uences were the dominant theme of bioinformatics in the early nineties. Machine learning mainly focused on predicting genes and proteins functions from their sequences and structures. The understanding of cellular functions and processes underlying complex diseases were out of reach. Bioinformatics scientists were a rare breed, and their contribution to molecular biology and genetics was considered marginal, because the computational tools available then for biomolecular data analysis were far more primitive than the array of experimental techniques and assays that were available to life scientists. Today, we are now witnessing the reversal of these past trends. Diverse sets of data types that cover a broad spectrum of genotypes and phenotypes, particularly those related to human health and diseases, have become available. Many interdisciplinary researchers, including applied computer scientists, applied mathematicians, biostatisticians, biomedical researchers, clinical scientists, and biopharmaceutical professionals, have discovered in biology a gold mine of knowledge leading to many exciting possibilities: unraveling of tree of life, harnessing the power of microbial organisms for renewable energy, finding new ways to diagnose disease early, and developing new therapeutic compounds that save lives. Much of the experimental high-throughput biology data are generated and analyzed “in haste”, therefore leaving plenty of opportunities for knowledge discovery even after the original data is released. Most of the bets on the race to separate the wheat from the chaff have been placed on biological data mining techniques. After all, when easy, straightforward, first-pass data analysis hasn’t yielded novel biological insights, data mining techniques must be able to help—or, many presumed so.
In reality, biological data mining is still much of an ‘art’, successfully practiced by a few bioinformatics research groups that occupy themselves in solving real-world biological problems. Unlikely data mining in business, where the 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 chap