Bayesian Network Models of Causal Interventions in Healthcare Decision Making: Literature Review and Software Evaluation

This report summarises the outcomes of a systematic literature search to identify Bayesian network models used to support decision making in healthcare. After describing the search methodology, the selected research papers are briefly reviewed, with the view to identify publicly available models and datasets that are well suited to analysis using the causal interventional analysis software tool developed in Wang B, Lyle C, Kwiatkowska M (2021). Finally, an experimental evaluation of applying the software on a selection of models is carried out and preliminary results are reported.Comment: 50 pages (19 + 31 Appendix

Applications of Mass Spectrometry in Proteomics and Pharmacokinetics

Tremendous technology improvements of the last decades has given mass spectrometry a more and more expanding role in the study of a wide range of molecules: from the identification and quantification of small molecular weight molecules to the structural determination of biomacromolecules. Many are the fields of application for this technique and the various versions of it. In the present study three different applications have been explored. The first application is a pharmacokinetics study of anticancer drug Gemcitabine and its principal metabolite, where the role of the LC-MS/MS is essential both for the selectivity of the detection of the small analytes and the sensitivity enhanced by multi-reaction monitoring experiments. The design of the study involved the collection of several blood samples at selected times and from patients that would have met certain eligibility criteria. The ESI demonstrated to be the most suitable approach and it provided the necessary data to conclude that toxicity of Gemcitabine did not increase when administered at FDR (Fixed Dose Rate) infusion in patients with impaired hepatic function. The second application describes an example of how MS represents a powerful tool in cancer research, from serum profiling study with high resolution MALDITOF and bioinformatic analysis, to the identification of potential biomarker through peak identification. Almost 400 serum sample – homogeneously distributed between biopsy confirmed ovarian cancer and high risk serum samples – were analyzed on a high resolution MALDI-TOF instrument after automated reverse phase magnetic beads separation. The high throughput data have undergone sophisticated bioinformatic procedures that lead to a list of upand down-regulated peaks, although identification studies were possible only for those peaks that showed a good reproducibility. One down-regolated peak has been identified using the LC-MS/MS technique. The identified peak confirmed a basic role of fibrinogen in the ovarian cancer; the other four peaks that have been identified as down-regulated showed an absolutely not satisfactory ionization in electro-spray, therefore further analysis will be performed on these analytes in order to determinate their amino acidic sequence. The most suitable technique seems to be MALDI-TOF/TOF mass spectrometry, since the peptides already showed a good degree of ionization in MALDI. The third and last study belongs to a quite new field, which is the combination of immuno precipitation assays with MALDI-TOF (Immuno Precipitation Mass Spectrometry, IPMS) experiments in order to evaluate the specificity of a series of monoclonal antibodies to specific antigen. The automated assay that has been developed provides structural information about the antigen that binds the monoclonal antibody to be tested and previously conjugated to the surface of magnetic beads, ideal support for robotic automation. IPMS showed its potential as a complementary tool of crucial importance in the selection of the monoclonal antibody for the development of ELISA based assay to be applied in the screening of a consistent number of human specimens for the clinical validation of proteins indicated in literature as potential biomarkers. Mass spectrometry in association with fractionation techniques, such as liquid or magnetic beads chromatography, is a very flexible tool in the cancer research field. Further improvement in the instrumentation and in the technology will bring always more and more results to be confident in

