Ontologies for Bioinformatics (Computational Molecular Biology) Review

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Ontologies for Bioinformatics (Computational Molecular Biology) ReviewI was disappointed in this book in the lack of depth or breadth on a couple of key areas as follows:
Item 1 - It appeared to me that ontologies were not explained in sufficient detail to help "newbies" determine exactly why a specific ontology was created and the specific functions it is designed to support. I'm considering the UMLS to prototype a search engine because it is a compilation of a number of ontologies. However, this provides numerous options for subsetting the UMLS; for those relatively new to these ontologies it is not clear which ontology subsets are the most important (i.e., which play a pivotal or lesser role for my intended use). For example, I can include SNOMED and HL7 subsets, but do they provide redundant concepts for my needs and if so, in which areas? It is not practical for me to learn then all, so I was hoping to obtain this information from this book. Unfortunately I was unable to do so quickly and have to dig more on my own (which was the reason to buy the book.....).
Item 2 - I noticed a couple of areas that may be missing for those more interested in the tools and process of using ontologies. For example, I did not see the Protege and LexGrid tools for visualization, from Stanford and Mayo Clinic respectively, in the index (I had located them in prior web search). In a more general sense, as I've been working with ontologies more and more, I've noticed that platform selection and interoperability seem to be stumbling blocks. This topic was not a major focus in the book.
Due to the breadth and complexity of the platforms and tools needed to effectively use ontologies, and the complexity and size of the ontologies themselves, addressing both of these large subjects in sufficient detail in one book may be very difficult. Perhaps this is the reason that this book did not reach the 5 stars potential. Perhaps it would if it had addressed one or the other, but not both.
Perhaps if the description and title clarified the focus of the book, I would have gotten a better idea of the content before purchasing (and unfortunately, returning).
Keep in mind that I'm relatively new to using ontologies.Ontologies for Bioinformatics (Computational Molecular Biology) OverviewRecent advances in biotechnology, spurred by the Human Genome Project,have resulted in the accumulation of vast amounts of new data. Ontologies --computer-readable, precise formulations of concepts (and the relationship amongthem) in a given field -- are a critical framework for coping with the exponentialgrowth of valuable biological data generated by high-output technologies. This bookintroduces the key concepts and applications of ontologies and ontology languages inbioinformatics and will be an essential guide for bioinformaticists, computerscientists, and life science researchers.The three parts of Ontologies forBioinformatics ask, and answer, three pivotal questions: what ontologies are; howontologies are used; and what ontologies could be (which focuses on how ontologiescould be used for reasoning with uncertainty). The authors first introduce thenotion of an ontology, from hierarchically organized ontologies to more generalnetwork organizations, and survey the best-known ontologies in biology and medicine.They show how to construct and use ontologies, classifying uses into threecategories: querying, viewing, and transforming data to serve diverse purposes.Contrasting deductive, or Boolean, logic with inductive reasoning, they describe thegoal of a synthesis that supports both styles of reasoning. They discuss Bayesiannetworks as a way of expressing uncertainty, describe data fusion, and propose thatthe World Wide Web can be extended to support reasoning with uncertainty. They callthis inductive reasoning web the Bayesian web.

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