Background: Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis. Methods: Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models. Results: The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum–paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved. Conclusions: In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.

Rare diseases represent a major unmet medical need, yet the development of therapeutics for these diseases is limited by small patient cohorts, fragmented data, and uncertain disease mechanisms. Recent advances that combine patient-derived induced pluripotent stem cell (iPSC) models with multi-omics profiling and artificial intelligence offer a robust approach for phenotype definition and drug target identification that requires only minimal patient material. By integrating transcriptomic, proteomic, and metabolomic data, machine learning can produce disease-relevant molecular signatures, identify convergent pathway drivers, and prioritize druggable targets despite limited sample sizes. This short review highlights emerging in vitro models and AI-enabled workflows, discusses their strengths and limitations, and outlines practical strategies to accelerate drug discovery for rare diseases.