An inference method from multi-layered structure of biomedical data

• Published in: BMC medical informatics and decision making 2017-05, Vol.17 (Suppl 1), p.52-52, Article 52
• Main Authors: Kim, Myungjun, Nam, Yonghyun, Shin, Hyunjung
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Aversion learning; Bioinformatics; Biological activity; Biological Science Disciplines; Biomedical data; Cancer; Comorbidity; Computational Biology - methods; Diagnosis; Disease; Disease co-occurrence prediction; Drugs; Gene expression; Genomes; Humans; Inference; Integrative inference on biomedical data; Machine learning; Metabolism; Methods; Multilayers; Novelty; Propagation; Proteins; Proteomes; Semi-supervised learning; Semi-supervised learning for multiple networks; Serial learning; Supervised Machine Learning; Symptom-disease multi-layered network
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-017-0450-4

Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels. To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required. In this study, we develop a semi-supervised learning algorithm that can be applied to the multi-layered complex system. In order to verify the validity of the inference, it was applied to the prediction problem of disease co-occurrence with a two-layered network composed of symptom-layer and disease-layer. The symptom-disease layered network obtained a fairly high value of AUC, 0.74, which is regarded as noticeable improvement when comparing 0.59 AUC of single-layered disease network. If further stretched to whole layered structure of omics, the proposed method is expected to produce more promising results. This research has novelty in that it is a new integrative algorithm that incorporates the vertical structure of omics data, on contrary to other existing methods that integrate the data in parallel fashion. The results can provide enhanced guideline for disease co-occurrence prediction, thereby serve as a valuable tool for inference process of multi-layered biological system.