Applications of Mass Spectrometry in Proteomics and Pharmacokinetics

Tremendous technology improvements of the last decades has given mass spectrometry a more and more expanding role in the study of a wide range of molecules: from the identification and quantification of small molecular weight molecules to the structural determination of biomacromolecules. Many are the fields of application for this technique and the various versions of it. In the present study three different applications have been explored. The first application is a pharmacokinetics study of anticancer drug Gemcitabine and its principal metabolite, where the role of the LC-MS/MS is essential both for the selectivity of the detection of the small analytes and the sensitivity enhanced by multi-reaction monitoring experiments. The design of the study involved the collection of several blood samples at selected times and from patients that would have met certain eligibility criteria. The ESI demonstrated to be the most suitable approach and it provided the necessary data to conclude that toxicity of Gemcitabine did not increase when administered at FDR (Fixed Dose Rate) infusion in patients with impaired hepatic function. The second application describes an example of how MS represents a powerful tool in cancer research, from serum profiling study with high resolution MALDITOF and bioinformatic analysis, to the identification of potential biomarker through peak identification. Almost 400 serum sample – homogeneously distributed between biopsy confirmed ovarian cancer and high risk serum samples – were analyzed on a high resolution MALDI-TOF instrument after automated reverse phase magnetic beads separation. The high throughput data have undergone sophisticated bioinformatic procedures that lead to a list of upand down-regulated peaks, although identification studies were possible only for those peaks that showed a good reproducibility. One down-regolated peak has been identified using the LC-MS/MS technique. The identified peak confirmed a basic role of fibrinogen in the ovarian cancer; the other four peaks that have been identified as down-regulated showed an absolutely not satisfactory ionization in electro-spray, therefore further analysis will be performed on these analytes in order to determinate their amino acidic sequence. The most suitable technique seems to be MALDI-TOF/TOF mass spectrometry, since the peptides already showed a good degree of ionization in MALDI. The third and last study belongs to a quite new field, which is the combination of immuno precipitation assays with MALDI-TOF (Immuno Precipitation Mass Spectrometry, IPMS) experiments in order to evaluate the specificity of a series of monoclonal antibodies to specific antigen. The automated assay that has been developed provides structural information about the antigen that binds the monoclonal antibody to be tested and previously conjugated to the surface of magnetic beads, ideal support for robotic automation. IPMS showed its potential as a complementary tool of crucial importance in the selection of the monoclonal antibody for the development of ELISA based assay to be applied in the screening of a consistent number of human specimens for the clinical validation of proteins indicated in literature as potential biomarkers. Mass spectrometry in association with fractionation techniques, such as liquid or magnetic beads chromatography, is a very flexible tool in the cancer research field. Further improvement in the instrumentation and in the technology will bring always more and more results to be confident in

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Nowadays, large amounts of data are generated in the medical domain. Various physiological signals generatedfrom different organs can be recorded to extract interesting information about patients’health. The analysis ofphysiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery inDatabases process. The application of such process in the domain of medicine has a series of implications anddifficulties, especially regarding the application of data mining techniques to data, mainly time series, gatheredfrom medical examinations of patients. The goal of this paper is to describe the lessons learned and the experiencegathered by the authors applying data mining techniques to real medical patient data including time series. In thisresearch, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epilepticpatients). We applied a previously proposed knowledge discovery framework for classification purpose obtaininggood results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in ourresearch are the groundwork for the lessons learned and recommendations made in this position paper thatintends to be a guide for experts who have to face similar medical data mining projects.2019-2

Development of a statistical method for the identification of gene-environment interactions

In order to understand common, complex disease it is necessary to consider not just genetic risks and environmental risks, but also the interplay between them. This thesis aims to develop methodology for the detection of gene-environment interactions specifically; both by looking at the strengths and weaknesses of traditional approaches and through the development and testing of a novel statistical method. Developments in genotyping technology enable researchers to collect large volumes of polymorphisms in human genes, yet very few statistical methods are able to handle the volume, variation and complexity of this data, especially in combination with environmental risk factors. Interactions between genes and the environment are often subject to the curse of dimensionality, with each new variable increasing the potential number of interactions exponentially, leading to low power and a high false positive rate. The Mixed Tree Method (MTM) exploits the differences between environmental and genetic variables, by selecting the most appropriate features from conventional methods (including recursive partitioning, random forests and logistic regression) and combining them with new comparison algorithms which rank the genetic variables by the likelihood that they interact with the environmental variable under study. Results show the MTM to be as effective as the most successful current method for identification of interactions, but maintaining a much lower false positive rate and computational burden. As the number of SNPs in the dataset increases, the success of MTM compared to other methods becomes greater while the comparator approaches exhibit computational problems and rapidly increasing processing times. The MTM is also applied to a colorectal cancer dataset to show its use in a practical setting. The results together suggest that MTM could be a useful strategy for identifying gene environment interactions in future studies into complex disease

INTEGRATED MOLECULAR PROFILING FOR ANALYZING AND PREDICTING THERAPEUTIC MECHANISM, RESPONSE, BIOMARKER AND TARGET

Ph.DDOCTOR OF PHILOSOPH