Interpretable Data Science Methods for Knowledge Discovery from Ovarian Cancer Data

Tesis Doctoral le?da en la Universidad Rey Juan Carlos de Madrid en 2021. Directores de la Tesis: Sergio Mu?oz Romero y Jos? Luis Rojo ?lvarez ; Background. Ovarian cancer (OC) is the second most common gynecological malignancy, which represents the gynecological tumor with the worst prognosis and the fth most common cause of cancer-related death. This situation is due, in part, to the advanced stage at presentation in most patients. Early detection is a di cult task in OC because of poor speci c signs and symptoms at early stages, and lack of reliable screening techniques. Thus, biomarkers, and more speci cally those ones based on omics data, have a great potential in this detection task at early stages. Modern data science techniques, such as big data and deep learning, are helping to properly interpret clinical and omics data by determining associations with the occurrence of diseases, with a given prognosis, or even with a certain response to a de ned therapeutic intervention, thus being used in the discovery of new biomarkers. In this regard, data interpretability analyses should require special attention. These analyses are intended to understand the data, to nd basic patterns in them, and to obtain inferences from the most representative patterns. However, these types of analyses are frequently obviated in machine learning and deep learning, focused mostly on the accuracy, which could result in missing relevant information for the practitioner or expert in the application eld. Objectives. The general objectives proposed in this doctoral thesis are as follows: (1) to study existing and new data interpretability analysis methods with the intention of understanding the data, nding patterns in them, and trying to obtain inferences due to the underlying patterns observed in the data; and (2) to nd relationships of clinical and genetic factors in patients of OC with regard to the disease progression, using for it data interpretability analysis methods with clinical and genomic data collected from patients ...