Cancer is a complex disease with diverse genetic changes that pose significant treatment challenges due to its heterogeneity. Identifying driver modules, which are crucial for cancer progression, has been aided by artificial intelligence (AI) techniques. However, existing approaches lack specificity, particularly for cancer types like lung cancer. This thesis addresses this gap by proposing a method that combines a gene-gene interaction network construction with AI-based clustering to identify distinct driver modules specific to lung cancer. The research aims to enhance our understanding of the disease by leveraging publicly available databases and large datasets using design science methodology. By mapping biological processes to genes and constructing a weighted gene-gene interaction network, correlations within gene clusters are identified. A clustering algorithm is applied to derive potential cancer-driver modules and pinpoint biologically relevant modules that contribute to the development of lung cancer. The results demonstrate the effectiveness and robustness of the clustering approach, with 110 unique and non-overlapping clusters identified, ranging in size from 4 to 10. These clusters surpass the evaluation requirements and exhibit significant relevance to critical pathways. The findings challenge previous assumptions about gene clusters and their significance in lung cancer, providing insights into the molecular underpinnings of the disease. The identified driver modules hold promise for influencing future approaches to diagnosis, prognosis, and treatment in the management of lung cancer. By expanding our understanding of the disease, this research paves the way for further investigations and potential clinical advancements.

Increasing numbers of alerts produced by network intrusion detection systems (NIDS) have burdened the job of security analysts especially in identifying and responding to them. The tasks of exploring and analysing large quantities of communication network security data are also difficult. This thesis studied the application of visualisation in combination with alerts classifier to make the exploring and understanding of network security alerts data faster and easier. The prototype software, NSAViz, has been developed to visualise and to provide an intuitive presentation of the network security alerts data using interactive 3D visuals with an integration of a false alert classifier. The needs analysis of this prototype was based on the suggested needs of network security analyst's tasks as seen in the literatures. The prototype software incorporates various projections of the alert data in 3D displays. The overview was plotted in a 3D plot named as "time series 3D AlertGraph" which was an extension of the 2D histographs into 3D. The 3D AlertGraph was effectively summarised the alerts data and gave the overview of the network security status. Filtering, drill-down and playback of the alerts at variable speed were incorporated to strengthen the analysis. Real-time visual observation was also included. To identify true alerts from all alerts represents the main task of the network security analyst. This prototype software was integrated with a false alert classifier using a classification tree based on C4.5 classification algorithm to classify the alerts into true and false. Users can add new samples and edit the existing classifier training sample. The classifier performance was measured using k-fold cross-validation technique. The results showed the classifier was able to remove noise in the visualisation, thus making the pattern of the true alerts to emerge. It also highlighted the true alerts in the visualisation. Finally, a user evaluation was conducted to find the usability problems in the tool and to measure its effectiveness. The feed backs showed the tools had successfully helped the task of the security analyst and increased the security awareness in their supervised network. From this research, the task of exploring and analysing a large amount of network security data becomes easier and the true attacks can be identified using the prototype visualisation tools. Visualisation techniques and false alert classification are helpful in exploring and analysing network security data.

Proteins in Escherichia coli were compared in terms of essentiality, centrality, and conservation. The hypotheses of this study are: for proteins in Escherichia coli, (1) there is a positive, measureable correlation between protein conservation and essentiality, (2) there is a positive relationship between conservation and degree centrality, and (3) essentiality and centrality also have a positive correlation. The third hypothesis was supported by a moderate correlation, the first with a weak correlation, and the second hypotheis was not supported. When proteins that did not map to orthologous groups and proteins that had no interactions were removed, the relationship between essentality and conservation increased to a strong relationship. This was due to the effect of proteins that did not map to orthologus groups and suggests that protein orthology represented by clusters of orthologus groups does not accurately dipict protein conservation among the species studied.

