# Identification and Characterisation of Technological Topics in the Field of Molecular Biology

Ivana Roche, Dominique Besagni, Claire François, Marianne Hörlesberger, Edgar L. Schiebel

► To cite this version:

Ivana Roche, Dominique Besagni, Claire François, Marianne Hörlesberger, Edgar L. Schiebel. Identification and Characterisation of Technological Topics in the Field of Molecular Biology. 10th International Conference on Science and Technology Indicators, Sep 2008, Vienne, Austria. pp.320-322, 10.1007/s11192-010-0178-8 . hal-00422211

HAL Id: hal-00422211

https://hal.science/hal-00422211v1

Submitted on 6 Oct 2009

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
# Identification and Characterisation of Technological Topics in the Field of Molecular Biology

Ivana Roche*

INIST-CNRS, 2 allée du Parc de Brabois, CS 10310, 54519 Vandoeuvre-les-Nancy, France, Tel.: +33 (0) 383504600, Fax: +33 (0) 383504650, ivana.roche@inist.fr

Dominique Besagni, Claire François

INIST-CNRS, 2 allée du Parc de Brabois, CS 10310, 54519 Vandoeuvre-les-Nancy, France, Tel.: +33 (0) 383504600, Fax: +33 (0) 383504650, dominique.besagni@inist.fr, claire.francois@inist.fr

Marianne Hörlesberger, Edgar Schiebel

Austrian Research Centers GmbH, Tech Gate Vienna, Donau-City-Straße 1, 1220 Wien, Austria, Tel.: +43 (0) 50 550-4524, Fax: +43 (0) 50 550-4500, marianne.hoerlesberger@arcs.ac.at, edgar.schiebel@arcs.ac.at

**Theme:** Primary: S&T indicators for the identification of emerging fields (Theme 2); Secondary: Visualisation and Science Mapping: tools, methods and applications (Theme 5).

**Keywords:** emerging technologies, bibliometric indicators, terminology evolution, diffusion model, diachronic clustering.

## 1 Background

This paper focuses on methodological approaches for characterising the specific topics within a technological field based on scientific literature data. We introduce a diachronic clustering analysis approach and some bibliometric indicators. The results are visualised with the software-tool Stanalyst® [1]. We are applying our methods to the field "Molecular Biology". This field has grown a great deal in the last decade.

## 2 Problem / application

How can we identify and characterise important topics in a set of several thousand articles? Which technological aspects can be detected? Which of them are already established and which of them are new? How are the topics linked to each other? We are trying to answer these questions by applying our bibliometric analysis methods to a set of scientific literature data recorded in the bibliographic database PASCAL.

<footnote>* Corresponding author</footnote>
## 3 Methodology

The data for our study were extracted from PASCAL database in function of their classification categories and keywords.

The diffusion model identifies three categories of terms: the established terms, terms unusual in this topic, and cross section terms. We apply the indicator TFIDF (text frequency inverse document frequency) adapted to our research question and the GINI coefficient, a measure of statistical dispersion most prominently used as a measure of inequality of income distribution or inequality of wealth distribution.

The diachronic cluster analysis is realized with the help of a clustering tool applying first the axial K-means method to produce a non-hierarchical clustering algorithm based on the neuronal formalism of Kohonen's self-organizing maps and then a principal component analysis to map the obtained clusters. This tool is implemented in the information analysis platform Stan analyst®. Considering two successive time periods, our diachronic approach enables us to follow the field time evolution by analysing the two obtained cluster sets and maps.

The applied methods are linked together as the following table shows.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">Diffusion Model</th></tr><tr><th>Unusual Terms</th><th>Established Terms</th><th>Cross Section Terms</th></tr></thead><tbody><tr><td rowspan="2"><strong>Diachronic Cluster Analysis</strong></td><td>Clusters in the First Period</td><td></td><td></td><td></td></tr><tr><td>Clusters in the Second Period</td><td>new terms</td><td>terms with roots in the first period</td><td>terms with roots in the first period</td></tr></tbody></table>

## 4 Outcome/findings/results

The diachronic cluster analysis is made for two periods and shows which topics of the second period have roots in the first one and which topics are new in the second period.
<figure><img src="image_2.png" /></figure>

On the map of both periods, as we can see in the above figure, it is possible to singularize an interesting dichotomous configuration represented by two very strongly connected cluster networks associating about two thirds of the clusters. However, these networks have quite different characteristics. On the one hand, the themes present in the biggest one are related to the modelling and simulation of biological phenomena. In the other hand, the little network is very homogeneous and deals essentially with instrumentation topics. The remaining clusters are scattered in the map with no significant links with the two above described networks.

Concerning the biggest network, located in the left side of the first period map and in the right side of the second period map, we can observe that structurally it remains the same with two sub-networks brought together by the cluster "Computerized simulation". But its content analysis allows us to find some interesting thematic evolutions. In the first period, its content is focused on both the description of physical characteristics of biological structures and the theoretical aspects related to the physiological process modelling using, for example, neural networks. In the second period, we can detect a refocusing of the couple formed by the clusters "Neural networks" and "Stochastic processes" and an impressive densification of the resulting sub-network with the new associated cluster "Brownian motion".

The smallest network, located in the right side of the first period map, deals with instrumentation techniques applied to measure and therapeutic issues. On the second period map, the network is located in the left side and shows a significant stability despite a reorganization of cluster contents.
**5 Conclusion**

"Molecular Biology" is a broad field. By applying the complementary methods presented here, it can be characterised and described presenting different views of the features of the field. On the other hand, the two methods point out the relationship of the different topics in our investigated field and their evolution.

**6 References:**

FERBER, R. (2003). Information Retrieval, Suchmodelle und Data-Mining-Verfahren für Textsammlungen und das Web, Heidelberg: dpunkt.

POLANCO, X.; FRNAÇOIS, C.; ROYAUTE, J.; BESAGNI, D.; ROCHE, I. (2001). Stanalyst®: An Integrated Environment for Clustering and Mapping Analysis on Science and Technology, In: Proceedings of the 8th ISSI, Sydney, July 16-20, 2001.

ROBERTSON, S. (2004). Understanding Inverse Document Frequency: On theoretical arguments for IDF, Journal of Documentation 60 no. 5, 503-520.

SCHIEBEL, E.; HÖRLESBERGER, M. (2007). About the Identification of Technology Specific Keywords in Emerging Technologies: The Case of "Magnetoelec-tronic". Torres-Salinas, D., Moed, H. F. (Eds), Proceedings of ISSI 2007, 11th International Conference of the Inter-national Society for Scientometrics and Informetrics, June 25th-27th, Madrid, 691-69.

SPÄRCK, J. K.; ROBERTSON, S. (2006). Inverse Document Frequency - The IDF page. Retrieved November 22, 2006 from: http://www.soi.city.ac.uk/~ser/idf.html.

VAN RIJSBERGEN, C.J. (1979). Information Retrieval, London: Butterworths.