Kumulative Habilitationsschrift: Bioinformatics approaches to dissect transcriptional regulation in development and disease
Bioinformatics has become an integral part of biomedicine, both in research and diagnostics. Indeed, bioinformatics approaches leveraging statistical and machine learning methodologies are necessary for the interpretation of omics data. This habilitation work focused on the development and use of bioinformatics approaches for the analysis of functional genomics data to dissect transcriptional regulation and support stratification in disease. The regulation of geneexpression is the core process governing both the development and adult homeostasis of all organisms and its misregulation is linked to a wide range of disorders. In this work, the choice of methodologies as well as the development of novel methods were application-oriented and motivated by the specific biological questions to be answered. In one study, bioinformatics analyses of targeted expression data in zero-time kidney biopsy samples revealed sample subclusters to be further investigated; in another work, integrative bioinformatics analyses, including geneco-expression networks, were employed to characterize the role of EDC3 in mRNA decay and its association with intellectual disability. As transcriptional regulation is the result of the complex interplay of several cellular players, data from different modalities were integrated to dissect the modes of action of the transcription factor ZEB1, a key stimulator of invasion, metastasis, and therapy resistance in cancer. In addition, a novel framework for meta-analysis of enriched pathways from transcriptomics datasets was proposed and utilized to analyze publicly available physiological and pathological cardiac hypertrophy datasets in order to provide a temporal signature of heart tissue response to hemodynamic overload and to identify differences between the two types of hypertrophy. Finally, an expression-based classifier was proposed to support the routine pathological diagnosis of small blue round cell sarcomas, characterized by chromosomal rearrangements leading to gene fusions. Leveraging on our recent work on artificial intelligence-based approaches for digital pathology, our future research aim is to develop approaches to combine omics with imaging as well as clinical data to support clinical decision making.
Preisträgerin PD Dr. Fulvia Ferrazzi mit FAU Präsident Prof. Hornegger; Foto: FAU/Iannicelli
Kumulative Habilitationsschrift: Bioinformatics approaches to dissect transcriptional regulation in development and disease
Bioinformatics has become an integral part of biomedicine, both in research and diagnostics. Indeed, bioinformatics approaches leveraging statistical and machine learning methodologies are necessary for the interpretation of omics data. This habilitation work focused on the development and use of bioinformatics approaches for the analysis of functional genomics data to dissect transcriptional regulation and support stratification in disease. The regulation of geneexpression is the core process governing both the development and adult homeostasis of all organisms and its misregulation is linked to a wide range of disorders. In this work, the choice of methodologies as well as the development of novel methods were application-oriented and motivated by the specific biological questions to be answered. In one study, bioinformatics analyses of targeted expression data in zero-time kidney biopsy samples revealed sample subclusters to be further investigated; in another work, integrative bioinformatics analyses, including geneco-expression networks, were employed to characterize the role of EDC3 in mRNA decay and its association with intellectual disability. As transcriptional regulation is the result of the complex interplay of several cellular players, data from different modalities were integrated to dissect the modes of action of the transcription factor ZEB1, a key stimulator of invasion, metastasis, and therapy resistance in cancer. In addition, a novel framework for meta-analysis of enriched pathways from transcriptomics datasets was proposed and utilized to analyze publicly available physiological and pathological cardiac hypertrophy datasets in order to provide a temporal signature of heart tissue response to hemodynamic overload and to identify differences between the two types of hypertrophy. Finally, an expression-based classifier was proposed to support the routine pathological diagnosis of small blue round cell sarcomas, characterized by chromosomal rearrangements leading to gene fusions. Leveraging on our recent work on artificial intelligence-based approaches for digital pathology, our future research aim is to develop approaches to combine omics with imaging as well as clinical data to support clinical decision making.
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Vita
zur Vita von PD Dr. Fulvia Ferrazzi
alle FAU Awards 2025