A metamorfose em insetos é um dos mais complexos e belos eventos biológicos conhecidos, dirigido por sucessivas alterações morfo-fisiológicas. Este intricado processo é coordenado por componentes moleculares como ecdisteroides (20E) e hormônio juvenil (HJ), fatores de transcrição e microRNAs (miRNAs). Os miRNAs regulam a expressão de genes-alvo, que por sua vez orquestram alterações fisiológicas e anatômicas necessárias para o completo desenvolvimento do organismo. Apesar do enorme esforço, os circuitos genéticos e endócrinos que regulam a metamorfose em insetos sociais, como a abelha Apis mellifera, estão longe de serem completamente esclarecidos. Os miRNAs são importantes componentes da maquinaria celular e parecem ser ubíquos no controle de processos biológicos. Desvendar novas interações miRNA-mRNAs alvo envolvidas com a metamorfose e a regulação das cascatas de 20E e HJ lançará uma luz sobre esse complexo evento. Em nosso estudo nós investigamos os papéis de miR-34, miR-281, miR-252a e miR-252b, conhecidos como reguladores da metamorfose em insetos, no modelo A. mellifera. Todos estes miRNAs revelaram alto grau de conservação filogenética, bem como responderam ao tratamento com 20E, sofrendo flutuações na abundância de transcritos. Usando as informações disponíveis e nossos bancos de dados, nós identificamos interações envolvendo estes miRNAs e genes participantes nas cascatas de HJ e 20E: ultraspiracle (Usp), fushi tarazu-transcription factor 1 (ftz-f1), ecdysone receptor (EcR), calponin (chd64), insulin receptor 2 (inr2), e Krüppel homolog 1 (Krh1). A predição das interações miRNA-mRNAs alvo revelou que os receptores de ecdisteroides EcR e Usp, bem como o fator de transcrição ftz-f1 são alvos importantes dos miRNAs estudados, apresentando sítios para os quatros miRNAs investigados. Observamos também que os seis genes codificadores de proteína são putativamente alvejados por miR-34. Por meio do ensaio da luciferase, pudemos validar as interações entre miR-34 e os alvos Kr-h1, chd64 e inr2; miR-252a e os alvos ftz-f1 e EcR; miR-252b e os alvos chd64 e ftz-f1; miR-281 e os alvos ftz-f1, EcR e Usp. A investigação dos perfis de expressão dos miRNAs ao longo do desenvolvimento larval (L3-PP3) e pupal (Pw), contrastados com os perfis de seus respectivos alvos, apontou muitos casos de relações positivas miRNA-mRNA. Estes resultados complementaram os resultados de validação, e expuseram a regulação exercida pelo miRNA sobre seus alvos. Juntos, os nossos resultados apontam para novas interações miRNA-mRNAs, envolvidas com a metamorfose em A. mellifera. As regulações por nós propostas e validadas bem como suas caracterizações e relações com os hormônios reguladores da metamorfose, são inéditas e acrescentam muito ao conhecimento sobre a regulação da metamorfose em A. mellifera. Nesse contexto, nossa pesquisa definitivamente contribui para uma melhor compreensão dos eventos moleculares envolvidos com a metamorfose de abelhas. / Insect metamorphosis is one of the most complex and beautiful of known biological events; it consists of successive morphological and physiological alterations. This intricate process is coordinated by various molecular components, including ecdysteroids (20E), juvenile hormone (JH), transcription factors and microRNAs (miRNAs). The miRNAs regulate gene expression, which in turn orchestrates physiological and anatomical changes necessary for successful insect ontogeny. Despite enormous efforts, the endocrine and genetic circuits that regulate metamorphosis in social insects, such as honey bees (Apis mellifera), are far from being completely elucidated. The miRNAs are a substantial component of this molecular machinery and seem to be ubiquitously involved in the control of biological processes. Disclosing new miRNA-target interactions involved in metamorphosis and in the regulation of 20E and JH cascades can shed light on these poorly understood events. In this study, we provide new pieces to this puzzle. We investigated the roles of miR-34, miR-281, miR-252a and miR-252b, known to be important regulators of insect metamorphosis, in the A. mellifera model. All of these miRNAs revealed a high degree of phylogenetic conservation and responded to treatment with 20E, which altered transcript abundance. Using available information and our databases, we identified interactions involving these miRNAs and the component genes of JH and 20E pathways: ultraspiracle (Usp), fushi tarazu-transcription factor 1 (ftz-f1), ecdysone receptor (EcR), calponin (chd64), insulin receptor 2 (inr2), and Krüppel homolog 1 (Kr-h1). Prediction of miRNA-target interactions revealed that the ecdysteroid receptors EcR and Usp and the transcription factor ftz-f1 are highly targeted by miRNAs involved in metamorphosis; they presented binding sites for all four miRNAs. We also observed that all six-protein coding genes are putatively targeted by miR-34. Using the luciferase assay, we were able to validate the interactions of miR-34 with the targets Krh1, chd64 and inr2; miR-252a with the targets ftz-f1 and EcR; miR-252b with the targets chd64 and ftz-f1; and miR-281 with the targets ftz-f1, EcR and Usp. Investigation of miRNA expression profiles during larval (L3-PP3) and pupal (Pw) development, as a function of the profiles of their respective targets, demonstrated many cases of positive miRNA-mRNA relationships. These results complemented the validation results, showing how the miRNAs regulate their targets. In conclusion, we identified various previously unknown miRNA-mRNA interactions involved in the metamorphosis of A. mellifera. The regulatory pathways proposed and validated by us, as well as their characterizations and relationships with metamorphosis regulator hormones, are unique and add to the understanding of the regulation of metamorphosis in A. mellifera. In this context, our research contributes to a better understanding of the molecular events involved in honey bee metamorphosis.

Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational miRNA target pr ediction algorithm. We compute several graph theoretic measures on human PPI network as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. We used clas- sification algorithms for that assessment. Since there exists only miRNA targets as training samples, this problem becomes a One Class Classification (OCC) problem. We devised a novel OCC method, DiVo, based on simple distance metrics and voting. Comparative analysis with the state of the art methods show that, DiVo attains better classification performance. Our eventual results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, netwo rk context correlates better with being a target compared to a sequence based score provided by the prediction tool.

Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational miRNA target prediction algorithm. We compute several graph theoretic measures on human PPI network as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. We used clas- sification algorithms for that assessment. Since there exists only miRNA targets as training samples, this problem becomes a One Class Classification (OCC) problem. We devised a novel OCC method, DiVo, based on simple distance metrics and voting. Comparative analysis with the state of the art methods show that, DiVo attains better classification performance. Our eventual results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, network context correlates better with being a target compared to a sequence based score provided by the prediction tool.

Τα τελευταία χρόνια κυριαρχεί στο χώρο της γενωμικής έρευνας η τεχνολογία των μικροσυστοιχιών, η οποία επέτρεψε την ποσοτική μέτρηση της έκφρασης χιλιάδων γονιδίων ταυτόχρονα. Παρόλο που τα δεδομένα έκφρασης των γονιδίων μπορεί να εμπεριέχουν θόρυβο και να μην είναι πλήρως αντικειμενικά, εντούτοις περιγράφουν την έκφραση όλου του γονιδιώματος ενός οργανισμού, κάτι το οποίο δεν ήταν εφικτό τις προηγούμενες δεκαετίες. Επίσης ένα άλλο είδος δεδομένων που συνέβαλλε δραστικά στην κατανόηση των δυναμικών διεργασιών του κυττάρου ήταν τα δεδομένα πρωτεϊνικών αλληλεπιδράσεων (πρωτεΐνη-πρωτεΐνη). Μεγάλης κλίμακας τεχνικές όπως το διυβριδικό σύστημα του σακχαρομύκητα και η φασματομετρία μάζας καθαρισμένων πρωτεϊνικών συμπλόκων παρήγαγαν μεγάλη ποσότητα πληροφορίας για τις σχέσεις μεταξύ των γονιδιακών προϊόντων. Επίσης και αυτό το είδος δεδομένων χαρακτηρίζεται από πολλές αναληθείς αλληλεπιδράσεις και στην εργασία αυτή χρησιμοποιούνται οι πιο έγκυρες από αυτές. Ταυτόχρονα ξεκίνησε μια προσπάθεια να περιγραφούν οι δυναμικές διεργασίες του κυττάρου μέσα από βιολογικά δίκτυα π.χ. γονιδιακά, πρωτεϊνικά, μεταβολικά κτλ. Ακόμα μεγαλύτερη πρόκληση είναι η εύρεση υποδικτύων με βιολογικά διακριτό ρόλο, τα οποία ονομάζονται λειτουργικές υπο-δομές. Η ανίχνευση τέτοιων υπο-δομών θα συντελέσει στην κατανόηση των σχέσεων μεταξύ των γονιδίων ή των προϊόντων τους αλλά και στην επισήμανση γονιδίων ή πρωτεϊνών που δεν έχουν χαρακτηριστεί ακόμα. Στην εργασία αυτή τέλος περιγράφονται τρόποι ομαδοποίησης των δεδομένων γονιδιακής έκφρασης, αναλύονται διεξοδικά τα δίκτυα αλληλεπίδρασης πρωτεϊνών και παρουσιάζονται τρόποι ομαδοποίησης αυτών. Επίσης προτείνεται ενοποίηση των παραπάνω δεδομένων στον οργανισμό Saccharomyces cerevisiae με σκοπό την ανίχνευση λειτουργικών υπο-δομών στον πρωτεϊνικό του γράφο. Επιπρόσθετα, η ανίχνευση αυτών των υπο-δομών υλοποιήθηκε με έναν νέο αλγόριθμο, τον Detect Module from Seed Protein (DMSP), ο οποίος δεν διαμερίζει το γράφο σε ομάδες όπως οι κλασικοί τρόποι ομαδοποίησης αλλά χτίζει υπο-δομές ξεκινώντας από μια πρωτεΐνη-«σπόρο». / -

