Let’s start with a definition: what is content marketing?

Content marketing is the creation and publication of original content to generate leads, enhance a brand’s visibility, and put the company’s subject matter expertise on display.

1. Begin with Branding

What’s your brand? How do you want to be known? Determine your niche and focus on it as your primary brand message. In today’s information-rich media environment, niche brands win. Your audience needs to develop expectations for your brand to develop confidence in and loyalty to it. It’s critical to meet your audience’s expectations in every interaction. Brand consistency is key.

2. Create your Branded Online Destinations

Where are you directing readers? Choose one branded online destination to be your core destination. Fragmenting content and conversation across multiple destinations is risky and can confuse readers. Your branded online destination will be the central hub of your online presence and will become the go-to place for people to learn everything about your brand. It’s important that you keep your branded online destination fresh with new content that effectively represents your brand promise and invites interaction and sharing.

3. Find Your Best Audience

Who is your target market and what are your users’ goals? Who do you want to connect with online? Use Google to search keywords your audience is likely to use to find brands, businesses, content and conversations like yours. Once you find your audience, observe and listen. What topics are important to them? What gets them excited? As you develop your branded online destination, pay attention to the content that resonates with your readers. Understanding what your audience wants and needs is a necessary step in developing a content strategy.

4. Join the Conversation

Interact with your audience. Avoid self-promotion. Apply the 80-20 rule, and make sure 80% of the time you are creating value to your audience. Provide a resource for readers first, then worry about enticing potential clients.

5. Publish Shareworthy Content

By creating content that is valuable to your audience, you create something that readers want to share with their friends, colleagues, and connections. Additionally, the Google search algorithm ranks websites with many incoming links (particularly from authoritative sites and especially from social media) higher than results with few incoming links.

With all of these steps, remember that content quality trumps quantity. Organic growth of readers truly interested in your brand will deliver the best long-term growth results.

How does your content marketing strategy measure up?

Life after Elsevier: making open access to scientific knowledge a reality http://t.co/N7qDpkAL #OpenAcess

Covance Among Fortune Magazine’s “World’s Most Admired Companies” http://t.co/UknHJIsj

Discovery park marks milestone http://bit.ly/JgbHcI

Content Curators Are The New Superheros Of The Web http://t.co/x2euhqtR

Purdue set to start building life sciences park http://bit.ly/JVZ6ZF

Rebooting the medical meeting – 9 lessons from TEDMED http://t.co/Mnvzt4Op

All links shared via Twitter – follow along!

BYOD is unstoppable. Smart companies must build apps. http://t.co/8HnJj6NQ

Half a million Mac computers ‘infected with malware’ http://t.co/xcuv7hwE

Apple and Facebook Should Be Terrified Of Google-tinted Glasses http://t.co/7hgoIfad

For decades, Target has collected vast amounts of data on shoppers. How Companies Learn Your Secrets http://ow.ly/a6wzz

Kaggle hosts contests for data scientists: http://t.co/A01UAb5I

TopHat 2.0.0 released from the Center for Bioinformatics and Computational Biology at University of MD http://t.co/frIKa4J9

Special issue of Science focusing on Computational Biology http://t.co/X1n8XfYS

All links shared via Twitter – follow along!

For the biopharmaceutical industry, big data is the abundant and ever-growing compendium of disease associations and genomic data that is (in many cases) publicly available. Instead of following the traditional pharma paradigm of systematically testing the effects of known compounds, computational biologists can now mine petabytes of data to identify linkages between genotypes and phenotypes, and genes and diseases to identify potential therapeutic candidates that corroborate drug screening data.

Indeed, I do this all the time! I’m frequently mining  and integrating data, building molecular interaction networks, and evaluating targets or indications before I ever need to “dig in” to specific drug data. From this process, I often know which types of drug data I need to focus on.

