Die Untersuchung des Phänotyps bringt z.B. bei genetischen Krankheiten ein Verständnis der zugrunde liegenden Mechanismen mit sich. Aufgrund dessen wurden neue Technologien wie RNA-Interferenz (RNAi) entwickelt, die Genfunktionen entschlüsseln und mehr phänotypische Daten erzeugen. Interpretation der Ergebnisse solcher Versuche ist insbesondere bei heterogenen Daten eine große Herausforderung. Wenige Ansätze haben bisher Daten über die direkte Verknüpfung von Genotyp und Phänotyp hinaus interpretiert. Diese Dissertation zeigt neue Methoden, die Entdeckungen in Phänotypen über Spezies und Methodik hinweg ermöglichen. Es erfolgt eine Erfassung der verfügbaren Datenbanken und der Ansätze zur Analyse ihres Inhalts. Die Grenzen und Hürden, die noch bewältigt werden müssen, z.B. fehlende Datenintegration, lückenhafte Ontologien und der Mangel an Methoden zur Datenanalyse, werden diskutiert. Der Ansatz zur Integration von Genotyp- und Phänotypdaten, PhenomicDB 2, wird präsentiert. Diese Datenbank assoziiert Gene mit Phänotypen durch Orthologie über Spezies hinweg. Im Fokus sind die Integration von RNAi-Daten und die Einbindung von Ontologien für Phänotypen, Experimentiermethoden und Zelllinien. Ferner wird eine Studie präsentiert, in der Phänotypendaten aus PhenomicDB genutzt werden, um Genfunktionen vorherzusagen. Dazu werden Gene aufgrund ihrer Phänotypen mit Textclustering gruppiert. Die Gruppen zeigen hohe biologische Kohärenz, da sich viele gemeinsame Annotationen aus der Gen-Ontologie und viele Protein-Protein-Interaktionen innerhalb der Gruppen finden, was zur Vorhersage von Genfunktionen durch Übertragung von Annotationen von gut annotierten Genen zu Genen mit weniger Annotationen genutzt wird. Zuletzt wird der Prototyp PhenoMIX präsentiert, in dem Genotypen und Phänotypen mit geclusterten Phänotypen, PPi, Orthologien und weiteren Ähnlichkeitsmaßen integriert und deren Gruppierungen zur Vorhersage von Genfunktionen, sowie von phänotypischen Wörtern genutzt. / In diseases with a genetic component, examination of the phenotype can aid understanding the underlying genetics. Technologies to generate high-throughput phenotypes, such as RNA interference (RNAi), have been developed to decipher functions for genes. This large-scale characterization of genes strongly increases phenotypic information. It is a challenge to interpret results of such functional screens, especially with heterogeneous data sets. Thus, there have been only few efforts to make use of phenotype data beyond the single genotype-phenotype relationship. Here, methods are presented for knowledge discovery in phenotypes across species and screening methods. The available databases and various approaches to analyzing their content are reviewed, including a discussion of hurdles to be overcome, e.g. lack of data integration, inadequate ontologies and shortage of analytical tools. PhenomicDB 2 is an approach to integrate genotype and phenotype data on a large scale, using orthologies for cross-species phenotypes. The focus lies on the uptake of quantitative and descriptive RNAi data and ontologies of phenotypes, assays and cell-lines. Then, the results of a study are presented in which the large set of phenotype data from PhenomicDB is taken to predict gene annotations. Text clustering is utilized to group genes based on their phenotype descriptions. It is shown that these clusters correlate well with indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. The clusters are then used to predict gene function by carrying over annotations from well-annotated genes to less well-characterized genes. Finally, the prototype PhenoMIX is presented, integrating genotype and phenotype data with clustered phenotypes, orthologies, interaction data and other similarity measures. Data grouped by these measures are evaluated for theirnpredictiveness in gene functions and phenotype terms.

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Whole-exome and genome sequencing are widely used to diagnose individual patients. However, despite its success, this approach leaves many patients undiagnosed. This could be due to the need to discover more disease genes and variants or because disease phenotypes are novel and arise from a combination of variants of multiple known genes related to the disease. Recent rapid increases in available genomic, biomedical, and phenotypic data enable computational analyses, reducing the search space for disease-causing genes or variants and facilitating the prediction of causal variants. Therefore, artificial intelligence, data mining, machine learning, and deep learning are essential tools that have been used to identify biological interactions, including protein-protein interactions, gene-disease predictions, and variant--disease associations. Predicting these biological associations is a critical step in diagnosing patients with rare or complex diseases. In recent years, computational methods have emerged to improve gene-disease prioritization by incorporating phenotype information. These methods evaluate a patient's phenotype against a database of gene-phenotype associations to identify the closest match. However, inadequate knowledge of phenotypes linked with specific genes in humans and model organisms limits the effectiveness of the prediction. Information about gene product functions and anatomical locations of gene expression is accessible for many genes and can be associated with phenotypes through ontologies and machine-learning models. Incorporating this information can enhance gene-disease prioritization methods and more accurately identify potential disease-causing genes. This dissertation aims to address key limitations in gene-disease prediction and variant prioritization by developing computational methods that systematically relate human phenotypes that arise as a consequence of the loss or change of gene function to gene functions and anatomical and cellular locations of activity. To achieve this objective, this work focuses on crucial problems in the causative variant prioritization pipeline and presents novel computational methods that significantly improve prediction performance by leveraging large background knowledge data and integrating multiple techniques. Therefore, this dissertation presents novel approaches that utilize graph-based machine-learning techniques to leverage biomedical ontologies and linked biological data as background knowledge graphs. The methods employ representation learning with knowledge graphs and introduce generic models that address computational problems in gene-disease associations and variant prioritization. I demonstrate that my approach is capable of compensating for incomplete information in public databases and efficiently integrating with other biomedical data for similar prediction tasks. Moreover, my methods outperform other relevant approaches that rely on manually crafted features and laborious pre-processing. I systematically evaluate our methods and illustrate their potential applications for data analytics in biomedicine. Finally, I demonstrate how our prediction tools can be used in the clinic to assist geneticists in decision-making. In summary, this dissertation contributes to the development of more effective methods for predicting disease-causing variants and advancing precision medicine.