A metamorfose em insetos é um dos mais complexos e belos eventos biológicos conhecidos, dirigido por sucessivas alterações morfo-fisiológicas. Este intricado processo é coordenado por componentes moleculares como ecdisteroides (20E) e hormônio juvenil (HJ), fatores de transcrição e microRNAs (miRNAs). Os miRNAs regulam a expressão de genes-alvo, que por sua vez orquestram alterações fisiológicas e anatômicas necessárias para o completo desenvolvimento do organismo. Apesar do enorme esforço, os circuitos genéticos e endócrinos que regulam a metamorfose em insetos sociais, como a abelha Apis mellifera, estão longe de serem completamente esclarecidos. Os miRNAs são importantes componentes da maquinaria celular e parecem ser ubíquos no controle de processos biológicos. Desvendar novas interações miRNA-mRNAs alvo envolvidas com a metamorfose e a regulação das cascatas de 20E e HJ lançará uma luz sobre esse complexo evento. Em nosso estudo nós investigamos os papéis de miR-34, miR-281, miR-252a e miR-252b, conhecidos como reguladores da metamorfose em insetos, no modelo A. mellifera. Todos estes miRNAs revelaram alto grau de conservação filogenética, bem como responderam ao tratamento com 20E, sofrendo flutuações na abundância de transcritos. Usando as informações disponíveis e nossos bancos de dados, nós identificamos interações envolvendo estes miRNAs e genes participantes nas cascatas de HJ e 20E: ultraspiracle (Usp), fushi tarazu-transcription factor 1 (ftz-f1), ecdysone receptor (EcR), calponin (chd64), insulin receptor 2 (inr2), e Krüppel homolog 1 (Krh1). A predição das interações miRNA-mRNAs alvo revelou que os receptores de ecdisteroides EcR e Usp, bem como o fator de transcrição ftz-f1 são alvos importantes dos miRNAs estudados, apresentando sítios para os quatros miRNAs investigados. Observamos também que os seis genes codificadores de proteína são putativamente alvejados por miR-34. Por meio do ensaio da luciferase, pudemos validar as interações entre miR-34 e os alvos Kr-h1, chd64 e inr2; miR-252a e os alvos ftz-f1 e EcR; miR-252b e os alvos chd64 e ftz-f1; miR-281 e os alvos ftz-f1, EcR e Usp. A investigação dos perfis de expressão dos miRNAs ao longo do desenvolvimento larval (L3-PP3) e pupal (Pw), contrastados com os perfis de seus respectivos alvos, apontou muitos casos de relações positivas miRNA-mRNA. Estes resultados complementaram os resultados de validação, e expuseram a regulação exercida pelo miRNA sobre seus alvos. Juntos, os nossos resultados apontam para novas interações miRNA-mRNAs, envolvidas com a metamorfose em A. mellifera. As regulações por nós propostas e validadas bem como suas caracterizações e relações com os hormônios reguladores da metamorfose, são inéditas e acrescentam muito ao conhecimento sobre a regulação da metamorfose em A. mellifera. Nesse contexto, nossa pesquisa definitivamente contribui para uma melhor compreensão dos eventos moleculares envolvidos com a metamorfose de abelhas. / Insect metamorphosis is one of the most complex and beautiful of known biological events; it consists of successive morphological and physiological alterations. This intricate process is coordinated by various molecular components, including ecdysteroids (20E), juvenile hormone (JH), transcription factors and microRNAs (miRNAs). The miRNAs regulate gene expression, which in turn orchestrates physiological and anatomical changes necessary for successful insect ontogeny. Despite enormous efforts, the endocrine and genetic circuits that regulate metamorphosis in social insects, such as honey bees (Apis mellifera), are far from being completely elucidated. The miRNAs are a substantial component of this molecular machinery and seem to be ubiquitously involved in the control of biological processes. Disclosing new miRNA-target interactions involved in metamorphosis and in the regulation of 20E and JH cascades can shed light on these poorly understood events. In this study, we provide new pieces to this puzzle. We investigated the roles of miR-34, miR-281, miR-252a and miR-252b, known to be important regulators of insect metamorphosis, in the A. mellifera model. All of these miRNAs revealed a high degree of phylogenetic conservation and responded to treatment with 20E, which altered transcript abundance. Using available information and our databases, we identified interactions involving these miRNAs and the component genes of JH and 20E pathways: ultraspiracle (Usp), fushi tarazu-transcription factor 1 (ftz-f1), ecdysone receptor (EcR), calponin (chd64), insulin receptor 2 (inr2), and Krüppel homolog 1 (Kr-h1). Prediction of miRNA-target interactions revealed that the ecdysteroid receptors EcR and Usp and the transcription factor ftz-f1 are highly targeted by miRNAs involved in metamorphosis; they presented binding sites for all four miRNAs. We also observed that all six-protein coding genes are putatively targeted by miR-34. Using the luciferase assay, we were able to validate the interactions of miR-34 with the targets Krh1, chd64 and inr2; miR-252a with the targets ftz-f1 and EcR; miR-252b with the targets chd64 and ftz-f1; and miR-281 with the targets ftz-f1, EcR and Usp. Investigation of miRNA expression profiles during larval (L3-PP3) and pupal (Pw) development, as a function of the profiles of their respective targets, demonstrated many cases of positive miRNA-mRNA relationships. These results complemented the validation results, showing how the miRNAs regulate their targets. In conclusion, we identified various previously unknown miRNA-mRNA interactions involved in the metamorphosis of A. mellifera. The regulatory pathways proposed and validated by us, as well as their characterizations and relationships with metamorphosis regulator hormones, are unique and add to the understanding of the regulation of metamorphosis in A. mellifera. In this context, our research contributes to a better understanding of the molecular events involved in honey bee metamorphosis.