The Science Careers article highlights the growing demand for computational biologists:

As the pharmaceutical industry’s blockbuster drugs fall off the patent cliff, with precious few drugs in the pipeline to replace them, there are signs that big pharma could turn more of its attention to biologically derived medicines. If that happens, computational biologists will likely play a leading role in their discovery, [says Russ Altman, a professor of bioengineering, genetics, and medicine and director of the biomedical informatics training program at Stanford University in Palo Alto, California].

Altman says that according to his own observations, demand for computational biologists far outstrips supply. “I was just talking to a colleague the other day from a major drug company who came in with a piece of paper with 15 bioinformatics jobs that they’re ready to hire tomorrow,” he says.

If there truly is a shortage of computational biologists in pharma and biotech, Altman says, the federal government should be investing more heavily in training. Three institutes at the National Institutes of Health (NIH) — the National Institute of General Medical Sciences, the National Cancer Institute, and the National Library of Medicine — fund bioinformatics training programs, he says, but that’s not enough. Altman’s training grant from NIH was renewed recently, but its funding was reduced, so he’ll have to trim three or four training slots. “I do think it’s worrisome that a field that’s exploding is seeing a reduced amount of support for training.”

A recently announced Obama Administration project could reverse or offset [the] trend [of reduced funding for computational biologists]. The Big Data Research and Development Initiative pledges $200 million for NIH, the National Science Foundation, and other federal agencies to support data collection, analysis, and dispersion. Some of that money will go toward training computational biologists. When the program comes online, it could result in some direct hiring within the agencies it supports.

NIH’s director, Francis Collins, seems to believe in the field’s importance. At a 29 March briefing announcing the initiative in Washington, D.C., Collins told Science Careers, “If I were a senior or first-year graduate student interested in biology, I would migrate as fast as I could into the field of computational biology. … There are vast quantities of high-quality data accessible to anybody who has the skills to find the nuggets of truth that are hiding in that information.”

Via Computational Biologists: The Next Pharma Scientists?

Staci Kramer at paidContent wrote a story this morning covering the Pew Research Center’s Project for Excellence in Journalism (PEJ) 2012 State of the News Media report. The annual report is a comprehensive analysis on the health of journalism in America. It includes a detailed examination of eight different media sectors as well as an overview that identifies key trends and key findings of the essential statistics about news in the last year.

Staci’s story highlighted the PEJ finding that although social media is a great source of traffic for news outlets, it’s not as important (yet) as previously thought:

I find it surprising that digital news consumers more often follow news recommendations from Facebook than from Twitter, especially given that Twitter news is viewed as more unique (only 43% of Twitter users report that the news they get on the platform could have been gotten elsewhere, compared to 56% for Facebook). This result likely comes from the fact that people on Facebook spend a staggering 17-fold more time on the platform than on Twitter!

Nevertheless, the report does find that Twitter is growing rapidly. The most recent estimates of U.S. Twitter users reflect a 32% increase over 2010. Given the growing role of social media, the report asks some difficult questions for news publishers:

Though far smaller than Facebook, the predominance of journalists and news organizations among its adherents has given Twitter outsized influence in the media world. It has also taken on a critical role in disseminating breaking news — as witnessed in everything from the Arab Spring to the death of Whitney Houston (announced via a Tweet sent out 55 minutes before AP posted confirmation of the news).

“Twitter is the new newswire,” said Chloe Sladden, the director of content and programming for the site, at a recent conference on the future of media at Stanford University.

One early adopter of social media strategies to considerable success is The Huffington Post. It was among the first to aggressively adopt Facebook Connect, which it credited with big traffic gains starting in 2009. The following year, HuffPo added Foursquare-style “badges” to its site, rewarding readers when they shared or commented on stories.

By making it something of a game — and a competition — HuffPo created one of the most active and engaged communities online. And in early 2011, it added even more tools to simplify sharing: with just one click, readers can receive updates within Facebook and Twitter on a specific story, a broader topic or a particular reporter.

By the fall, HuffPo was getting as many traffic referrals from Facebook as from Google, according to Hitwise. Moreover, 7 of the top 20 political stories shared on Facebook in 2011 came from the HuffPo.

