The local DNA conformation in the region of transcription factor binding sites, determined by context, is one of the factors underlying the specificity of DNA–protein interactions. Analysis of the local conformation of a set of functional DNA sequences may allow for determination of the conservative conformational and physicochemical parameters reflecting molecular mechanisms of interaction. The web resource SITECON is designed to detect conservative conformational and physicochemical properties in transcription factor binding sites, contains a knowledge base of conservative properties for >100 high-quality sample sites and allows for recognition of potential transcription factor binding sites based on conservative properties from both the knowledge base and the results of analysis of a sample proposed by a user. The resource SITECON is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/sitecon/.

Discusses eliminating contradictions among rules in computer-aided systems, experts rules, and databases. The study has demonstrated how consistent data mining in medical diagnosis can create a set of logical diagnostic rules for computer-aided diagnostic systems. Consistency avoids contradiction among rules generated using data mining software, rules used by an experienced radiologist, and a database of pathologically confirmed cases. The authors identified major problems: to find contradiction between diagnostic rules and to eliminate contradiction. They applied two complimentary intelligent technologies for extraction of rules and recognition of their contradictions. The first technique is based on discovering statistically significant logical diagnostic rules. The second technique is based on the restoration of a monotone Boolean function to generate a minimal dynamic sequence of questions to a medical expert. The results of this mutual verification of expert and data-driven rules demonstrate feasibility of the approach for designing consistent computer-aided diagnostic systems.

An inductive probabilistic approach to formal concept analysis (FCA) is proposed in which probability on formal contexts is considered; probabilistic formal concepts that have predictive force are defined: nonclassified objects can be assigned to earlier found probabilistic formal concepts; random attributes are eliminated from probabilistic formal concepts; probabilistic formal concepts are robust with respect to data noise. A result of experiment is presented in which formal concepts (in their standard definition in FCA) are first distorted by random noise and then recovered by detecting probabilistic formal concepts.