Au cours du traitement thermique rapide (RTP) des cellules photovoltaïques à couches minces, un suivi du profil de température souhaité et une homogénéité de la chauffe substrat doivent être assurés. Le but de cette thèse est de proposer une méthodologie de conception d'un four RTP permettant d'atteindre la qualité du cycle de la chauffe souhaitée.Une modélisation thermique est réalisée en se basant sur la méthode de réseaux de composants afin de prédire le comportement thermique dynamique du four. L'approximation des flux plans et l'approximation des couches minces semi-transparentes sont utilisées pour le calcul des facteurs d'échanges directs. L'algorithme des revêtements est appliqué pour en déduire les facteurs de transfert. Le modèle thermique développé est validé expérimentalement sur un four de petites dimensions. Une méthodologie de conception du four RTP est proposée en tenant compte de l'aspect dynamique des conditions thermiques du four. Une optimisation par algorithme génétique est effectuée pour trouver l'emplacement des émetteurs. Pour chacune des configurations testées, la distribution de la puissance aux émetteurs à fournir à chaque instant est optimisée par programmation dynamique. Finalement, cette méthodologie est appliquée pour la conception d'un four RTP pour le traitement de cellules photovoltaïques à couches minces de 30 × 60 cm2. Les résultats des essais confirment la validité de la méthodologie proposée. / During the rapid thermal processing (RTP) of thin film photovoltaic cells, the temperature of the latter has to follow a preset time evolution profile, while keeping spatial uniformity of the wafer. The aim of this study is to propose a design methodology of RTP furnace in order to obtain the quality of the required heating cycle.A thermal modeling is performed based on the component interaction network approach to predict the thermal behavior of the furnace. Flux plane approximation and semi-transparent thin layer approximation are used to calculate the direct exchange factor. The plating algorithm is then applied to calculate the transfer factor. The thermal model developed is validated experimentally on a furnace of small dimensions. A methodology to design a RTP furnace is proposed taking into account the dynamic aspect of the thermal conditions of the furnace. An optimization using the genetic algorithm is performed in order to find emitter dispositions. For each tested configuration, the optimal input power distribution over the emitters at each time step is found by using real time dynamic programming. Finally, the methodology is applied for the design of RTP furnace for the heat treatment of thin film photovoltaic cells of 30 × 60 cm2. Test results confirm the validity of the methodology proposed.

<div> <div> <div> <p>Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanisms of diseases. This dissertation introduces the computational method to predict PPIs. In the first chapter, the history of identifying protein interactions and some experimental methods are introduced. Because interacting proteins share similar functions, protein function similarity can be used as a feature to predict PPIs. NaviGO server is developed for biologists and bioinformaticians to visualize the gene ontology relationship and quantify their similarity scores. Furthermore, the computational features used to predict PPIs are summarized. This will help researchers from the computational field to understand the rationale of extracting biological features and also benefit the researcher with a biology background to understand the computational work. After understanding various computational features, the computational prediction method to identify large-scale PPIs was developed and applied to Arabidopsis, maize, and soybean in a whole-genomic scale. Novel predicted PPIs were provided and were grouped based on prediction confidence level, which can be used as a testable hypothesis to guide biologists’ experiments. Since affinity chromatography combined with mass spectrometry technique introduces high false PPIs, the computational method was combined with mass spectrometry data to aid the identification of high confident PPIs in large-scale. Lastly, some remaining challenges of the computational PPI prediction methods and future works are discussed. </p> </div> </div> </div>