Knowledge Discovery in Life Sciences: overview, case studies, complexities, and lessons learned
published: July 5, 2008, recorded: September 2007, views: 237
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Knowledge discovery is the process of developing and applying strategies to discover useful and ideally all previously unknown knowledge from historical or real-time data. Applied to biological and life sciences data, 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 experimental conditions such as in-vitro or in-vivo studies, and (iv) discovering models for accurate 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 focusing on bioinformatics domain and describe the BioMine project where we share our experiences on initiating and managing a data mining project involving several groups. In part two of this talk, we describe a few of our case studies using some existing or newly developed methods. 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. In the last part of this talk, we will describe complexities and challenges in dealing with real data, demonstrate important areas that need to be carefully understood in a typical data mining application, and share some of our experiences gained over the past 7 years.
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