Biomedical subjects

Steven Carroll

Publications and source records attributed to Steven Carroll.

4 recordsLinked to original sources

PubMed2006

Automated recognition of malignancy mentions in biomedical literature.

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.

Algorithms↗

PubMed2006

Protein classification using probabilistic chain graphs and the Gene Ontology structure.

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.

Algorithms↗

PubMed2006

Integrated risk framework for onsite wastewater treatment systems.

Onsite wastewater treatment systems (OWTS) are becoming increasingly important for the treatment and dispersal of effluent in new urbanised developments that are not serviced by centralised wastewater collection and treatment systems. However, the current standards and guidelines adopted by many local authorities for assessing suitable site and soil conditions for OWTS are increasingly coming under scrutiny due to the public health and environmental impacts caused by poorly performing systems, in particular septic tank-soil adsorption systems. In order to achieve sustainable onsite wastewater treatment with minimal impacts on the environment and public health, more appropriate means of assessment are required. This paper highlights an integrated risk based approach for assessing the inherent hazards associated with OWTS in order to manage and mitigate the environmental and public health risks inherent with onsite wastewater treatment. In developing a sound and cohesive integrated risk framework for OWTS, several key issues must be recognised. These include the inclusion of relevant stakeholders throughout framework development, the integration of scientific knowledge, data and analysis with risk assessment and management ideals, and identification of the appropriate performance goals for successful management and mitigation of associated risks. These issues were addressed in the development of the risk framework to provide a generic approach to assessing risk from OWTS. The utilisation of the developed risk framework for achieving more appropriate assessment and management techniques for OWTS is presented in a case study for the Gold Coast region, Queensland State, Australia.

Industrial Waste↗

PubMed2004

Imidazole acetic acid TAFIa inhibitors: SAR studies centered around the basic P(1)(') group.

Structural modifications of the aminopyridine P(1)(') group of imidazole acetic acid based TAFIa inhibitors led to the discovery of the aminocyclopentyl analog 28, a 1 nM TAFIa inhibitor with CLT(50) functional activity of 14 nM but without selectivity against CPB. While not as active, aminobutyl derivative 27 provided an improved 6.7-fold selectivity for TAFIa versus CPB.

Carboxypeptidase B2↗