We have known for some time that vitamin D insufficiency is a risk factor for developing Multiple Sclerosis and we would like to know whether vitamin D levels influence the disease course of those who already have Multiple Slcerosis and what are the scientific articles dealing with the subject. This use case nis inspired by an article seen on ScienceDaily.

1. We first search for Low Vitamin D in novoseek. There are 8,916results in Pubmed and 3,714results in Free Full Text up to date.

2. We are interested in low Vitamin D Levels so we click on “Vitamin d” from the “Pharmacologicl Substances” category in sidebar to add it to the current search and make it more specific. We now have 4,584 results in Pubmed up to date.

3. Now, we need to look up for Multiple Sclerosis disease-related articles. In order to do so, we will check if it appears in the diseases category in the left sidebar. We need to click on the “more” link to see all the diseases related to the current search. Multiple Sclerosis diseases does not show up so we have to think of another search strategy.
4. The other way of searching for “multiple sclerosis”. It consists of typing in the search box: low vitamin d and “multiple sclerosis”. Typing the term “multiple sclerosis” between brackets will tell the system to look only for these two terms when they are mentioned together. We press search and we now have 61 results in Pubmed.

Note: in order to make the search even more specific and relevant, we can click on the “multiple sclerosis” filter that now appears at the top of the related concepts list in the Diseases category.
5. Last search term to be added to the current search consists of adding “children”. This will help us consult solely articles related to humans. You can either add and children in the search box or look in the sidebar where the category Organisms appears and add Homo Sapiens (showing up in 4th position) to the search. We now have 34 results in pubmed. 
   

Those articles found in a few steps in Pubmed are satisfactory and help start working on knowing if Low Vitamin D Levels Associated With Greater Risk of Relapse in Childhood Multiple Sclerosis.

This is the 3rd and last post of a series of post about the technology behind novoseek. In the first issue we talked about the problem of synonyms, in the second we showed the challenge of dealing with homonyms, in our third issue we would like to share some thoughts behind data organization and its representation which is a common issue to any type of Web application.

I must confess, I am fond of data visualization. I love all those keynotes, or graphics that have great colors, shapes… They catch my attention despite the fact that I might not understand them or that they provide me with irrelevant information. However some of them are really amazing. When I was doing my research on bioinformatics I was desperate looking for ways to represent all the data I had on protein interactions in a way that I could get a big picture at first and then focus on the details. I found a few amazing things at Visual Complexity but not flexible enough. I must confess that I failed in my intention to apply my programming skills to this task.

When I joined Bioalma and I started promoting our first product AKS, I was really excited with one of its main features that represents the relations among concepts based on the co-occurence in the literature. Is a great piece of software that lets you see at-a-glance which concepts are more related and visualize clusters. However, the information behind it was not always understandable.

When we started the novoseek project we decide to embrace the KIS (Keep It Simple) principle. Although we  try to keep up with this philosophy, I must confess that in our meetings the development manager, marketing director and an art director, its hard to say if we are even close to this philosophy.

Regarding the novoseek interface

As you might remember from previous posts, novoseek analyzes all the literature with an algorithm that integrates database information and takes into account the context of terms to annotate them in the literature. So when we started the project and we had all the data from the analysis of all the literature, we asked ourselve “what should we do with it? How could the user take advantage of all this analysis?”. Obviously, putting it in a search engine that is simple, clear and easy to use was our best choice. We needed to start organizing the data and designing a visualization interface to interact with it.

We needed to arrange all that information in a data structure that could give a fast, efficient and scalable service. The scalability issue was a really important concern. We didn’t want to change the data model when the system needed to serve millions of simultaneous petitions.

We also needed to have a picture of what type of information we wanted to display and how the user could interact with it. Based on our experience we knew that we needed to develop something not only simple but also familiar to the end user. We knew that designing an advanced interface with lots of information would be likely to disconcert the users. Our CEO was always telling us “we need to do something that doesn’t need to be explained to use it and understand it”. And so we did.

So the indexing technology and the automatic disambiguation method enabled novoseek to search faster and more efficiently the most relevant documents. We decided to take advantage of that and build what we called Profile. This profile is the result of the analysis that novoseek does taking advantage of the results of our text-mining analysis to build a list of the most relevant concepts to the query. We thought that this list would be really helpful since it gives a quick idea of what are the main themes related to the query. As we thought this list of relevant concepts needed to be interactive, we then added some functionality to it. Whenever you click on one of the terms of the list you get all the documents that take into account the very query term and the clicked concept. You can check examples with our user cases.

After that, we added many other features, some of which are really handy! Others may be a bit more hidden for advanced users that want to make the most out of the system.

However, understanding the users, the way they interact with us, what is useful and what can be removed to keep up with the KIS philosophy is an endless and ongoing process. At Bioalma, we are always studying what would happen if we put this menu here, if we choose this color or if we set up this log-in box there. Indeed, we mix our own craziness with the user suggestions and it is clear that sometimes we come up with a different (or strange) interface. So stay tuned and find out soon the results of our conversation with users and our own schizophrenia.