The growing role of social media raises difficult questions for news sites. First, does the traffic from social media lead to any broader traffic growth? In other words, do readers coming in from Facebook or Twitter develop loyalty to the news brand? Or is the increased dependence on social media another way that ties are loosening between the organizations who produce the news (and shoulder the costs of doing so) and the readers who ultimately consume it, thus making it difficult for news producers to monetize their content and develop loyalty to their brands?

There are few clear answers yet. But many media companies have become cognizant over the last year of the need to have a comprehensive, newsroom-wide social media strategy. And with Google’s January decision to integrate information from Google+, its own recently launched social media site, into search results, the significance of developing one will probably only grow.

Via Digital: News Gains Audience but Loses Ground in Chase for Revenue, The State of the News Media 2012

The Truly Staggering Cost Of Inventing New Drugs ($4-11 billion per drug) http://t.co/0sTxP1rT

How cool! Ferris Bueller’s latest day off (in a CR-V), Super Bowl commercial: http://t.co/gQscJoJN

Covance and BioPontis Alliance Collaborate to Bridge Drug Development Gap http://t.co/AZWsgmXD

Selventa Receives Patent for Method to Identify Biomarker Profiles http://t.co/oFvXrHek

Top Cardiologist Argues We Should Dial Back On Statins Because Of Diabetes Risk http://ow.ly/9rP5e

Scientists release project aimed at creating the cellular equivalent of Google Earth http://bit.ly/xrRj4l

All links shared via Twitter – follow along!

It was my first year attending the MMTC and it was really a lot of fun. I caught up with Sean Ekins, whom I met last month at ScienceOnline2012 and whose work on GeneGo MetaCore and MetaDrug, surprisingly enough, I had unknowingly referenced on my poster Mining PubMed for Biomarker-Disease Associations to Guide Discovery. We brainstormed an exciting path forward for the work.

Live Tweeting a Science Conference

It was especially exciting for me to find several people live tweeting the conference. Besides, Sean (@collabchem) and myself (@wjjessen), Ingrid Akerblom (@ingridbio), Executive Director, Merck Research Laboratories (MRL) Clinical IT, was very active on Twitter. Additional contributors included Martin Leach (@mdleach), Chief Information Officer at the Broad Institute; João André Carriço (@jacarrico), a researcher in the Knowledge Discovery and Bioinformatics group of INESC-ID (Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa); and Kate Skaare (@kateskaare), Conference Producer for Cambridge Healthtech Institute (CHI).

Last month, I created a tag cloud of tweets from the ScienceOnline2012 conference. With an established workflow, it didn’t take much time over the weekend to create a 2012 Molecular Med Tri-Con tag cloud. The cloud covers tweets from Wednesday, February 15th to Saturday, February 25th. In total, there were 491 tweets comprised of 8,762 words.

From 8,762 words, 2,275 were unique. I calculated the frequency of all 2,275 words and then curated the data set, removing all words less than 4 characters, numbers, and words that were either common words (such as “with”, “this” or “your”) or gibberish (consisting principally of url strings from shared links). I also reduced the frequency of the top term — tricon — from 429 to 125, so that it was easier to see the other terms. The top 250 terms were used to generate a weighted tag cloud. Feel free to download any of the files below and reshare.

Here’s the top 10 terms from the cloud above:

All data and images are available for download: low-resolution image, high-resolution image or raw data set of 250 words with frequencies.

Light on Data

Unfortunately, most of the informatics sessions were light on data; a few were simply marketing. With twelve parallel sessions, I should have left the track more often. I heard there were several good talks in the Bioinformatics and Cancerinformatics track, which unfortunately I never made it to. A good point to remember for next year’s meeting: track hop.

Notes from the Conference

Below are my notes from the conference. Most of them were taken in the Integrated R&D Informatics & Knowledge Management track.

The project began with two simple questions: (1) Which therapeutic areas are seeing the most research for the discovery, refinement or application of biomarkers? (2) Which diseases are most frequently associated with the term “biomarker”? I hope to expand upon this work in a collaboration later this year.

