Knowledge discovery is the process of developing strategies to discover useful and ideally all previously unknown knowledge from historical or real-time data. Applied to high throughput genomics applications, knowledge discovery processes will help in various research and development activities, such as (i) studying data quality for possible anomalous or questionable expressions of certain genes or experiments, (ii) identifying relationships between genes and their functions based on time-series or other high throughput genomics profiles, (iii) investigating gene responses to treatments under various conditions such as in-vitro or in-vivo studies, and (iv) discovering models for clinical diagnosis/classifications based on expression profiles among two or more classes.
This presentation consists of three parts. In part one, we provide an overview of knowledge discovery in genomics and the BioMine project. In part two of this talk we describe some of our case studies using the BioMiner data mining software that we have built in this project. These are all cases in which real genomics data sets (obtained from public or private sources) have been used for tasks such as gene function identification and gene response analysis. We will describe a few examples explaining complexities and challenges in dealing with real data. In the last part of this talk, we share our experiences gained over the last 6 years and describe our current activities and future plans in BioIntelligence research direction.