Causal Models for the Result of Percutaneous Coronary Intervention in Coronary Chronic Total Occlusions

Background: Patients undergoing coronary angiography very frequently exhibit coronary chronic total occlusions (CTOs). Over the last decade, there has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs due to, among else, rising operator experience and advances in technology. This study is an effort to address the problem of identifying important factors related to the success or failure of the PCI. Methods: The analysis is based on the EuroCTO Registry, which is the largest database available worldwide, consisting of 164 variables and 29,995 cases for the period 2008–2018. The aim is to assess the dynamics of causal models and causal discovery, using observational data, in predicting the result of the PCI. Causal models use graph structure to assess the cause–effect relationships between variables. In this study, the constrained-based algorithm PC was employed. The focus was to find the local causal structure around the PCI result and use it as a feature selection tool for building a predictive model. Results: The model developed was compared with other modeling approaches from the literature, and it was found to perform equally well or better. Conclusions: The analysis showcased the potential of employing local causal structure in predictive model development

Causal models with application in biosciences

The aim of this PhD thesis was to investigate the dynamics of the application of causal models to biological data from different scientific fields. In recent years, large biological databases have been and continue to be systematically developed. Many factors have contributed to this, such as interdisciplinary research synergies and rapid technological developments. The existence of such data bases creates the need for modern analytical methods, which will be able to respond to the special requirements that are emerging, and to make use of the rich information available. In this direction, causal models constitute a tool with significant and largely unexplored potential. Their competitive advantages over conventional analytical methods can be the springboard for the development of innovative methodological approaches and applications in the life sciences, enhancing the efforts to exploit the data available and extract new knowledge. In the context of this thesis, it was investigated: (a) the optimization of the selection of the predictive factors and the final prediction of the dependent variable using the local causal structure, (b) the identification of important biological mechanisms by exploiting the causal models in omics data, (c) the comparison of the causal structure of the independent variables between different categories of the dependent variable, and the robustness of the causal structure under data interventions.Ο στόχος της παρούσας διδακτορικής διατριβής ήταν να διερευνήσει τη δυναμική της εφαρμογής των αιτιατών μοντέλων σε βιολογικά δεδομένα από διαφορετικά επιστημονικά πεδία. Τα τελευταία χρόνια έχουν αναπτυχθεί και συνεχίζουν να αναπτύσσονται συστηματικά μεγάλες βάσεις βιολογικών δεδομένων. Σε αυτό έχουν συνεισφέρει πολλοί παράγοντες όπως οι διεπιστημονικές ερευνητικές συνέργειες και οι ραγδαίες τεχνολογικές εξελίξεις. Η ύπαρξη τέτοιων βάσεων δημιουργεί την ανάγκη για σύγχρονες μεθόδους ανάλυσης, που θα μπορούν να ανταποκριθούν στις ιδιαίτερες απαιτήσεις που δημιουργούνται και να αξιοποιήσουν την πλούσια διαθέσιμη πληροφορία. Στην κατεύθυνση αυτή, τα αιτιατά μοντέλα προβάλλουν ως ένα εργαλείο με σημαντική και σε μεγάλο βαθμό ανεξερεύνητη δυναμική. Τα ανταγωνιστικά τους πλεονεκτήματα σε σχέση με συμβατικές μεθόδους ανάλυσης μπορούν να αποτελέσουν το εφαλτήριο για την ανάπτυξη καινοτόμων μεθοδολογικών προσεγγίσεων και εφαρμογών στις βιοεπιστήμες, ενισχύοντας την προσπάθεια για αξιοποίηση των δεδομένων και την εξαγωγή νέας γνώσης. Στο πλαίσιο της παρούσας διατριβής ερευνήθηκε: (α) η βελτιστοποίηση της επιλογής των προβλεπτικών παραγόντων και της τελικής πρόβλεψης της εξαρτημένης μεταβλητής με χρήση της τοπικής αιτιατής δομής, (β) ο εντοπισμός σημαντικών βιολογικών μηχανισμών αξιοποιώντας τα αιτιατά μοντέλα σε ομικά δεδομένα, (γ) η σύγκριση της αιτιατής δομής των ανεξάρτητων μεταβλητών μεταξύ των διαφορετικών κατηγοριών της εξαρτημένης μεταβλητής και η ανθεκτικότητα της αιτιατής δομής σε παρεμβάσεις στα δεδομένα