Introduction

Biomedical knowledge is growing exponentially; however, meta-knowledge around the data is often lacking. PubMed is a database comprising more than 21 million citations for biomedical literature from MEDLINE and additional life science journals dating back to the 1950s. To explore the use and frequency of biomarkers across human disease, we mined PubMed for biomarker-disease associations. We then ranked the top 100 linked diseases by relevance and mapped them to medical subject headings (MeSH) and, subsequently, to the Disease Ontology. To identify biomarkers for each disease, we queried Covance BioPathways, an online data resource that maps commercial biomarker assays to biological and disease pathways. We then integrated pathways-based information to describe both known and potential biomarkers as well as disease-associated genes/proteins for select diseases. This approach identifies therapeutic areas with candidate or validated biomarkers, and highlights those areas where a paucity of biomarkers exists.

Materials and Methods

Text mining was performed using PolySearch, a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites [1]. The MeSH Browser (2012 MeSH) was used to map disease associations to MeSH IDs. Once MeSH IDs were assigned, the Disease Ontology was used to map DOIDs [2]. Interaction networks were constructed in GeneGo MetaCore [3] using the Auto expand algorithm, which gradually expands sub-networks around every object from the seed object list based on interactions identified in the literature. At every step, preference is given to objects with more connectivity to the initial object, and expansion halts when the sub-networks intersect, or when the overall network size reaches a predefined limit. Genes/proteins for which validated commercial assays exist were identified using Covance BioPathways at http://www.Covance.com/BioPathways and are indicated with a red dot. These genes/proteins can be considered potential biomarkers.

Results

Data Extraction and Curation

In June 2011, we mined PubMed for term(‘biomarker’)-disease associations and identified a total of 1,181 disease associations (Table 1). We then curated the top 100 disease associations from the list, mapping each result to both medical subject (MeSH) ID and Disease Ontology ID (DOID), and then subsequently queried the GeneGo diseases ontology for associated biomarkers (Table 2). Of 100 results, 62 map to both MeSH ID and DOID and are shown below.

Table 1. A representative list of term(‘biomarker’)-disease associations mined from PubMed in June 2011. The top 100 disease associations were ranked by Z Score. The Z-score indicates the number of standard deviations that the relevancy score is above the mean; larger Z-scores denote stronger associations. The top 100 data set is available under the Open Data Commons Attribution License at http://BiomarkerCommons.org

Table 2. The curated list of disease associations minded from PubMed and organized by high-level Disease Ontology. Each specific disease association has a unique MeSH ID, DOID and number of associated genes as defined in the GeneGo MetaCore knowledgebase.

Disease Interaction Network and Biomarker Assay Identification

For illustrative purposes, we constructed an interaction network around disease-associated genes for two diseases – one with few associated genes (atherosclerosis) and one with many associated genes (asthma) – using a network building algorithm in GeneGo MetaCore. For each interaction network gene set, we then queried Covance BioPathways, a publicly accessible, web-based data source that integrates biological and disease pathway maps with validated Covance assays and antibody products, to identify commercially available biomarker assays.

Figure 1. Atherosclerosis interaction network. Disease-associated genes are indicated with blue halos; genes without a halo were included by the network building algorithm.  Biomarkers that have commercially validated assays are indicated with a red dot; they either are known or can be considered potential atherosclerosis biomarkers.

Figure 2. Asthma interaction network. All nodes shown are disease-associated genes. Biomarkers that have commercially validated assays are indicated with a red dot; they are either known or can be considered potential asthma biomarkers.

