The Impact of Boundary Spanning Scholarly Publications and Patents

Background

Human knowledge and innovation are recorded in two media: scholarly publication and patents. These records not only document a new scientific insight or new method developed, but they also carefully cite prior work upon which the innovation is built.

Methodology

We quantify the impact of information flow across fields using two large citation dataset: one spanning over a century of scholarly work in the natural sciences, social sciences and humanities, and second spanning a quarter century of United States patents.

Conclusions

We find that a publication''s citing across disciplines is tied to its subsequent impact. In the case of patents and natural science publications, those that are cited at least once are cited slightly more when they draw on research outside of their area. In contrast, in the social sciences, citing within one''s own field tends to be positively correlated with impact.     

Automating document classification for the Immune Epitope Database

Background  

The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose.     

Using caching and optimization techniques to improve performance of the Ensembl website

Background  

The Ensembl web site has provided access to genomic information for almost 10 years. During this time the amount of data available through Ensembl has grown dramatically. At the same time, the World Wide Web itself has become a dramatically more important component of the scientific workflow and the way that scientists share and access data and scientific information.     

Quantifying information transfer by protein domains: Analysis of the Fyn SH2 domain structure

Background  

Efficient communication between distant sites within a protein is essential for cooperative biological response. Although often associated with large allosteric movements, more subtle changes in protein dynamics can also induce long-range correlations. However, an appropriate formalism that directly relates protein structural dynamics to information exchange between functional sites is still lacking.     

A Principal Component Analysis of 39 Scientific Impact Measures

Background

The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact.

Methodology

We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data.

Conclusions

Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.     

CGMIM: Automated text-mining of Online Mendelian Inheritance in Man (OMIM) to identify genetically-associated cancers and candidate genes

Background  

Online Mendelian Inheritance in Man (OMIM) is a computerized database of information about genes and heritable traits in human populations, based on information reported in the scientific literature. Our objective was to establish an automated text-mining system for OMIM that will identify genetically-related cancers and cancer-related genes. We developed the computer program CGMIM to search for entries in OMIM that are related to one or more cancer types. We performed manual searches of OMIM to verify the program results.     

Reporting science and conflicts of interest in the lay press

Background

Forthright reporting of financial ties and conflicts of interest of researchers is associated with public trust in and esteem for the scientific enterprise.

Methods/Principal Findings

We searched Lexis/Nexis Academic News for the top news stories in science published in 2004 and 2005. We conducted a content analysis of 1152 newspaper stories. Funders of the research were identified in 38% of stories, financial ties of the researchers were reported in 11% of stories, and 5% reported financial ties of sources quoted. Of 73 stories not reporting on financial ties, 27% had financial ties publicly disclosed in scholarly journals.

Conclusions/Significance

Because science journalists often did not report conflict of interest information, adherence to gold-standard recommendations for science journalism was low. Journalists work under many different constraints, but nonetheless news reports of scientific research were incomplete, potentially eroding public trust in science.     

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data

Background  

A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification.     

New directions in biomedical text annotation: definitions, guidelines and corpus construction

Background  

While biomedical text mining is emerging as an important research area, practical results have proven difficult to achieve. We believe that an important first step towards more accurate text-mining lies in the ability to identify and characterize text that satisfies various types of information needs. We report here the results of our inquiry into properties of scientific text that have sufficient generality to transcend the confines of a narrow subject area, while supporting practical mining of text for factual information. Our ultimate goal is to annotate a significant corpus of biomedical text and train machine learning methods to automatically categorize such text along certain dimensions that we have defined.     

Exploring supervised and unsupervised methods to detect topics in biomedical text

Background  

Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.     

Thesaurus-based disambiguation of gene symbols

Background  

Massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene symbols is a major bottleneck.     

Bacterial response to siderophore and quorum-sensing chemical signals in the seawater microbial community

Background  

Oceans are iron-deficient and nutrient-poor environments. These conditions impart limitations on our understanding of and our ability to identify microorganisms from the marine environment. However, less of knowledge on the influence of siderophores and N-acyl homoserinelactone as interspecies communication signals on the bacterial diversity of seawater has been understood.     

An e-learning application on electrochemotherapy

Background  

Electrochemotherapy is an effective approach in local tumour treatment employing locally applied high-voltage electric pulses in combination with chemotherapeutic drugs. In planning and performing electrochemotherapy a multidisciplinary expertise is required and collaboration, knowledge and experience exchange among the experts from different scientific fields such as medicine, biology and biomedical engineering is needed. The objective of this study was to develop an e-learning application in order to provide the educational content on electrochemotherapy and its underlying principles and to support collaboration, knowledge and experience exchange among the experts involved in the research and clinics.     

Perception of stroke and knowledge of potential risk factors among Omani patients at increased risk for stroke

Background  

Previous studies have demonstrated poor knowledge of stroke among patients with established risk factors. This study aims to assess the baseline knowledge, among patients with increased risk for stroke in Oman, of warning symptoms of stroke, impending risk factors, treatment, and sources of information.     

Information extraction from full text scientific articles: Where are the keywords?

Background  

To date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant.     

Concept-based query expansion for retrieving gene related publications from MEDLINE

Background  

Advances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in these fields of study. While much of the data and results described in these articles are entered and annotated in the various existing biomedical databases, the scientific literature is still the major source of information. There is, therefore, a growing need for text mining and information retrieval tools to help researchers find the relevant articles for their study. To tackle this, several tools have been proposed to provide alternative solutions for specific user requests.     

BioInfer: a corpus for information extraction in the biomedical domain

Background  

Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora.     

Magnetic Resonance Imaging in Multiple Sclerosis – Patients' Experiences,Information Interests and Responses to an Education Programme

Background

Magnetic resonance imaging (MRI) is a key diagnostic and monitoring tool in multiple sclerosis (MS) management. However, many scientific uncertainties, especially concerning correlates to impairment and prognosis remain. Little is known about MS patients'' experiences, knowledge, attitudes, and unmet information needs concerning MRI.

Methods

We performed qualitative interviews (n = 5) and a survey (n = 104) with MS patients regarding MRI patient information, and basic MRI knowledge. Based on these findings an interactive training program of 2 hours was developed and piloted in n = 26 patients.

Results

Interview analyses showed that patients often feel lost in the MRI scanner and left alone with MRI results and images while 90% of patients in the survey expressed a high interest in MRI education. Knowledge on MRI issues was fair with some important knowledge gaps. Major information interests were relevance of lesions as well as the prognostic and diagnostic value of MRI results. The education program was highly appreciated and resulted in a substantial knowledge increase. Patients reported that, based on the program, they felt more competent to engage in encounters with their physicians.

Conclusion

This work strongly supports the further development of an evidence-based MRI education program for MS patients to enhance participation in health-care.     

FunSpec: a web-based cluster interpreter for yeast

Background  

For effective exposition of biological information, especially with regard to analysis of large-scale data types, researchers need immediate access to multiple categorical knowledge bases and need summary information presented to them on collections of genes, as opposed to the typical one gene at a time.     

KID - an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes

Background  

The amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed.