The increasing availability of health-related datasets and the advances in processing capabilities and technology related to data management and the big data spectrum have led to great interest in analyzing biomedical and e-health data. This book is composed of 13 independent chapters that cover several topics related to health informatics data analysis. In particular, the following areas are reviewed: building fitness functions for protein structures, analyzing mass spectometry imaging (MSI) data, identifying and annotating human long non-coding RNAs (lncRNAs), analyzing metabolomics data to characterize human diseases, collecting and analyzing genomes contained in an environmental sample (metagenomics), assessing risk factors for diseases using single-nucleotide polymorphism (SNP) data and Bayesian data analysis methods, exploiting imaging and genetic data for association analysis (imaging genetics), using biomedical imaging informatics to determine disease markers, annotating/classifying electrocardiogram (ECG) data, analyzing and visualizing electroencephalographic (EEG) data, applying data mining techniques in health informatics at different data levels (molecular level, tissue level, patient level, and population level), managing hypertension using a telehealth computational infrastructure, and performing association rule mining for healthcare.

The chapters are written by different authors and they are completely independent from each other, exhibiting varied quality and comprehensiveness. Most chapters are quite specialized in specific topics and require significant background knowledge of the medical domain. In this sense, from my point of view, the book lies much closer to the medical domain than to the computer science (CS) domain: CS experts in data mining that want to extend their knowledge to the e-health or biomedical area as a new application domain will not find a smooth learning path with this book (unless they have previous background knowledge or the support of an expert in the medical area).

A positive aspect of the book is the high number of bibliographic references contained in each chapter, which will help the interested reader to acquire in-depth knowledge of the corresponding topic. On the negative side, significant copyediting seems to be required in some places, sometimes affecting readability. It would have also been interesting to include a chapter dealing with the topic in a broader way, for example, providing a general overview and background knowledge, analyzing the challenges and future trends, and relating the different topics studied in the remaining chapters. Including a global list of acronyms used in the book would have also been beneficial.

Finally, in general, many examples contained in the book are not easily reproducible in a didactic way. It would be nice to enhance the examples presented throughout the book with a more detailed step-by-step guide for non-experts in the medical domain, including a reader-friendly analysis and interpretation of the results obtained along with a summary of lessons learned. Nevertheless, readers from bioinformatics and health informatics areas may find some specific chapters of the book particularly relevant, depending on their research interests.