Algorithms in Bioinformatics
暫譯: 生物資訊學中的演算法

Algorithms in Bioinformatics: Theory and Implementation is a concise yet comprehensive textbook of bioinformatics which describes some of the main algorithms that are used to elucidate biological functions and relationships. This unique guide to Algorithms in Bioinformatics approaches the subject along the four convergent lines of mathematics, implementation, simulation, experimentation, and can be ideal for upper-undergraduate bioinformatics courses, researchers, doctoral students, and sociologists or engineers charged with data analysis. This work first begins with a general introduction to biology, which is meant to bring a more concrete understanding of the molecular processes concerning the field of Bioinformatics. Following this introduction, an in-detail look is made to subjects like sequence alignment, forced alignment, detection of motifs, sequence logos, markov chains or information entropy. Other novel approaches are also described, such as self-sequence alignment, objective digital stains (ODSs) or spectral forecast and the discrete probability detector (DPD) algorithm. This work also contains thorough step-by-step explanations regarding the meaning of the background models in bioinformatics from several angles. More importantly, it introduces the readers to the art of algorithms; shows how to design computer implementations, and provides extensive worked examples with detailed case studies. The implementations presented here, point out how native programming in javascript can broaden the horizons of possibilities in bioinformatics by considering the might of modern Internet browsers. Graphical illustrations are used for technical details on computational algorithms to aid an in-depth understanding of their innerworkings. Moreover, this work brings to the reader attention more than 100 open source implementations and 33 powerpoint presentations.