Searching for relevant scientific articles in known to be a common task (more or less difficult depending on the searching knowledge and the tools used) for life sciences professionals. In fact, there are several web-based services, journals and/or paying (password-restricted) platforms to search from. It is often difficult to know the good search strategy (which platform, what keywords, what filters, etc) in the first place. You may find what you are looking for eventually, but you are desperate to fasten this process and make it easier and less resource-consuming. Even if most of us can ask a librarian (you will find valuable information and resources with these 10+1 medical librarians you should read) or have a Pubmed class, life science professionals need to improve their searches. Improving searches can be done through several ways: reading use cases, tips, using novoseek and, last but not least, knowing what kind of publication type you are looking for. Indeed, filtering a search in Pubmed database by one or several publication types will help you limit a large amount of search results and find what you need to read faster.

This list aims to help the life science professionals determine what the publication types correspond to and what they will allow them to find within the biomedical literature. We will put emphasis on the main and most common publication types you can find while searching for scientific articles in the biomedical literature. Each Publication Type is illustrated with an example on novoseek.

On top of that, we would like to present interesting facts as dealing with huge databases allow us to play around with figures and interpret them. For example, from the nearly 19 million articles available in Medline to date, nearly 97% are categorized as Journal Articles and more than 8% account for english abstracts. However, an article can be categorized in several publication types at the same time. The following image shows how the literature is categorized in publication types.

note:the publication types accounting for less of 2% are not shown in this graph and are gathered in the category “rest of publication types”.

• Clinical Trial


A Clinical Trial is a work that is the report of a pre-planned clinical study of the safety, efficacy, or optimum dosage schedule of one or more diagnostic, therapeutic, or prophylactic drugs, devices, or techniques in humans selected according to predetermined criteria of eligibility and observed for predefined evidence of favorable and unfavorable effects.
Example: BRCA1 – Clinical Trial

• Comparative Study


A comparative study is a study in which a participant is randomly assigned to one of two or more different treatment groups for purposes of comparing the effects of the treatments.
Example: BRCA1 – Comparative Study

• Comment


A Comment is a work consisting of a critical or explanatory note written to discuss, support, or dispute an article or other presentation previously published. It may take the form of an article, letter, editorial, etc. It appears in publications under a variety of names: comment, commentary, editorial comment, viewpoint, etc.
Example: BRCA1 AND BRCA2 mutation – Comment

• Controlled Clinical Trial


A Controlled Clinical Trial is a work consisting of a clinical trial involving one or more test treatments, at least one control treatment, specified outcome measures for evaluating the studied intervention, and a bias-free method for assigning patients to the test treatment. The treatment may be drugs, devices, or procedures studied for diagnostic, therapeutic, or prophylactic effectiveness.
Example: Sinusitis – Controlled Clinical Trial

• Consensus Development Conference


A Consensus Development Conference is a work that consists of summary statements representing the majority and current agreement of physicians, scientists, and other professionals meeting to reach a consensus on a selected subject.
Example: Asthma – Consensus Development Conference

• English Abstract


An English Abstract refers to publications that have been written in a language different from English and which abstracts and title have been translated to English for indexing reasons in NKM. When browsing results, you can notice the English Abstracts directly thanks to the bracket in the title.
Example: Asthma – English Abstract

• Evaluation Studies


An Evaluation Studies is a work consisting of studies determining the effectiveness or utility of processes, personnel, and equipment.
Example: Asthma – Evaluation Studies

• Guideline


A Guideline is a work consisting of a set of statements, directions, or principles presenting current or future rules or policy. Guidelines may be developed by government agencies at any level, institutions, organizations such as professional societies or governing boards, or by the convening of expert panels.
Example: Asthma – Guideline

• Journal Article


Journal Articles are the predominant publication type for articles and other items indexed for NLM (National Library of Medicine) databases.
Example: BRCA1 – Journal Articles

• Meta-Analysis


Meta-Analysis are works consisting of studies using a quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc. It is often an overview of clinical trials.
Example: Asthma – Meta Analysis

• Multicenter Study


A Multicenter Study is a work consisting of a controlled study executed by several cooperating institutions.
Example: Asthma – Multicenter Study

• Practice Guideline


A Practice Guideline is a work consisting of a set of directions or principles to assist the health care practitioner with patient care decisions about appropriate diagnostic, therapeutic, or other clinical procedures for specific clinical circumstances. Practice guidelines may be developed by government agencies at any level, institutions, organizations such as professional societies or governing boards, or by the convening of expert panels.
Example: Asthma – Practice Guideline

• Randomized Controlled Trial


A Randomized Controlled Trial (RCT) is a work consisting of a clinical trial that involves at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table.
Example: Asthma – Randomized Controlled Trial

For further information and benefits on RCT, I strongly recommend you to read the post “the best study for dummies” by Jacqueline Limpens.

