BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.

/pdf/the-obo-foundry-coordinated-evolution-of-ontologies-to-3mst0iaq12.pdf

The OBO Foundry : coordinated evolution of ontologies to support biomedical data integration

From the Book:
“Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons:
Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). 
In Test-Driven Development, you:
Write new code only if you first have a failing automated test.Eliminate duplication.

Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy

The two rules imply an order to the tasks ofprogramming:
1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first
2. Green—make the test work quickly, committing whatever sins necessary in the process
3. Refactor—eliminate all the duplication created in just getting the test to work


Red/green/refactor. The TDD’s mantra.

Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications:
If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers


So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage.
Courage
Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects:
Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy

None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and:
Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) 

Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet.

The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test.

Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately.

That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress.

There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code.

Once you are finished reading this book, you should be ready to:
Start simplyWrite automated testsRefactor to add design decisions one at a time

This book is organized into three sections.
An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples.

I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference.

Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read.

A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

Test Driven Development: By Example

The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents. The meaningful constructs provided by OWL 2 are defined in terms of their structure. As well, a functional-style syntax is defined for these constructs, with examples and informal descriptions. One can reason with OWL 2 ontologies under either the RDF-Based Semantics [OWL 2 RDF-Based Semantics] or the Direct Semantics [OWL 2 Direct Semantics]. If certain restrictions on OWL 2 ontologies are satisfied and the ontology is in OWL 2 DL, reasoning under the Direct Semantics can be implemented using techniques well known in the literature.

/pdf/owl-2-web-ontology-language-structural-specification-and-16ue9glrem.pdf

OWL 2 Web Ontology Language: structural specification and functional-style syntax

Understanding the relation of the two most widely-used ontology modeling paradigms, OWL (and other DL paradigm) to frame paradigms is an important issue. Because the Protege tool supports both paradigms within a single overall framework, it provides an opportunity to compare the two paradigms both in theory and practice. In this paper, we focus on the relationship between the Frames paradigm, as defined in the OKBC specification 1 and implemented in core Protege, and OWL and its implementation in Protege-OWL. OWL provides three increasingly expressive sublanguages, OWL Lite, OWL DL, and OWL Full, for different communities of users. In this paper, we will primarily use OWL DL as the basis for comparison, as OWL DL provides the description-logic reasoning capabilities that are the distinctive features of OWL. Therefore, unless otherwise noted, when we use “OWL”, we are actually referring to OWL DL. Contrastingly, there is not a single standard frame representation language analogous to OWL. The past standardization effort has produced the Open Knowledge-Base Connectivity (OKBC) protocol, which is a generic access and manipulation layer for knowledge representation services. For the purpose of this paper, we will primarily use the Protege-Frames implementation of the OKBC Knowledge Model. When appropriate, we comment on features of frame representations that may not be available in Protege. Unless otherwise noted, when we use “Frames,” we are actually referring to Protege-Frames.

https://protege.stanford.edu/conference/2006/submissions/slides/7.2wang_protege2006.pdf

Frames and OWL Side by Side

Being able to extend an OWL ontology with some form of rules is a feature that many ontology developers consider as very important. Nevertheless, working with rules in practice can be difficult since the tool support is not as good as for handling standard ontologies. Furthermore, the existing rule syntaxes are not very well aligned with the new OWL 2 standard. We propose, therefore, an extension to OWL 2 for representing rules, which is directly inspired by (DL Safe) SWRL rules, but uses and extends the succinct and human-readable functional-style syntax of OWL 2. We also propose an OWL/XML version of the syntax for easy XML serialization. Support for parsing such rules has been added to the new OWL API 3.0 and reasoning support is available in the two OWL 2 reasoners Pellet and HermiT. In HermiT, these rules can also be used in conjunction with description graphs.

/pdf/a-syntax-for-rules-in-owl-2-vk44v3qdx1.pdf

A syntax for rules in OWL 2

The Manchester OWL syntax is a user-friendly syntax for OWL DL, fundamentally based on collecting all information about a particular class, property, or individual into a single construct, called a frame. The Manchester OWL syntax has been revised to be a syntax for OWL 1.1, involving adding the new OWL 1.1 description constructs and the new axioms allowed in OWL 1.1 ontologies.

/pdf/manchester-syntax-for-owl-1-1-9nl0ce8cvw.pdf

Manchester Syntax for OWL 1.1.

This paper describes integrated tools support for OWL 1.1 in the form of the FaCT++ Description Logic reasoner and the ProtegeOWL ontology editor. Challenges of designing and implementing OWL 1.1 reasoning algorithms are highlighted, and an outline of an OWL 1.1 API and editing environment is provided.

/pdf/supporting-early-adoption-of-owl-1-1-with-protege-owl-and-211us64rak.pdf

Supporting early adoption of OWL 1.1 with Protégé-OWL and FaCT++

This paper presents use cases for modular development of ontologies using the OWL imports mechanism. Many of the methods are inspired by work in modular development in software engineering. The approach is aimed at developers of large ontologies covering multiple subdomains that make use of OWL reasoners for inference. Such ontologies are common in biomedical sciences, but nothing in the paper is specific to biomedicine. There are four groups of use cases: (i) organisation and factoring of ontologies; (ii) maintaining stable interfaces and bindings between ontologies and between ontologies and software; (iii) localization of ontologies to the requirements of specific sites and (iv) extension of ontologies and encapsulation of modifications. OWL's axiom-oriented import mechanism has many similarities with import mechanisms in object-oriented software but also important differences - in particular, the effects of OWL imports are global, and the order in which modules are imported is irrelevant. The advantages and disadvantages of OWL's axiom-oriented approach are discussed, and suggestions are made for extensions to allow axioms to be filtered out as well as added - a mechanism that we term “adaptation” to distinguish it from the standard import mechanism. Finally we discuss possible alternatives and practical experience with the approaches presented.

Matthew Horridge

Papers

Frames and OWL Side by Side

A syntax for rules in OWL 2

Manchester Syntax for OWL 1.1.

Supporting early adoption of OWL 1.1 with Protégé-OWL and FaCT++

Engineering use cases for modular development of ontologies in OWL