Bio-medical Ontologies Maintenance and Change Management

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224 M. Berlingerio et al. In this section we report a real-world medical case study, in which the TAS mining paradigm is applied to clinical data regarding a set of patients in the follow-up of a liver transplantation. The aim of the data analysis is that of assessing the effectiveness of the extracorporeal photopheresis (ECP) as a therapy to prevent rejection in solid organ transplantation. For each patient, a set of biochemical variables is recorded at different time moments after the transplantation. The TAS patterns extracted show the values of interleukins and other clinical parameters at specific dates, from which it is possible for the physician to assess the effectiveness of the ECP therapy. The temporal information contained in the TAS patterns extracted is a fruitful knowledge that helps the physicians to evaluate the outcome of the ECP therapy even during the therapy itself. We believe that this case study does not only show the interestingness of extracting TAS patterns in this particular context but, more ambitiously, it suggests a general methodology for clinical data mining, whenever the time dimension is an important variable of the problem in analysis. The interestingness of the case study by the medical perspective lies in the uniqueness and relevance of the dataset under analysis. While by the data analysis perspective, the interestingness lies (i) in the structure of the data under analysis, (ii) in the importance of the temporal dimension, and (iii) in the repeatability of the experience to other medical data analyses where the data exhibit the same structure. In fact, a typical structure of medical data is a sequence of observations of clinical parameters taken at different time moments. Here the time dimension is crucial: the focus is not only on the observed values, nor only in the sequence they compose, but the typical time that elapses among two events is also very important. For instance, the time elapsed from the start of a certain therapy, to the appearance of a certain clinical phenomenon. The main contribution of this section is to provide a methodological approach to this kind of data, by means of Time-Annotated Sequences (TAS ) mining [7, 8]. The rest of the section is organized as follows. In section 5.1 we describe the biological problem studied in this section. In Section 5.2 we introduce the TAS paradigm and how this can be used for mining time-annotated data. Section 5.3 describes the real-world case study in which the TAS paradigm is applied to the photopheresis dataset. 5.1 Extracorporeal Photopheresis as a Therapy against Allograft Rejection In this subsection we describe the biological background to the problem presented in this section. Originally introduced for treatment of cutaneous T-cell lymphomas [6] and autoimmune diseases [16], extracorporeal photopheresis (ECP) has been reported to be effective to reverse acute heart, lung, and kidney allograft
Mining Clinical, Immunological, and Genetic Data 225 rejection episodes, as well as to treat acute and chronic graft-versus-host disease (GVHD) [14]. ECP is performed through a temporary peripheral venous access. It has been speculated that ECP modulates alloreactive Tcell responses by multiple mechanisms: induction of apoptosis, inhibition of antigen-driven T-cell proliferation, and reduced expression of cell surface receptors. To date, the body of evidence supporting the use of ECP in the treatment of solid organ graft rejection stems from experiences with heart, lung, and renal transplant recipients. In the setting of liver transplantation (LT), acute graft rejection is nearly always amenable to reversal with steroid pulses. Severe graft rejection episodes or those not responding to steroid treatment may be reversed by means of lymphocytolytic antibody therapy. However, the treatment of rejection is not devoid of complications, namely those related to high-dose immunosuppression and steroid pulses. Therefore, the use of ECP for allograft rejection in LT recipients might represent a valid alternative to overcome the side effects associated with current treatment modalities. In [28], ECP showed no added morbidity or mortality, was well tolerated, and no procedure-related complications were observed. Its efficacy to reverse rejection was clinically remarkable. The use of ECP allowed reduction in immunosuppression in three out of five patients. Noteworthy, ECP was not associated with HCV or HBV flares in the present experience. Such data need long-term confirmation but they may contribute to an expanded application of ECP for patients grafted for viral cirrhosis, thereby helping to reduce the use of steroids in this category of patients. In conclusion, the results in [28] suggest that ECP may represent a valuable alternative to treat graft rejection in selected recipients. Emerging issues awaiting clarification concern indications to ECP, timing and length of treatment, and its cost-effectiveness ratio. In the case study described in the following, we analyze the dataset collected in [28]. Since prior to [28] only anecdotal reports describe the use of ECP in immunosuppressive protocols for LT recipients, and only one case of a LT recipient treated by ECP has appeared in the international literature [18], we can conclude that this is a unique dataset of this kind. 5.2 From Sequential Patterns to TAS In this section we summarize the main aspects of the Sequential Pattern Mining (introduced first in [3]), how it has evolved in the TAS [7,8]paradigm, and how this can be used as a general paradigm for time-annotated data. Sequential Pattern Mining Frequent Sequential Pattern mining (FSP) deals with the extraction of frequent sequences of events from datasets of transactions; those, in turn, are time-stamped sequences of events (or sets of events) observed in some
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Amandeep S. Sidhu and Tharam S. Dil
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Amandeep S. Sidhu and Tharam S. Dil
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Preface The molecular biology commu
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Preface VII biomedical systems, and
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X Contents Mining Clinical, Immunol
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2 A.S. Sidhu, M. Bellgard, and T.S.
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Towards Bioinformatics Resourceomes
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2 The New Waves Towards Bioinformat
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2.4 Semantic Web Towards Bioinforma
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Protein Data Integration Problem 57
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Fig. 3. Class Hierarchy of Protein
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Bio-medical Ontologies Maintenance
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TextToOnto (Maedche and Volz 2001)
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Extraction of Constraints from Biol
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Extraction of Constraints from Biol
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Completing the Total Wellbeing Puzz
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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