Transfer Learning for Gene Prioritization Gene prioritization (gene ranking) plays an critical role in cancer diagnosis and therapy, and current algorithms rank genes by exploiting the pathogenic (cancercausing) genes and interactions among genes. However, these algorithms are criticized for the low accuracy when the number of pathogenic genes is limited. To overcome this problem, we propose the Transfer Learning based algorithm for Gene Prioritization without pathogenic genes in target cancer (called TLGP) under the assumption that knowledge shared by similar cancers can improve the accuracy of gene prioritization. Specifically, we first calculate the affinity matrix between target domain and source domain, which quantifies the similarity of genes among various cancers. Then, the TLGP algorithm automatically learns a fusion network in target cancer via fusing affinity matrix, pathogenic genes and genomic data of source cancers. Finally, we prioritize genes in target cancer by node ranking algorithms. The experimental results demonstrate that the accuracy of TLGP is at least 6.5% higher than that of stateoftheart approaches.

Breast Cancer Classification using Deep Transfer Learning on Structured Healthcare Data Efforts to improve early identification of aggressive high grade breast cancers, which pose the greatest risk to patient health if not detected early, are hindered by the rarity of these events. To address this problem, we proposed an accurate and efficient deep transfer learning method to handle the imbalanced data problem that is prominent in breast cancer data. In contrast to existing approaches based primarily on large image databases, we focused on structured data, which has not been commonly used for deep transfer learning. We used a number of publicly available breast cancer data sets to generate a pretrained model and transfer learned concepts to predict high grade malignant tumors in patients diagnosed with breast cancer at Mayo Clinic. We compared our results with stateoftheart techniques for addressing the problem of imbalanced learning and confirmed the superiority of the proposed method. To further demonstrate the ability of the proposed method to handle different degrees of class imbalance, a series of experiments were performed on publicly available breast cancer data under simulated class imbalanced settings. Based on the experimental results, we concluded that the proposed deep transfer learning on structured data can be used as an efficient method to handle imbalanced class problems in clinical research.