bioinformatician working in neuro-oncology.

Last month, an article debating Scientist vs Developer in Bioinformatics was posted on Web 2.0 and Semantic Web for Bioinformaticians. I would fall into the first category (Scientist Bioinformatician), as I’m interested in testing hypotheses and discovering new knowledge.

Michael Barton wrote an entertaining article on career paths for Bioinformaticians earlier this year. In the World of Bioinformatics Quest, my character class is a Wet lab Bioinformatician (WB):

A dry lab scientist, working in wet lab, the WB is the support for all things computational biology in the laboratory. The WB is familiar enough with Perl to create simple scripts, but the real strength of the WB doesn’t lay in writing code, but rather in using tools. While most bioinformaticians are familiar, in theory, with how to find a gene of interest, the WB uses these tools day in and day out — the WB puts many to shame on basic bioinformatics. While others have their head in the clouds thinking about theories and algorithms, the WB is getting his hands dirty with real data as it is being produced.

The majority of my time is spent performing Functional and Comparative Genomics on DNA microarray data. The information I analyze is used to further our understanding of the molecular pathways altered in neurofibromatosis and to identify potential therapeutic targets.

Note that I’m an Apple user (MacBook 2.16 GHz Intel Core 2 Duo, 3 GB 667 Mhz DDR2 SDRAM), so all my core apps are for a Mac. With all of the visual data I manipulate, a good monitor is essential; I use a Samsung Syncmaster 245BW 24″ LCD monitor. Finishing out the hardware list is an Apple wireless keyboard and mouse.

Local Mac applications

GeneSpring GX

GeneSpring GX provides powerful, accessible statistical tools for fast visualization and analysis of gene expression data. Sure, there are more powerful alternatives (I hear all you R users grumbling …). Nevertheless, the ability to quickly manipulate gene lists and display gene expression patterns makes GeneSpring GX an essential core app for expression analysis.

R/Bioconductor

I use R in conjunction with Bioconductor, an open source and open development software project for the analysis and comprehension of genomic data, principally for data normalization prior to import and analysis in GeneSpring. I routinely use custom CDF ([Gene] Chip Definition File) libraries, so R/Bioconductor is absolutely essential for data preprocessing.

Microsoft Excel

Genomics analysis requires manipulation of many, many data points (100 samples x 56,000 probe sets = 5.6 million data points). Sorting, searching and organizing the data can all be accomplished with Microsoft Excel. A necessary evil.

Skim

Skim is a PDF reader and note-taker for OS X. It is designed to help you read and annotate scientific papers in PDF, but is also great for viewing any PDF file (think Preview on steroids). Unlike Adobe Acrobat, it’s FAST.

Bookends

I’ve tried a number of applications to organize and tag scientific journal articles. Keeping up is an impossible task. I’ve tried several applications (yes, I’ve even tried Papers). However, for those of us that write research articles for publication, a reference manager is an essential app. Bookends is bibliography software that can also organize PDFs. The interface isn’t flashy but it’s a powerful piece of software. You can perform PubMed searches, retrieve references and associated PFDs and create static, smart and virtual groups to organize and find information in your personal collection. And it’s FAST. Bookends uses Spotlight to search within PDFs. You can search titles, abstracts, authors, journals, dates and more. Bookends is actively supported and has a useful user forum.

Microsoft Word

Speaking of writing research articles for publication, Microsoft Word is another necessary evil. I’m writing all the time and a powerful word processor is a must.

Microsoft PowerPoint

Yes, another Microsoft app. Scientists are always talking about their research and pictures speak louder than words. Especially in my position as a Bioinformatician, I need software that allows me to display and present information; Microsoft PowerPoint is that application.

I should pause here and mention out that I’m testing Apple iWork to replace Word and PowerPoint. The entire Office suite isn’t optimized for OS X (it really runs poorly) and both Pages and Keynote — iWork apps — rival the utility of Word and PowerPoint.

Web applications/databases

I spend a great deal of time analyzing large high-throughput gene expression data sets. Each analysis involves addressing several hypotheses and establishing biological meaning from the data. I rely on a number of web applications and databases to identify genes or gene sets associated with a particular phenotype (meaning an observable characteristic) and to identify enriched biological themes, particularly gene ontology (GO) terms.