Causal Discovery on Health-related Quality of Life of cancer patients

The management of cancer patients increasingly includes Health-related Quality of Life (HRQoL) as a crucial endpoint. Physical, psychological, lifestyle, and social aspects expressed via responses to HRQoL questionnaires offer valuable insights for patient care. However, a still unexplored field is the identification and understanding of causal relationships among the questions involved. This study evaluates the capability of detecting cause-effect relationships in this context, applying causal structure-learning algorithms to simulated data. Different data configurations are examined, encompassing the number of hypothetical questions in an HRQoL questionnaire, the quantity of cause-effect relationships, and the number of participants involved. Exploring this issue holds potential significance in shaping the design and/or selection of HRQoL questionnaires, accounting for limitations in sample size and intuition regarding the underlying causal structure. Uncovering cause-effect relationships can contribute to enhanced management and improved HRQoL for cancer patients

Could Causal Discovery in Proteogenomics Assist in Understanding Gene–Protein Relations? A Perennial Fruit Tree Case Study Using Sweet Cherry as a Model

Genome-wide transcriptome analysis is a method that produces important data on plant biology at a systemic level. The lack of understanding of the relationships between proteins and genes in plants necessitates a further thorough analysis at the proteogenomic level. Recently, our group generated a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. ‘Tragana Edessis’ tissues represented by 29,247 genes and 7584 proteins. The aim of the current study was to perform a targeted analysis at the gene/protein level to assess the structure of their relation, and the biological implications. Weighted correlation network analysis and causal modeling were employed to, respectively, cluster the gene/protein pairs, and reveal their cause–effect relations, aiming to assess the associated biological functions. To the best of our knowledge, this is the first time that causal modeling has been employed within the proteogenomics concept in plants. The analysis revealed the complex nature of causal relations among genes/proteins that are important for traits of interest in perennial fruit trees, particularly regarding the fruit softening and ripening process in sweet cherry. Causal discovery could be used to highlight persistent relations at the gene/protein level, stimulating biological interpretation and facilitating further study of the proteogenomic atlas in plants

A personalized stepwise dynamic predictive algorithm of the time to first treatment in chronic lymphocytic leukemia

Summary: Personalized prediction is ideal in chronic lymphocytic leukemia (CLL). Although refined models have been developed, stratifying patients in risk groups, it is required to accommodate time-dependent information of patients, to address the clinical heterogeneity observed within these groups. In this direction, this study proposes a personalized stepwise dynamic predictive algorithm (PSDPA) for the time-to-first-treatment of the individual patient. The PSDPA introduces a personalized Score, reflecting the evolution in the patient’s follow-up, employed to develop a reference pool of patients. Score evolution’s similarity is used to predict, at a selected time point, the time-to-first-treatment for a new patient. Additional patient’s biological information may be utilized. The algorithm was applied to 20 CLL patients, indicating that stricter assessment criteria for the Score evolution’s similarity, and biological similarity exploitation, may improve prediction. The PSDPA capitalizes on both the follow-up and the biological background of the individual patient, dynamically promoting personalized prediction in CLL

A wide foodomics approach coupled with metagenomics elucidates the environmental signature of potatoes

Summary: The term “terroir” has been widely employed to link differential geographic phenotypes with sensorial signatures of agricultural food products, influenced by agricultural practices, soil type, and climate. Nowadays, the geographical indications labeling has been developed to safeguard the quality of plant-derived food that is linked to a certain terroir and is generally considered as an indication of superior organoleptic properties. As the dynamics of agroecosystems are highly intricate, consisting of tangled networks of interactions between plants, microorganisms, and the surrounding environment, the recognition of the key molecular components of terroir fingerprinting remains a great challenge to protect both the origin and the safety of food commodities. Furthermore, the contribution of microbiome as a potential driver of the terroir signature has been underestimated. Herein, we present a first comprehensive view of the multi-omic landscape related to transcriptome, proteome, epigenome, and metagenome of the popular Protected Geographical Indication potatoes of Naxos