My motivation for reviewing this started 6 years ago. To be precise:  on Tuesday, 22/08/06, between 10:30 and 12:30 in Hong Kong. At that time I was presenting our poster “Matching 2D Shapes Using their Symmetry Sets” (www.computer.org/csdl/proceedings/icpr/2006/2521/02/252120179-abs.html) at ICPR 2006. So this is also a story about why I actually love poster sessions!

The paper dealt with some mathematical aspects of shape matching, and I had some examples showing the results of shape matching on the Kimia data set. Especially, a set of tools and fishes got nice results, and because of that, Ilkka approached me saying that these were exactly the shapes he was interested in, basically because they were mostly elongated with some additional structures. He had similar shapes obtained from the internet that visualized microRNA structures and wanted to compare such shapes. Although I was a bit skeptical, we exchanged email addresses, and (lo and behold!) we had contact, exchanged data and it did work out well. We even published some papers together.

So, I love poster sessions! ☺

But let’s get back to the book. When I learned that Ilkka had written a book, I was of course interested in it. While writing our papers, it was already clear that although our backgrounds intersected a bit, they didn’t completely overlap. This book is a clear proof of that. BioInformatics is a booming research area, but the biggest problem is the different backgrounds, interests, and languages for computer scientists and biologists. For me, the microRNA was ‘only’ an application, while for Ilkka the matching was merely an ‘algorithm’. Without the will to understand each other more deeply, fruitful collaboration is quite difficult.

This book tries to bridge this gap. For completeness, the topics covered start with an Introduction to Modern Molecular Biology, then discuss the Biodata Explosion before going into depth with the chapters that deal with discovery, comparison, searching, and classification: Local Pattern Discovery and Comparison Genes and Proteins, Global Pattern Discovery and Comparison Genomes, Molecule Structure Based Searching and Comparison, and Function Annotation and Ontology Based Searching and Classification.

Two chapters discuss novel methods that are starting to find their way into bioinformatics: New Methods for Genomics Data: SVM and Others and Integration of Multimodal Data: Toward Systems Biololy. The book ends with the almost obligatory Future Challenges.

For biologists, it describes relevant pattern matching tools for finding relevant and/or related genomes and molecule structures. For PR people, it describes the used biological data – a huge jungle in which one easily gets lost. Nice, predefined ground truth data sets are most of the time not there, and formats differ. This makes ‘simply applying known methods’ almost impossible.

The chapters are meant as an introduction to the topic. Each of them contains a rich list of references for further reading. This makes the book a good source for those starting in biodata mining and analysis, but also for those willing to broaden their scope. Evidently, as the field is booming, the subtitle “novel approaches” may be outdated soon, but that is the inevitable problem with state of the art reports.

I found the book very interesting and complete. Perhaps is was a bit hard to read every now and then, but very useful as, for instance, here in Darmstadt we are also collaborating with biologists in Interactive Visual Comparison of Multiple Phylogenetic Trees (cf. Chapter 8) (www.gris.informatik.tu-darmstadt.de/research/vissearch/projects/ViPhy/) and massively-parallel (GPU) implementation of the computation of (co)evolutionary signals from biomolecular sequence alignments based on mutual information and a normalization procedure to neutral evolution (www.gris.informatik.tu-darmstadt.de/projects/comic/) (cf. Chapter 9).

For those interested, several chapters of the book can be downloaded from the web site of Worldscientific: www.worldscientific.com/worldscibooks/10.1142/6709#t=toc.