Weidong Mao Libri

Focusing on genetic susceptibility to complex diseases, this study diverges from traditional statistical approaches by incorporating techniques from computer science. It emphasizes the importance of haplotyping and predicting genetic risk through a novel combinatorial prediction complexity measure. With the rise of high-throughput biology data, including extensive SNP and genotype datasets, the book aims to bridge the gap between biology and data science. It serves as a resource for professionals in biology, bioinformatics, genetic epidemiology, and researchers in computer science and data mining.

The accessibility of high-throughput biology data brought a great deal of attention to disease association studies. High density maps of single nucleotide polymorphism (SNP's) as well as massive genotype data with large number of individuals and number of SNP's become publicly available. By now most analysis of the new data is undertaken by the statistics community. This study pursues a different line of attack on genetic susceptibility to complex disease that adheres to the computer science community. The main goal of disease association analysis is to identify gene variations contributing to the risk of susceptibility to a particular disease. There are basically two main steps in susceptibility: the haplotyping of the population (also referred as phasing) and the predicting the genetic susceptibility to diseases. A combinatorial prediction complexity measure has been proposed for case/control studies. This book is addressed to prefessionals in biology, bioinformatics, and genetic epidemiology. It is also directed towards researchers in Computer Science and Data Mining and Discovery.