• Research Support, NIH, Extramural


Research Support, NIH, Extramural, refers to publications of research resulting from extramural research funded by the National Institutes of Health.
Example: Asthma – Research Support, N.I.H., Extramural

• Review


A Review is an article or book published after examination of published material on a subject. It may be comprehensive to various degrees and the time range of material scrutinized may be broad or narrow, but the reviews most often desired are reviews of the current literature.
Example: Asthma – Review

• Validation Studies


A Validation Study is a work consisting of research using processes by which the reliability and relevance of a procedure for a specific purpose are established.
Example: Asthma – Validation Studies

Regarding novoseek functionalities, it helps you filter by Publication Type easily as we explain in this TIP blogspot and you can refer to the following images to see where to filter by Publication Type when you are on the results page and where to identify the Publication Type when you are reading a publication on novoseek.

Resources: PubTypes, research, novoseek and insights from José Cristobal Buñuel .

This user case is inspired by a search tutorial for Pubmed (see page 61) brought to our knowledge by María, Medical Librarian from Torrevieja Hospital (Spain). We found it interesting to show how novoseek provides results as satisfactory as Pubmed in a simple and quick way.

1. We first search for « tamiflu » in novoseek.There are 840 results in pubmed and 144 in free full text to date.
   
2. Looking in the search details we notice that our search has been mapped to pharmacological substance « tamiflu ».
   Clicking on the the term tamiflu pops up a window with complementary information. This is handy to know the synonyms (alternative names) to Tamiflu : Oseltamivir phosphate , GS 4104 are some of them.

3. We need to look for influenza disease to keep building our search.Obviously, it should appear in the first relevant related concepts to the previous search. Indeed, it is the first related disease to the search Tamiflu. Clicking on it will add it directly to the current search (alternatively you can click on the « i » icon and click on « add filter » as shown on image). The search is now refined and we have gone from 840 results to 639 results in Pubmed.
   
   
   In this case, we are looking for Meta-Analysis publications. In science, “meta-analysis does more than just combine the effect sizes of a set of studies. It can test if the studies’ outcomes show more variation than the variation that is expected because of sampling different research participants” as explained in the Wikipedia. This should help us find publications showing the efficiency of Tamiflu to diminish the percentage of secondary complications.
4. We now need to select the publication type (or type or article).We click on the « bibliographics tab » and where publication type is, we click on the « more » button. Where « Meta Analysis » appears, we click on that term which is directly added to the current search. We now have gone from 639 results to 9 results in Pubmed.
   
   
5. The results are very satisfactory as they contain the same publications as in Pubmed. Should we need to look specifically for publication mentioning children, look for the filter “Homo Sapiens” in the related concepts tab on the left and add it to your search. We now have 5 results in Pubmed.
   • Check more User cases.
   • Check more Tips.


We can compare now these results to Pubmed’s and we notice that we have the 3 publications found in Pubmed in novoseek results. The advantage with novoseek is that we have performed a complex search in a simple way and in no time.

Flash Forward is unsurprisingly one of the most exciting TV show of this year. As a result, I’m dying to watch a new episode every week. And when I do, I’m having a great time that leads to great discussions at the office about whether this is about future or not and how it can be modified. Nevertheless, I could notice a few weeks ago the interesting case of Edward Ned (also called Ned Ned) whose flash forward vision finds him in a club and having his skin totally black, whereas he’s white currently. Dr. Olivia Benford chooses to treat him as a regular patient no matter his flash forward but Dr. Bryce Varley -her colleague and now totally changed by his flash forward- has another opinion. Indeed, he thinks that this color change may be due to a disease; and that would explain many things regarding this patient. This is why he decides to refer to an online search engine to look for more information.

In order to know more about this Ned’s health condition, Bryce looks for “Pigment Change” in a symptoms search engine. His search returns 107 results and then helps him explain afetrwards that:

- Ned may have Addison’s disease which would explain why he’s black in the future (as he sees himself in his flashforward)
- The disease forces his body make melanine compounds instead of adrenaline
- Without Adrenaline his body is unable to build proper stress response (which explains he’s being so serene)

Obviously, novoseek has different goals (to the webpage Bryce is using) as it offers to explore the scientific literature. Nevertheless we can search for that disease -Addison’s disease- and observe what are the results like.

1. A search for Addison’s disease via the Advanced Search panel returns 2,563 results in Medline.
2. Observing the related concepts sidebar we can see that the most relevant diseases related to Addison’s disease are: Adrenal insufficiencies, primary adrenal insufficiency, autoimmune addisons disease, diabetes and Hyperpigmentation (with a relevance of 41%).

3. Also, the most relevant related Signs and Symptoms indicate: alopecia, fatigue, malaise, cryoglobulinemic purpura, scalp pruritus…
4. We click the “hyperpigmentation” disease and it is added to the current search: there are now 66 results in Medline
5. From there, we can start exploring the literature and read interesting publications such as Adrenal autoantibodies and organ-specific autoimmunity in patients with Addison’s disease, Generalized pigmentation due to Addison disease., Long-lasting subclinical Addison’s disease..
6. The reading of these is a good starting point to know more about the disease, its origins and possible treatments.

Obviously, this complementary information helps save Ned during surgery and Dr. Olivia Benford now has to admit that Ned’s Flash Forward actually helped save him. Based on that, we see the importance of research to know more about a disease, its symptoms and the existing treatments. Furthermore, a search for Addison’s Disease in US Grants could help know what are the current studies about this disease.

And now, I’m asking you:

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