An algorithm for identifying novel targets of transcription factor families: application to hypoxia-inducible factor 1 targets.

Efficient and effective analysis of the growing genomic databases requires the development of adequate computational tools. We introduce a fast method based on the suffix tree data structure for predicting novel targets of hypoxia-inducible factor 1 (HIF-1) from huge genome databases. The suffix tree data structure has two powerful applications here: one is to extract unknown patterns from multiple strings/sequences in linear time; the other is to search multiple strings/sequences using multiple patterns in linear time.

RETNA: From Requirements to Testing in a Natural Way.

Most problems in building and refining a system can be traced back to errors in requirements. Poorly organized requirements, most often in natural language are among the major causes of failures of software projects. In this paper, we present a requirements analysis tool called RETNA and the technology behind it.

Does Your Result Checker Really Check?

A result checker is a program that checks the output of the computation of the observed program for correctness. Introduced originally by Blum, the result checking paradigm has provided a powerful platform assuring the reliability of software. However, constructing result checkers for most problems requires not only significant domain knowledge but also ingenuity and can be error prone.

Predicting Fault Prone Modules by the Dempster-Shafer Belief Networks.

This paper describes a novel methodology for predicting fault prone modules. The methodology is based on Dempster-Shafer (D-S) belief networks. Our approach consists of three steps: First, building the Dempster-Shafer network by the induction algorithm; Second, selecting the predictors (attributes) by the logistic procedure; Third, feeding the predictors describing the modules of the current project into the inducted Dempster-Shafer network and identifying fault prone modules.