|to Students, Professionals, and Researchers interested in Data Mining and Knowledge Discovery in Biology|
This 733-page book examines the concepts, problems, progress, and trends in developing and applying data mining techniques in genome biology, a rapidly growing field of study. By studying the concepts and case studies presented in the book, readers can gain significant insight and develop practical solutions in future biological data mining projects.
Modern biology has become an information science. Since the invention of DNA sequencing method by Sanger in the late seventies, public repositories of genomic sequences have been growing exponentially, doubling in size every sixteen months—a rate often compared to the growth of semiconductor transistor densities in CPUs known as Moore’s Law. In the nineties, the public-private race to sequence the human genome further intensified the fervor to generate high-throughput biomolecular data from highly parallel and miniaturized instruments. Today, sequencing data from thousands of genomes, including plants, mammals, and microbial genomes are accumulating at an unprecedented rate. The advent of second-generation DNA sequencing instruments, high-density cDNA microarrays, tandem mass spectrometers, and high-power NMRs, have fueled the growth of molecular biology into a wide spectrum of disciplines such as personalized genomics, functional genomics, proteomics, metabolomics, and structural genomics. Few experiments in molecular biology and genetics performed today can afford to ignore the vast amount of biological information accessible publicly. Suddenly, molecular biology and genetics have become data rich.
Biological data mining is a data-guzzling turbo engine for post-genomic biology, driving the competitive race towards unprecedented biological discovery opportunities in the 21st century. Classical bioinformatics emerged from the study of macromolecules in molecular biology, biochemistry and biophysics. Analysis, comparison, and classification of DNA and protein sequences 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 t