he major concerns are often related to the bottom line—profit, the goals of biological data mining can be as diverse as the spectrum of biological questions that exist. In the business domain, association rules discovered between sales items are immediately actionable; in biology, any unorthodox hypothesis produced by computational models has to be first red-flagged and is lucky to be validated experimentally. In the internet business domain, classification, clustering, and visualization of blogs, network traffic patterns, and news feeds add significant values to regular internet users who are unaware of high-level patterns that may exist in the data set; in molecular biology and genetics, any clustering or classification of the data presented to biologists may promptly elicit questions like “great, but how and why did it happen?” or “how can you explain these results in the context of the biology I know?” The majority of general-purpose data mining techniques do not take into considerations prior knowledge domain of the biological problem, leading them to often underperform hypothesis-driven biological investigative techniques. The high level of variability of measurements inherent in many types of biological experiments or samples, the general unavailability of experimental replicates, the large number of hidden variables in the data, and the high correlation of biomolecular expression measurements also constitute significant challenges in the application of classical data mining methods in biology. Many biological data mining projects are attempted and then abandoned, even by experienced data mining scientists. In the extreme cases, large-scale biological data mining efforts are jokingly labeled as fishing expeditions and dispelled, in national grant proposal review panels.
This book represents a culmination of our past research efforts in biological data mining. Through this book, we wanted to showcase a small, but noteworthy sample of successful projects involving data mining and molecular biology. Each chapter of the book is authored by a distinguished team of bioinformatics scientists whom we invited to offer the readers the widest possible range of application domains. To ensure high quality standards, each contributed chapter went through standard peer-reviews and a round of revisions. Contributed chapters have been grouped into four major sections. The first section, entitled Sequence, Structure, and Function, collects contributions on data mining techniques designed to analyze biological sequences and structures with the objective of discovering novel functional knowledge. The second section on Genomics, Transcriptomics, and Proteomics, contains studies addressing emerging large-scale data mining challenges in analyzing high-throughput ‘omics’ data. The chapters in the third section, entitled Functional and Molecular Interaction Networks, address emerging system-scale molecular properties and their relevance to cellular functions. The fourth section is about Literature, Ontology, and Knowledge Integrations, and it collects chapters related to knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The contributed works in the fifth and last section, entitled Genome Medicine Applications, address emerging biological data mining applications in medicine.
We believe this book can serve as a valuable guide to the field for graduate students, researchers, and practitioners. We hope that the wide range of topics covered will allow readers to appreciate the extent of the impact of data mining in molecular biology and genetics. For us, research in data mining and its applications to biology and genetics is fascinating and rewarding. It may even help save human lives one day. This field offers great opportunities and rewards if one is prepared to learn molecular biology and genetics, design user-friendly software tools under the proper biological assumptions, and validate all discovered hypothesis rigorously using appropriate models.
In closing, we would li