Dataset Information

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features.

ABSTRACT: Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. "Full feature spectrum" knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross-validated using data from Wake Forest Baptist Medical Center's electronic medical records and annotated with BioCarta signaling signatures. The clinical potential of the discovered modules was exhibited by stratifying patients for drug responses. A series of discovered associations provided new insights into breast cancer, such as the effects of patient's cultural background on preferences for surgical procedure. We also discovered two groups of highly associated features, the HER2 and the ER modules, each of which described how phenotypes were associated with molecular signatures, diagnostic features, and clinical decisions. The discovered "ER module", which was dominated by cancer immunity, was used as an example for patient stratification and prediction of drug responses to tamoxifen and chemotherapy. BUFAM-derived RDN modeling demonstrated unique ability to discover clinically meaningful and actionable knowledge across highly heterogeneous biomedical big data sets.

SUBMITTER: Chen H

PROVIDER: S-EPMC4947904 | biostudies-other | 2016 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features.

Chen Huaidong H Chen Wei W Liu Chenglin C Zhang Le L Su Jing J Zhou Xiaobo X

Scientific reports 20160718

Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. "Full feature spectrum" knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross ...[more]

PMID: 27427091

Similar Datasets

Project description:BackgroundCircular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the regulation of various diseases and cancers. Determining the functions of circRNAs in mammalian cells is of great significance for revealing their mechanism of action in physiological and pathological processes, diagnosis and treatment of diseases. However, determining the functions of circRNAs on a large scale is a challenging task because of the high experimental costs.ResultsIn this paper, we present a hierarchical deep learning model, DeepciRGO, which can effectively predict gene ontology functions of circRNAs. We build a heterogeneous network containing circRNA co-expressions, protein-protein interactions and protein-circRNA interactions. The topology features of proteins and circRNAs are calculated using a novel representation learning approach HIN2Vec across the heterogeneous network. Then, a deep multi-label hierarchical classification model is trained with the topology features to predict the biological process function in the gene ontology for each circRNA. In particular, we manually curated a benchmark dataset containing 185 GO annotations for 62 circRNAs, namely, circRNA2GO-62. The DeepciRGO achieves promising performance on the circRNA2GO-62 dataset with a maximum F-measure of 0.412, a recall score of 0.400, and an accuracy of 0.425, which are significantly better than other state-of-the-art RNA function prediction methods. In addition, we demonstrate the considerable potential of integrating multiple interactions and association networks.ConclusionsDeepciRGO will be a useful tool for accurately annotating circRNAs. The experimental results show that integrating multi-source data can help to improve the predictive performance of DeepciRGO. Moreover, The model also can combine RNA structure and sequence information to further optimize predictive performance.

Dataset Information

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features.

Publications

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets