# A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning
## 1 Introduction  
Ovarian cancer is the most lethal gynecologic malignancy, with a five-year survival rate of 49% for all stages of the cancer [1]. Ovarian cancer is very aggressive and often recurs after subsequent treatments. Most patients will acquire resistance through treatment consisting of carboplatin-based chemotherapy as well as PARP inhibitors [2; 3]. Ovarian cancer is frequently diagnosed at advanced FIGO stages, which leads to overall poor survival rates. The symptoms are generally non-specific, and therefore early detection methods, genetic screening, and multiple treatment options are needed to improve the outcomes of patients with ovarian cancer [4; 5]. Recent studies have demonstrated that biological parameters like mRNA gene expression can be linked to and predict the outcome of cancer patients [6; 7; 8]. For that matter, statistical methods have been used, as well as machine learning methods [9]. Studies have shown that machine learning models achieve high performance in predicting potential biomarkers, the stage of cancer, platinum sensitivity, relapse time, as well as overall survival (OS) in ovarian cancer using gene expression profiles, image data, copy number variations, and more [10; 11; 12; 13; 14]. In a previous study by [PERSON] et al. [15], the SPLASH algorithm was used for the initial discovery of candidate biomarkers from mRNA gene expression analysis and weighted voting and k nearest neighbor for training and leave-one-out cross-validation for predictive accuracy. In another study by [PERSON] et al. [11], a supervised machine-learning approach using the support vector machine algorithm was applied to mRNA gene expression to identify biomarkers associated with early recurrences in ovarian cancer. [PERSON] et al. [16] used microRNA expression data and differential gene expression analysis between a complete response and noncomplete response to identify miRNAs differentially associated with clinical response to chemotherapy. In previous studies by the Cancer Genome Atlas Research Network [17], a univariate Cox regression model was used to identify 193-genes signature predictive of overall survival in high-grade serous ovarian cancer, and this model was refined to 100-genes signature in subsequent studies [18] using ssGSEA scores of tumor samples from multiple independent gene expression data sets and a multiple covariate model that includes molecular subtypes. [PERSON] et al. [12] used the Cox proportional hazards regression adjusted for molecular subtype from six microarray gene expression studies, totaling 1455 participants, and identified 200 candidate genes. They included additional 313 genes based on the literature and published data, and candidate gene expression was determined by Nanostring in 4071 samples from 16 studies and selected elastic net regularized regression model from evaluated four models for further training and validation to arrive at 101-genes prognostic signature. Finally, in [PERSON] et al. [19] studies, a network-based Cox proportional hazard model was applied to three independent data sets to identify biomarkers associated with patient outcomes in ovarian cancer. In this study, we use differential gene expression analysis to identify initial biomarkers associated with outcome and apply SHAP (Shapley additive explanations) algorithm for feature extraction, which has so far been used only once in ovarian cancer data for platinum sensitivity and to our knowledge, not on outcome prediction [20; 21]. We analyze one of the largest publicly available gene expression data sets for ovarian cancer to predict the outcome of patients with ovarian cancer and find candidate biomarkers associated with differential outcomes or responses to chemotherapy. Figure 1 provides an overview of the structure of the approach. A combination of bioinformatics analysis and machine learning methods is used to identify relationships between biological components, disease progression, and patient outcomes. Figure 1: The schematic shows the overall project structure.