Discussion

Given the molecular interdependencies within a cell, a disease is rarely a consequence of a single gene abnormality but instead reflects the perturbation of a complex network of biological and signaling pathways. The approach described here describes the detection and ranking of human disease based on research/clinical activity surrounding biomarkers. It also enables the identification of therapeutic areas with candidate or validated biomarkers. The strategy takes an integrative approach to identify candidate disease biomarkers by combining disease-associated genes/proteins with commercially validated assays for known biomarkers. We first constructed a system-level model of disease that incorporates molecular interactions across biological and signaling pathways. We then identified each gene/protein in the model that has an existing commercially validated assay. This research offers an alternative, comprehensive view of key relationships and pathway perturbations that may identify biomarkers of disease emergence or progression.

References

1. Cheng et al. PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W399-405. Epub 2008 May 16.
   View abstract
2. Schriml et al. Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res. 2012 Jan;40(Database issue):D940-6. Epub 2011 Nov 12.
   View abstract
3. Ekins et al. Pathway mapping tools for analysis of high content data. Methods Mol Biol. 2007;356:319-50.
   View abstract

If you’re also attending the MMTC, you can catch me at most of the sessions in the Integrated R&D Informatics & Knowledge Management track on Tuesday and Wednesday. I’ll also be at the Covance booth during the day both days demoing BioPathways.

Most hotels provide irons and ironing boards, but who wants to spend time ironing when you could be enjoying a new place instead? As the dry cleaner in the video below demonstrates, with proper folding techniques you can ensure that your dress clothes arrive at your destination relatively wrinkle-free.

HT: Lifehacker

An Economix article last week put the company’s value in perspective:

Here are a few other comparisons:

• With a market value of about $460 billion, Apple is worth more than Google, Goldman Sachs, General Motors, Ford, Starbucks and Boeing combined.
• Apple is now worth almost twice as much as Microsoft (about $258 billion) and more than twice as much as Google ($198 billion).
• It is also worth more than twice as much as General Electric (about $202 billion), I.B.M. (about $224 billion) or Wal-Mart ($212 billion).
• Apple — ranked 35th in the Fortune 500, which is based on annual sales — is worth eight times as much as the company just below it on the Fortune list (Boeing, at about $56.5 billion). Its value is 20 times as much as the company just above it (Medco Health Solutions, about $23.4 billion).

via Apple Stock’s Lofty Heights, in Context: Economix [2012-02-09].

That first bullet point bears repeating: Apple is worth more than Google, Goldman Sachs, General Motors, Ford, Starbucks and Boeing combined.

And the company is still growing.

Given the trajectory of Apple stock, there’s a chance the company could hit the $1 trillion mark this decade, making it the most valued company in history.

How likely is the possibility? Consider this:

In 2006, Apple stock rose 18%. In 2007, it jumped 133%. The stock price fell 56% in 2008, but bounced back 147% in 2009. In 2010, it rose 53%. If the company’s five year average 59% annual growth rate continues, Apple could be worth $1 trillion in as little as three years [1]. Using a more conservative annual growth rate of 23%, Apple will hit $1 trillion by 2017.

In a Marketplace report today, Panos Mourdoukoutas, professor of Economics at Long Island University, contended that only market rookies look just at a stock’s price.

This idea that the stock is cheap or expensive, it’s actually useless. [Even at $502], Apple is a very inexpensive stock.

Besides share value, you also have to look at a company’s earnings.

And Apple’s earnings are impressive. Last month, the company announced financial results for its fiscal 2012 first quarter, posting a record quarterly revenue of $46.33 billion and record quarterly net profit of $13.06 billion [2]. Compared to 2011, Apple revenues increased by 73% and profits by 117%.

And why stop at $1 trillion? Last year, James Altucher at MarketWatch maintained that Apple is going to be the world’s first $2 trillion company. Or is it $3 trillion?

All these financial numbers aside, I think there are several really good (and as-of-yet unreleased) reasons to own Apple stock: iPad3, iPhone5 and Apple TV. Nevertheless (and yes, you can call me a market rookie), $500 bucks is still a lot to spend for a single share of stock, even if it is for stock in a $1 trillion company.

References

1. Can Apple reach $1 trillion in market value? It’s possible. USA Today. 2011 Apr 18.
2. Apple Reports First Quarter Results. Apple. 2012 Jan 24.