A Review of the Literature on the Empathy Construct Using Cluster Analysis <s> Background <s> The paper describes the results of applying Latent Semantic Analysis (LSA), an advanced information retrieval method, to program source code and associated documentation. Latent semantic analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). Here LSA is used as the basis to cluster software components. This clustering is used to assist in the understanding of a nontrivial software system, namely a version of Mosaic. Applying latent semantic analysis to the domain of source code and internal documentation for the support of program understanding is a new application of this method and a departure from the normal application domain of natural language. <s> BIB001 </s> A Review of the Literature on the Empathy Construct Using Cluster Analysis <s> Background <s> Motivation: A major challenge in the interpretation of high-throughput genomic data is understanding the functional associations between genes. Previously, several approaches have been described to extract gene relationships from various biological databases using term-matching methods. However, more flexible automated methods are needed to identify functional relationships (both explicit and implicit) between genes from the biomedical literature. In this study, we explored the utility of Latent Semantic Indexing (LSI), a vector space model for information retrieval, to automatically identify conceptual gene relationships from titles and abstracts in MEDLINE citations. ::: ::: Results: We found that LSI identified gene-to-gene and keyword-to-gene relationships with high average precision. In addition, LSI identified implicit gene relationships based on word usage patterns in the gene abstract documents. Finally, we demonstrate here that pairwise distances derived from the vector angles of gene abstract documents can be effectively used to functionally group genes by hierarchical clustering. Our results provide proof-of-principle that LSI is a robust automated method to elucidate both known (explicit) and unknown (implicit) gene relationships from the biomedical literature. These features make LSI particularly useful for the analysis of novel associations discovered in genomic experiments. ::: ::: Availability: The 50-gene document collection used in this study can be interactively queried at http://shad.cs.utk.edu/sgo/sgo.html ::: ::: Contact: rhomayouni@utmem.edu ::: ::: Supplementary information: http://shad.cs.utk.edu/sgo/pubs.html <s> BIB002
The clustering of documents has been used effectively for a variety of applications such as finding gene relationships BIB002 , identifying software components BIB001 , grouping conference papers  ], and categorizing documents on the World Wide Web [Boley et al. 1999 ]. All of these applications apply one of a variety of clustering approaches to a large corpus of text to cluster the documents into distinct groups. Additionally, projection is a commonly used technique that is frequently employed in the information retrieval area [Berry and Browne 2005] . Therefore, the techniques that are at the core of the approach used in this study to review literature are not novel. However, we are not aware of any research that has combined cluster analysis and projection for the purpose of performing a literature review-our contribution is leveraging these two techniques for a new application.