Full text clustering and relationship network analysis of biomedical publications.

Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP) to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

Introduction

With the proliferation of biomedical research and publications, individual scientists can no longer keep track of relevant articles through reading alone. Instead, they must rely on text mining tools to explore the implicit knowledge and hypotheses presented in biomedical texts and to extract the data and concepts relevant to their work [1], [2]. In this regard, a number of new challenges have emerged, especially in relation to full text analysis [3], complex relation extraction [4], [5], and information fusion [6].

To date, research approaches to biomedical text mining have been based exclusively on article abstracts (Iliopoulos et al., 2001 [7]; Yu and Lee 2006 [8]; Zhu et al., 2009 [9]), culminating most recently in 2011, when Boyack et al managed to cluster about two million MEDLINE abstracts [10]. While such clustering can provide hints about primary results and conclusions, the full texts of articles are where more detailed methodologies, experimental results, critical discussions and interpretations are found.

Rodriguez-Esteban pointed out that full article texts are the only source for certain crucial information, such as experimental measurements [11]. Recent research initiatives in bioinformatics, such as TREC Genomics (http://ir.ohsu.edu/ge-nomics/) and BioCreAtIvE (http://www.biocreative.org/), also recommend migrating text analysis from abstracts to full texts [12]. Intuitively, consultation only of abstracts is inadequate for judicious analysis of biomedical articles, and can even lead to inappropriate clinical decisions [13]–[15].

In the Genomics2005 data collected in [9], the mean number of documents in the 100 datasets was 690.7 and the average number of unique words was 2214.4. By contrast, in our experiments using a BioMed dataset containing 600 texts, the unique word count was 54367—approximately 24 times greater than the count for Genomics2005. With the addition of these terms, it is expected that biomedical text mining will provide more relevant information and more complete analysis.

Unfortunately, access to the full text and citations of biomedical papers remains limited, and the complexity of full text mining, which must contend with large amounts of information noise, is much greater than that of abstract text mining. While the number of non-redundant words (terms) in a biomedical abstract text is typically less than 100, that of a full text is often much greater than 1000. Since most of mining frameworks employ the vector space model (VSM) [16], which treats a document as a bag of words and uses plain language terms as features, the dimensions of a full text corpus can be several times greater than that of abstract data.

Clustering is one of the most popular techniques for data analysis in many disciplines [17]. The basic strength of text clustering is its capacity to automatically organize texts into meaningful groups. As such, it has been applied in a number of ways, including cluster-based retrieval [18], key sentence extraction [19], concept discovery in molecular biology [7], and so on. Traditional clustering also forms an important component of unsupervised learning algorithms, which along with various supervised techniques, play an important role in biomedical research, including studies of cancer [20], Bayesian analysis of HIV drug resistance [21], linear regression frameworks for motif finding [22], etc.

In supervised text mining algorithms, a training process is first performed on a large set of known predefined topics and text labels. This generally results in better topic detection [23] than unsupervised approaches, for which there is no prior training step. Obviously, supervised and unsupervised learning algorithms can work together to improve learning processes or handle more complex problems, such as phosphorylation site prediction [24]. However, training set labeling is in general very costly and frequently unavailable in practical scenarios. In recent years, semi-supervised clustering approaches have caught the attention of the machine learning community. These approaches make use of a smaller and more easily obtained set of labeled samples to guide clustering strategy. They have been applied in many application domains, including text clustering [25], gene expression analysis [26], and image processing [27]. However, to the best of our knowledge, semi-supervised learning has not yet been applied in full text mining of real biomedical publications.

In this paper, the Semi-supervised Affinity Propagation (SSAP) method of text clustering is proposed. Then this method is applied to the corpus of a real biomedical text database called BioMed Central open access full-text corpus, and compare its clustering performance to that of two classical clustering algorithms. Finally, a directed relationship network and a cluster distribution matrix are constructed based on the SSAP clustering results, and use these to reveal publication interests among the top 10 journals. The source code and datasets used in this paper are available in the Supporting Information.

Materials and Methods

Datasets

The dataset used in the experiments was the BioMed Central open-access corpus (BioMed), which can be downloaded at http://www.biomedcentral.com/about/datamining. From the BioMed corpus, 110,369 articles between Jan. 2012 to Jun. 2012 are downloaded. Each BioMed file is a well-formatted XML document, and contains various tags, such as