The investigation of correlations between radiomic and genomic profiling in breast cancer (BC) molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment. We present a well-designed radiogenomic framework—image-gene-gene set (IMAGGS), which detects multi-way associations in BC subtypes by integrating radiomic and genomic features.Our dataset consists of 721 patients, each of whom has 12 ultrasound (US) images captured from different angles and gene mutation data. To better characterize tumor traits, 12 multi-angle US images are fused using two distinct strategies. Then, we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm, deviating from the prevalent one-to-one relationship pattern observed in previous studies. Key radiomic and genomic features are screened using these associations. In addition, gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes. We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases. Taken together, IMAGGS provides a comprehensive characterization for diseases by associating imaging, genes, and gene sets, paving the way for biological interpretation of radiomics and development of targeted therapy.

Polycystic ovary syndrome (PCOS) is a highly familial and heritable endocrine disorder. Over half of the daughters born to women with PCOS may eventually develop their own PCOS-related symptoms. Progress in the treatment of PCOS is currently hindered by the complexity of its clinical manifestations and incomplete knowledge of its etiopathogenesis. Various animal models, including experimentally-induced, naturally-occurring, and spontaneously-arising ones, have been established to emulate a wide range of phenotypical and pathological traits of human PCOS. These studies have led to a paradigm shift for understanding the genetic,developmental, and evolutionary origins of this disorder. Furthermore, emerging evidence suggests that animal models are useful in evaluating state-of-the-art drugs and treatments for PCOS. This review aims to provide a comprehensive summary of recent studies of PCOS in animal models, highlighting the power of these disease models in understanding the biology of PCOS and aiding high-throughput approaches.