The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of Genomics and Proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years there is a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature, and find the nuggets of information most relevant and useful for specific analysis tasks. This paper

Literature mining is the process of extracting and combining facts from scientific publications. In recent years, many studies have resulted in computer programs to extract various molecular biology findings using Medline abstracts or full text articles. This article describes the range of techniques that have been applied in literature mining. In doing so, it divides automated reading into four general subtasks: text categorization, named entity tagging, fact extraction and collection-wide analysis. Special attention is given to the domain particularities of molecular biology.

Introduction More and more, a successful understanding of complex genetic mechanisms (such as regulation, functional understanding,...) critically depends on the interaction between statistical analysis and various knowledge sources, such as annotations databases, specialized literature, and curated cross-links be- tween them (Baxevanis a). Despite these efforts, the current interaction between the experimental (data) analysis and text-based information requires extensive user intervention. Gene expression experiments, which measure large-scale genetic activity under a variety of biological conditions, are excellent examples of environments that rely strongly on this interaction. Indeed as (1) the cost of data collection is high, (2) measurements are often noisy or unreliable, and (3) established relationships in the transcriptome are fragmentary at best, a deeper integration between data and text-based information will benefit the knowledge discovery process. The present strategies

In a hierarchical categorization problem, categories are partially ordered to form a hier-archy. In this dissertation, we explore two main aspects of hierarchical categorization: learning algorithms and performance evaluation. We introduce the notion of consistent hierarchical classification that makes classification results more comprehensible and easily interpretable for end-users. Among the previously introduced hierarchical learning algo-rithms, only a local top-down approach produces consistent classification. The present work extends this algorithm to the general case of DAG class hierarchies and possible internal class assignments. In addition, a new global hierarchical approach aimed at performing consistent classification is proposed. This is a general framework of convert-ing a conventional “flat ” learning algorithm into a hierarchical one. An extensive set of experiments on real and synthetic data indicate that the proposed approach significantly outperforms the corresponding “flat ” as well as the local top-down method. For eval-uation purposes, we use a novel hierarchical evaluation measure that is superior to the existing hierarchical and non-hierarchical evaluation techniques according to a number