Peter Antal

Evaluation of the vector space representation in text-based gene clustering.

Thanks to its increasing availability, electronic literature can now be a major source of information when developing complex statistical models where data is scarce or contains much noise. This raises the question of how to deeply integrate information from domain literature with experimental data. Evaluating what kind of statistical text representations can integrate literature knowledge in clustering still remains an unsufficiently explored topic. In this work we discuss how the bag-of-words representation can be used successfully to represent genetic annotation and free-text information coming from different databases. We demonstrate the effect of various weighting schemes and information sources in a functional clustering setup. As a quantitative evaluation, we contrast for different parameter settings the functional groupings obtained from text with those obtained from expert assessments and link each of the results to a biological discussion.

Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection

Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to estimate an informative prior for a black-box model (e.g. a multilayer perceptron). Two technical approaches are proposed for the transformation of the belief network into an informative prior. The first one consists in generating samples according to the most probable parameterization of the Bayesian belief network and using them as virtual data together with the real data in the Bayesian learning of a multilayer perceptron. The second approach consists in transforming probability distributions over belief network parameters into distributions over multilayer perceptron parameters. The essential attribute of the hybrid methodology is that it combines prior knowledge and statistical data efficiently when prior knowledge is available and the sample is of small or medium size. Additionally, we describe how the Bayesian approach can provide uncertainty information about the predictions (e.g. for classification with rejection). We demonstrate these techniques on the medical task of predicting the malignancy of ovarian masses and summarize the practical advantages of the Bayesian approach. We compare the learning curves for the hybrid methodology with those of several belief networks and multilayer perceptrons. Furthermore, we report the performance of Bayesian belief networks when they are allowed to exclude hard cases based on various measures of prediction uncertainty.

Bayesian networks in ovarian cancer diagnosis: potentials and limitations

The pre-operative discrimination between malignant and benign masses is a crucial issue in gynaecology. Next to the large amount of background knowledge, there is a growing amount of collected patient data that can be used in inductive techniques. These two sources of information result in two different modelling strategies. Based on the background knowledge, various discrimination models have been constructed by leading experts in the field, tuned and tested by observations. Based on the patient observations, various statistical models have been developed, such as logistic regression models and artificial neural network models. For the efficient combination of prior background knowledge and observations, Bayesian network models are suggested. We summarize the applicability of this technique, report the performance of such models in ovarian cancer diagnosis and outline a possible hybrid usage of this technique.

Annotated Bayesian networks: a tool to integrate textual and probabilistic medical knowledge

We have previously (2000) reported on the development of Bayesian network models for the pre-operative discrimination between malignant and benign ovarian masses. The models incorporated both medical background knowledge and patient data, which required the traceability of the incorporated prior medical knowledge. For this purpose, we followed a particular annotation method for Bayesian networks using a dedicated representation. In this paper, we present the resulting annotated Bayesian network (ABN) representation that consists of a regular Bayesian network, with standard probabilistic semantics, and a corresponding semantic network, to which textual information sources are attached. We demonstrate the applicability of such a dual model to represent both the rigorous probabilistic and the unconstrained textual medical knowledge. We describe methods on how these ABN models can be used: (1) as a domain model to arrange the personal textual information of a clinician according to the semantics of the domain, (2) in decision support to provide detailed (and even personalized) explanation, and (3) to enhance the information retrieval to find new textual information more efficiently.

On the potential of domain literature for clustering and Bayesian network learning

Thanks to its increasing availability, electronic literature can now be a major source of information when developing complex statistical models where data is scarce or contains much noise. This raises the question of how to integrate information from domain literature with statistical data. Because quantifying similarities or dependencies between variables is a basic building block in knowledge discovery, we consider here the following question. Which vector representations of text and which statistical scores of similarity or dependency support best the use of literature in statistical models? For the text source, we assume to have annotations for the domain variables as short free-text descriptions and optionally to have a large literature repository from which we can further expand the annotations. For evaluation, we contrast the variables similarities or dependencies obtained from text using different annotation sources and vector representations with those obtained from measurement data or expert assessments. Specifically, we consider two learning problems: clustering and Bayesian network learning. Firstly, we report performance (against an expert reference) for clustering yeast genes from textual annotations. Secondly, we assess the agreement between text-based and data-based scores of variable dependencies when learning Bayesian network substructures for the task of modeling the joint distribution of clinical measurements of ovarian tumors.

Domain knowledge based information retrieval language: an application of annotated Bayesian networks in ovarian cancer domain

The increasing amount and variety of domain knowledge and the availability of increasingly large quantities of electronic literature requires new types of support for the development of complex knowledge models. P. Antal et al. (2001) proposed the application of so-called annotated Bayesian networks (ABNs), which are textually-enriched probabilistic domain models that help knowledge engineers and medical experts to find and organize the information that is necessary in model-building. In this paper, we describe an information retrieval language in which the formalized domain knowledge and the attached textual information can be accessed in an integrated fashion and can be used to define various retrieval schemes and relevance measures. This language on the one hand provides maximum flexibility for knowledge engineers to exploit the available annotated domain model as contextual information. On the other hand, it allows the definition of complex, high-level queries, in which the contextual use of the annotated domain model can be optimized for clinical situations. We compare the performance of the standard and the proposed query language in the ovarian cancer domain.

Web-based data collection for uterine adnexal tumors: a case study

We have developed a World Wide Web application for the collection of EPRs (electronic patient records) from uterine adnexal masses pre-operatively examined with transvaginal ultrasonography. The application has been used intensively since November 2000 by nine of the 19 international centers that joined the International Ovarian Tumor Analysis (IOTA) consortium. The IOTA database contains 68 parameters for 1,150 masses. We report the design and implementation of the generic Web-based clinical data entry system and describe the advantages and drawbacks that we have experienced while developing, using and maintaining the system. The data model, the user interface, the help system, the constraints (mandatory/optional) and the quality checking were all based on the medical protocol created by the IOTA consortium. The data collection system has become an open and transparent implementation of the formalized protocol. It covers the complete path of the patient data from the clinical situation to the finalized database. This approach provides new types of possibilities for the data analysis, since all aspects of the data collection are documented and formally available to the data analyst. The IOTA Web site can be found at , which also serves as the entry point for the secure EPR application.