In this pilot study we examine the performance of text-based profiling in recovering a set of validated inventor-author links. In a first step we match patents and publications solely based on their similarity in content. Next, we compare inventor and author names on the highest ranked matches for the occurrence of name matches. Finally, we compare these candidate matches with the names listed in a validated set of inventor-author names. Our text-based profile methodology performs significantly better than a random matching of patents and publications, suggesting that text-based profiling is a valuable complementary tool to the name searches used in previous studies.

In the present study full-text analysis and traditional bibliometric methods are combined to improve the efficiency of the individual methods in the mapping of science. The methodology is applied to map research papers from a special issue of Scientometrics. The outcomes substantiate that such hybrid methodology can be applied to both research evaluation and information retrieval. The subject classification given by the guest-editors of the special issue is used for validation purposes. Because of the limited number of papers underlying the study the paper is considered a pilot study that will be extended in a later study on the basis of a larger corpus.

In the present study results of an earlier pilot study by Glenisson, Glänzel and Persson are extended on the basis of larger sets of papers. Full text analysis and traditional bibliometric methods are serially combined to improve the efficiency of the two individual methods. The text mining methodology already introduced in the pilot study is applied to the complete publication year 2003 of the journal Scientometrics. Altogether 85 documents that can be considered research articles or notes have been selected for this exercise. The outcomes confirm the main results of the pilot study, namely, that such hybrid methodology can be applied to both research evaluation and information retrieval. Nevertheless, Scientometrics documents published in 2003 cover a much broader and more heterogeneous spectrum of bibliometrics and related research than those analysed in the pilot study. A modified subject classification based on the scheme used in an earlier study by Schoepflin and Glänzel has been applied for validation purposes.

Terms are the building blocks to organize and access information, and hold a key position in information retrieval. In forthcoming work we have shown how a methodology of indexing full-text scientific articles combined with an exploratory statistical analysis can improve on bibliometric approaches to mapping science. Textual documents are indexed and further characterized using data mining techniques and co-word analysis. We start this paper by briefly demonstrating the text mining approach. Whereas statistical processing based on full-text documents provides a relational view based on the topicality represented by these documents, bibliometric components can include other characteristics that describe their position in the set. Therefore we extend on previous work and explore how hybrid methodologies that deeply combine text analysis and bibliometric methods can improve the mapping of science and technology. In particular, we propose a method to mathematically combine document similarity matrices resulting from vector-based indices on the one hand, and from selected bibliometric indicators on the other hand. Weighted linear combinations as well as approaches inspired on statistical meta-analysis are presented. Both pitfalls and possible solutions are discussed. The resulting combined similarity matrix offers an attractive way to 'co-cluster' documents based on both lexical and bibliographic information.

We implemented a framework called TXTGate that combines literature indices of selected public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. By means of tailored vocabularies, term- as well as gene-centric views are offered on selected textual fields and MEDLINE abstracts used in LocusLink and the Saccharomyces Genome Database. Subclustering and links to external resources allow for in-depth analysis of the resulting term profiles.

The current tendency in the life sciences to spawn ever growing amounts of high-throughput assays has led to a situation where the interpretation of data and the formulation of hypotheses lag the pace at which information is produced. Although the first generation of statistical algorithms scrutinizing single, large-scale data sets found their way into the biological community, the great challenge to connect their results to existing knowledge still remains. Despite the fairly large number of biological databases that is currently available, a lot of relevant information is found in free-text format (such as textual annotations, scientific abstracts and full publications). In this paper we explore how an integrated analysis of expression data and literature-extracted information can reveal biologically meaningful clusters not identified when using microarray information alone. The joint analysis is validated in terms of transcriptional regulation.

INCLUSive is a suite of algorithms and tools for the analysis of gene expression data and the discovery of cis-regulatory sequence elements. The tools allow normalization, filtering and clustering of microarray data, functional scoring of gene clusters, sequence retrieval, and detection of known and unknown regulatory elements using probabilistic sequence models and Gibbs sampling. All tools are available via different web pages and as web services. The web pages are connected and integrated to reflect a methodology and facilitate complex analysis using different tools. The web services can be invoked using standard SOAP messaging. Example clients are available for download to invoke the services from a remote computer or to be integrated with other applications. All services are catalogued and described in a web service registry. The INCLUSive web portal is available for academic purposes at http://www.esat.kuleuven.ac.be/inclusive.

The current tendency in the life sciences to spawn ever growing amounts of high-throughput assays has led to the situation were the interpretation of data and the formulation of hypotheses lag the pace with which information is produced. Although the first generation of statistical algorithms scrutinizing single, large-scale data sets found their way into the biological community, the great challenge to connect their results to the existing knowledge still remains. Despite the fairly large number of biological databases that is currently available, we find a lot of relevant information presented in free-text format (such as textual annotations, scientific abstracts, and full publications). Moreover, many of the public interfaces do not allow queries with a broader scope than a single biological entity (gene or protein). We implemented a methodology that covers various public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. We discuss and exemplify how structured term- and concept-centric views complement each other in presenting gene summaries.

Thanks to its increasing availability, electronic literature can now be a major source of information when developing complex statistical models where data is scarce or contains much noise. This raises the question of how to deeply integrate information from domain literature with experimental data. Evaluating what kind of statistical text representations can integrate literature knowledge in clustering still remains an unsufficiently explored topic. In this work we discuss how the bag-of-words representation can be used successfully to represent genetic annotation and free-text information coming from different databases. We demonstrate the effect of various weighting schemes and information sources in a functional clustering setup. As a quantitative evaluation, we contrast for different parameter settings the functional groupings obtained from text with those obtained from expert assessments and link each of the results to a biological discussion.

Thanks to its increasing availability, electronic literature can now be a major source of information when developing complex statistical models where data is scarce or contains much noise. This raises the question of how to integrate information from domain literature with statistical data. Because quantifying similarities or dependencies between variables is a basic building block in knowledge discovery, we consider here the following question. Which vector representations of text and which statistical scores of similarity or dependency support best the use of literature in statistical models? For the text source, we assume to have annotations for the domain variables as short free-text descriptions and optionally to have a large literature repository from which we can further expand the annotations. For evaluation, we contrast the variables similarities or dependencies obtained from text using different annotation sources and vector representations with those obtained from measurement data or expert assessments. Specifically, we consider two learning problems: clustering and Bayesian network learning. Firstly, we report performance (against an expert reference) for clustering yeast genes from textual annotations. Secondly, we assess the agreement between text-based and data-based scores of variable dependencies when learning Bayesian network substructures for the task of modeling the joint distribution of clinical measurements of ovarian tumors.

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.