MOTIVATION: Probabilistic graphical models have been developed in the past for the task of protein classification. In many cases, classifications obtained from the Gene Ontology have been used to validate these models. In this work we directly incorporate the structure of the Gene Ontology into the graphical representation for protein classification. We present a method in which each protein is represented by a replicate of the Gene Ontology structure, effectively modeling each protein in its own 'annotation space'. Proteins are also connected to one another according to different measures of functional similarity, after which belief propagation is run to make predictions at all ontology terms. RESULTS: The proposed method was evaluated on a set of 4879 proteins from the Saccharomyces Genome Database whose interactions were also recorded in the GRID project. Results indicate that direct utilization of the Gene Ontology improves predictive ability, outperforming traditional models that do not take advantage of dependencies among functional terms. Average increase in accuracy (precision) of positive and negative term predictions of 27.8% (2.0%) over three different similarity measures and three subontologies was observed. AVAILABILITY: C/C++/Perl implementation is available from authors upon request.

BACKGROUND: The rapid proliferation of biomedical text makes it increasingly difficult for researchers to identify, synthesize, and utilize developed knowledge in their fields of interest. Automated information extraction procedures can assist in the acquisition and management of this knowledge. Previous efforts in biomedical text mining have focused primarily upon named entity recognition of well-defined molecular objects such as genes, but less work has been performed to identify disease-related objects and concepts. Furthermore, promise has been tempered by an inability to efficiently scale approaches in ways that minimize manual efforts and still perform with high accuracy. Here, we have applied a machine-learning approach previously successful for identifying molecular entities to a disease concept to determine if the underlying probabilistic model effectively generalizes to unrelated concepts with minimal manual intervention for model retraining. RESULTS: We developed a named entity recognizer (MTag), an entity tagger for recognizing clinical descriptions of malignancy presented in text. The application uses the machine-learning technique Conditional Random Fields with additional domain-specific features. MTag was tested with 1,010 training and 432 evaluation documents pertaining to cancer genomics. Overall, our experiments resulted in 0.85 precision, 0.83 recall, and 0.84 F-measure on the evaluation set. Compared with a baseline system using string matching of text with a neoplasm term list, MTag performed with a much higher recall rate (92.1% vs. 42.1% recall) and demonstrated the ability to learn new patterns. Application of MTag to all MEDLINE abstracts yielded the identification of 580,002 unique and 9,153,340 overall mentions of malignancy. Significantly, addition of an extensive lexicon of malignancy mentions as a feature set for extraction had minimal impact in performance. CONCLUSION: Together, these results suggest that the identification of disparate biomedical entity classes in free text may be achievable with high accuracy and only moderate additional effort for each new application domain.

Much of the research in compiler design and optimization has traditionally focused on the effectiveness and efficiency of code optimization. However, the subject of efficiency of the entire compilation process itself (as opposed to the complexity of individual analysis or optimization algorithms) remains a highly complex and less investigated topic. In this paper we present a global approach to extensible and efficient compiler design, which aims at also improving the effectiveness and efficiency of analysis and optimization capabilities. Extensibility in complex compiler systems goes well beyond modularity of design and it needs to be considered from the early stages of the design, especially the design of the Intermediate Representation (IR). One of the primary barriers to compiler pass extensibility and modularity is interference between passes caused by transformations that invalidate existing analysis information. In this paper we also present a callback system which is provided to automatically track changes to the compiler's internal representation (IR) allowing full pass reordering and an easy-to-use interface for developing lazy update incremental analysis passes. We present a new algorithm for incremental interprocedural data flow analysis and demonstrate the benefits of our design framework and our prototype compiler system. It is shown that compilation time for multiple data flow analysis algorithms can be cut in half by incrementally updating data flow analysis. Our quantitative performance analysis is based on well-known benchmarks instead of random graphs like previous works. For smaller optimizations, order of magnitude improvements have been demonstrated. Our approach also simplifies design and implementation and hides many of the intricacies of a compiler's internal structures from the developer.