PubMed

Efficient literature search is essential to the practice of Evidence-Based Medicine and to Biomedical Research. PubMed provides free access to MEDLINE, one of the largest searchable databases of biomedical research articles. An absolute necessity.

DAVID Bioinformatics Resources 2008

The Database for Annotation, Visualization and Integrated Discovery (DAVID) 2008 provides a comprehensive set of functional annotation tools for researchers to understand biological meaning behind large list of genes. I’ve tried various other tools for functional annotation analysis, but I find DAVID the most comprehensive app on the web. DAVID is owned by the NIH and is under continuous development and improvement.

OMIM

OMIM (Online Mendelian Inheritance in Man) is a comprehensive and authoritative compendium of human genes and genetic phenotypes. The full-text, referenced overviews in OMIM contain information on all known mendelian disorders and over 12,000 genes. OMIM focuses on the relationship between phenotype and genotype. I frequently link to OMIM for genes discussed on Highlight HEALTH.

iHop

No, not the restaurant. Information Hyperlinked over Proteins (iHop) provides the network of genes and proteins as a natural way of accessing the millions of abstracts in PubMed. By employing genes and proteins as hyperlinks between sentences and abstracts, the information in PubMed becomes bound together into one navigable resource.

Web 2.0 tools

I’m using Web 2.0 tools more and more in my daily workflow. Many have become indispensable for organization and communication.

WordPress

Both Highlight HEALTH and Highlight HEALTH 2.0 run on WordPress. I’ve used WordPress for a couple of years now and (between plugins and hacks) it does everything I need it to do.

Delicious

I gave up trying to manage browser-based bookmarks some time ago. Delicious.com allows users to tag, save, manage and share web pages from a centralized source. The latest addition to my workflow is the use of the “toread” tag to find bookmarked articles of interest for later review (since emailing or saving links to the desktop is inefficient).

FreshReader

I use RSS frequently, not only for blogs of interest but also to keep up with biomedical journals (current issue table of contents RSS), PubMed searches (to keep up with the latest published research in my field of work), LinkedIn updates and news. I try not to depend on Google too much, although that can be difficult nowadays. For RSS, I use FreshReader, a web-based, server type RSS and Atom feed reader. Fed up with slow feed readers? FreshReader is FAST.

FriendFeed

FriendFeed is becoming an excellent social networking tool, since several rooms have been setup for life science researchers and programmers. In addition to being a social aggregator, FriendFeed has comment streams that allow you to contribute to the conversation. In many cases, entire discussions are held entirely on FriendFeed.

Twitter

Twitter is also becoming a useful tool to communicate with other doctors and researchers (most are also using FriendFeed as mentioned above). Twitter allows you to keep up with people that share similar interests.

If you use both both FriendFeed and Twitter, I’ve found a convenient tool. MySocialChatter.com brings FriendFeed and Twitter together in a single web page using frames and automatically refreshes every few minutes. It’s quite useful for keeping an eye on the conversation, is easy to read (compared to some desktop apps) and is non-disruptive.

… And the rest that I couldn’t live without

Other everyday applications that I couldn’t live without:

Quicksilver

Act without doing. If you use a Mac, Quicksilver is simply a must have.

Expose

Move over command-tab (that’s alt-tab for all you windows users), Expose is AWESOME.

Mail.app

Mail.app, my e-mail program of choice.

Safari

The web, Apple style. Safari with the Saft extension is my internet browser of choice. OS X optimized and FAST.

Journler

My digital notepad. Journler is a daily notebook and entry based information manager. Never again do I have to deal with miscellaneous text files scattered across my hard drive or random bits of text emailed to myself. That said however, I am looking into Evernote to supplement (and possibly replace) Journler.

iTunes

My iTunes plays mostly ambient, new age music. I frequently work with Groove Salad or Radio IO Ambient playing in the background.

Final thoughts

So, there you go. There’s many applications, but since I’m a bioinformatician that should come as no surprise. I think you’ll get a better idea of who I am and what I do from the software tools I use and rely on everyday. These are my core biomedical research apps.

Are you a physician or biomedical scientist? Is there software or Web 2.0 tools that you couldn’t live without? What are your core apps? Share in the comments below or write your own post and link back here.