scholarly journals The LAILAPS Search Engine: A Feature Model for Relevance Ranking in Life Science Databases

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Matthias Lange ◽  
Karl Spies ◽  
Christian Colmsee ◽  
Steffen Flemming ◽  
Matthias Klapperstück ◽  
...  
Keyword(s):  
Search Engine ◽  
Life Science ◽  
User Behavior ◽  
Science Research ◽  
Feature Model ◽  
Relevance Ranking ◽  
Great Chance ◽  
Regression Problem ◽  
Prediction Function ◽  
Search Engine Result

SummaryEfficient and effective information retrieval in life sciences is one of the most pressing challenge in bioinformatics. The incredible growth of life science databases to a vast network of interconnected information systems is to the same extent a big challenge and a great chance for life science research. The knowledge found in the Web, in particular in life-science databases, are a valuable major resource. In order to bring it to the scientist desktop, it is essential to have well performing search engines. Thereby, not the response time nor the number of results is important. The most crucial factor for millions of query results is the relevance ranking.In this paper, we present a feature model for relevance ranking in life science databases and its implementation in the LAILAPS search engine. Motivated by the observation of user behavior during their inspection of search engine result, we condensed a set of 9 relevance discriminating features. These features are intuitively used by scientists, who briefly screen database entries for potential relevance. The features are both sufficient to estimate the potential relevance, and efficiently quantifiable.The derivation of a relevance prediction function that computes the relevance from this features constitutes a regression problem. To solve this problem, we used artificial neural networks that have been trained with a reference set of relevant database entries for 19 protein queries.Supporting a flexible text index and a simple data import format, this concepts are implemented in the LAILAPS search engine. It can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. LAILAPS is publicly available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

scholarly journals The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

2010 ◽  
Vol 7 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Matthias Lange ◽  
Karl Spies ◽  
Joachim Bargsten ◽  
Gregor Haberhauer ◽  
Matthias Klapperstück ◽  
...  
Keyword(s):  
Neural Network ◽  
Search Engine ◽  
Life Science ◽  
Expert Knowledge ◽  
Feature Model ◽  
Training Data ◽  
Relevance Ranking ◽  
User Interactions ◽  
Daily Work ◽  
Machine Learning Approach

SummarySearch engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases.With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented.In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

scholarly journals The LAILAPS Search Engine: A Feature Model for Relevance Ranking in Life Science Databases

2010 ◽  
Author(s):  
Matthias Lange ◽  
Karl Spies ◽  
Christian Colmsee ◽  
Steffen Flemming ◽  
Matthias Klapperstück ◽  
...  
Keyword(s):  
Search Engine ◽  
Life Science ◽  
Feature Model ◽  
Relevance Ranking

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

scholarly journals Web Strategies for the Curation and Discovery of Open Educational Resources

Open Praxis ◽  
2016 ◽  
Vol 8 (4) ◽  
pp. 297 ◽  
Author(s):  
Vivien Rolfe
Keyword(s):  
Search Engine ◽  
Life Science ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Small Scale ◽  
Open Educational Resource ◽  
Educational Resource ◽  
Open Education ◽  
Pragmatic Approach ◽  
The Uk

For those receiving funding from the UK HEFCE-funded Open Educational Resource Programme (2009–2012), the sustainability of project outputs was one of a number of essential goals. Our approach for the hosting and distribution of health and life science open educational resources (OER) was based on the utilisation of the WordPress.org blogging platform and search engine optimisation (SEO) techniques to curate content and widen discovery.This paper outlines the approaches taken and tools used at the time, and reflects upon the effectiveness of web strategies several years post-funding. The paper concludes that using WordPress.org as a platform for sharing and curating OER, and the adoption of a pragmatic approach to SEO, offers cheap and simple ways for small-scale open education projects to be effective and sustainable.

scholarly journals A beginner’s guide to RT-PCR, qPCR and RT-qPCR

2020 ◽  
Vol 42 (3) ◽  
pp. 48-53 ◽  
Author(s):  
Grace Adams
Keyword(s):  
Gene Expression ◽  
Polymerase Chain Reaction ◽  
Messenger Rna ◽  
Life Science ◽  
Science Research ◽  
Quantitative Real Time Pcr ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Mrna Quantitation ◽  
Polymerase Chain

The development of the polymerase chain reaction (PCR), for which Kary Mullis received the 1992 Novel Prize in Chemistry, revolutionized molecular biology. At around the time that prize was awarded, research was being carried out by Russel Higuchi which led to the discovery that PCR can be monitored using fluorescent probes, facilitating quantitative real-time PCR (qPCR). In addition, the earlier discovery of reverse transcriptase (in 1970) laid the groundwork for the development of RT-PCR (used in molecular cloning). The latter can be coupled to qPCR, termed RT-qPCR, allowing analysis of gene expression through messenger RNA (mRNA) quantitation. These techniques and their applications have transformed life science research and clinical diagnosis.

scholarly journals Bioterrorism: Lessons Learned Since the Anthrax Mailings

mBio ◽  
2011 ◽  
Vol 2 (6) ◽  
Author(s):  
Michael J. Imperiale ◽  
Arturo Casadevall
Keyword(s):  
United States ◽  
Bacillus Anthracis ◽  
Life Science ◽  
Science Research ◽  
The United States ◽  
September 11 ◽  
Lessons Learned ◽  
Content Type ◽  
Risks And Benefits ◽  
September 11 Attacks

ABSTRACT In the fall of 2001, Bacillus anthracis spores were spread through letters mailed in the United States. Twenty-two people are known to have been infected, and five of these individuals died. Together with the  September 11 attacks, this resulted in a reevaluation of the risks and benefits of life science research with the potential for misuse. In this editorial, we review some of the results of these discussions and their implications for the future.

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