PMID: 16820427;Abstract:
One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference. © 2006 Oxford University Press.
PMID: 9232526;Abstract:
This research examined the applicability of using a neural network approach to analyze population pharmacokinetic data. Such data were collected retrospectively from pediatric patients who had received tobramycin for the treatment of bacterial infection. The information collected included patient- related demographic variables (age, weight, gender, and other underlying illness), the individual's dosing regimens (dose and dosing interval), time of blood drawn, and the resulting tobramycin concentration. Neural networks were trained with this information to capture the relationships between the plasma tobramycin levels and the following factors: patient-related demographic factors, dosing regimens, and time of blood drawn. The data were also analyzed using a standard population pharmacokinetic modeling program, NONMEM. The observed vs predicted concentration relationships obtained from the neural network approach were similar to those from NONMEM. The residuals of the predictions from neural network analyses showed a positive correlation with that from NONMEM. Average absolute errors were 33.9 and 37.3% for neuraL networks and 39.9% for NONMEM. Average prediction errors were found to be 2.59 and -5.01% for neural networks and 17.7% for NONMEM. We concluded that neural networks were capable of capturing the relationships between plasma drug levels and patient-related prognostic factors from routinely collected sparse within-patient pharmacokinetic data. Neural networks can therefore be considered to have potential to become a useful analytical tool for population pharmacokinetic data analysis.
Abstract:
The problem of information and knowledge management in the knowledge intensive and time critical environment of law enforcement has posed an interesting problem for information technology professionals in the field. Coupled with this challenging environment are issues relating to the integration of multiple systems, each having different functionalities resulting in difficulty for the end user. COPLINK offers a cost-efficient way of web enabling stovepipe law enforcement information sharing systems by employing a model for allowing different police departments to more easily share data amongst themselves through an easy-to-use interface that integrates different data sources. The COPLINK project has two major components: COPLINK Database (DB) Application and COPLINK Concept Space (CS) Application. The COPLINK DB design facilitates retrieval of case details based on known information. COPLINK CS is an investigative tool that captures the relationships between objects (e.g., people, locations, vehicles, organizations, crime types) in the entire database allowing investigators and detectives to perform investigative associations and case analysis. This paper describes how we have applied the design criteria of platform independence, stability, scalability, and an intuitive graphical user interface to develop the COPLINK systems. Results of user evaluations that have been conducted on both applications to study the impact of COPLINK on law enforcement personnel. The COPLINK DB Application is currently being deployed at the Tucson Police Department and the Conctept Space is undergoing further modifications. Future development efforts for COPLINK project will also be discussed.
Abstract:
Timely information access and effective knowledge support is crucial to law enforcement officers' crime fighting and investigations. An expanding array of e-government initiatives target the development of advanced information technologies and their deployment in law enforcement agencies. Abase in point is COPLINK, an integrated system that provides law enforcement officers with timely data access, effective information support, integrated knowledge sharing, and improved collaboration within or beyond the agency boundaries. In this study, we examine law enforcement officers' acceptance of COPLONK Mobile by proposing and testing a factor model premised in established theoretical foundations. According to our results, the model is capable of explaining or predicting officers' intentions to use the technology. Our survey data support the proposed model and the hypotheses it suggests. Among the acceptance determinants we investigated, perceived usefulness appears to have the most significant influence on individual officers' intention to use COPLONK Mobile. Copyright © 2009 ACM.
Abstract:
Identity verification is essential in our mission to identify potential terrorists and criminals. It is not a trivial task because terrorists reportedly assume multiple identities using either fraudulent or legitimate means. A national identification card and biometrics technologies have been proposed as solutions to the identity problem. However, several studies show their inability to tackle the complex problem. We aim to develop data mining alternatives that can match identities referring to the same individual. Existing identity matching techniques based on data mining primarily rely on personal identity features. In this research, we propose a new identity matching technique that considers both personal identity features and social identity features. We define two groups of social identity features including social activities and social relations. The proposed technique is built upon a probabilistic relational model that utilizes a relational database structure to extract social identity features. Experiments show that the social activity features significantly improve the matching performance while the social relation features effectively reduce false positive and false negative decisions. © 2010 Springer Science+Business Media, LLC.
