Background. Fibrosis is a highly dynamic process caused by prolonged injury, deregulation of the normal processes of wound healing, and extensive deposition of extracellular matrix (ECM) proteins. During fibrosis process, multiple genes interact with environmental factors. Over recent decades, tons of fibrosis-related genes have been identified to shed light on the particular clinical manifestations of this complex process. However, the genetics information about fibrosis is dispersed in lots of extensive literature. Methods. We extracted data from literature abstracts in PubMed by text mining, and manually curated the literature and identified the evidence sentences. Results. We presented FibroAtlas, which included 1,439 well-annotated fibrosis-associated genes. FibroAtlas 1.0 is the first attempt to build a nonredundant and comprehensive catalog of fibrosis-related genes with supporting evidence derived from curated published literature and allows us to have an overview of human fibrosis-related genes.

Background Osteoporosis is a common, complex disease of bone with a strong heritable component, characterized by low bone mineral density, microarchitectural deterioration of bone tissue and an increased risk of fracture. Due to limited drug selection for osteoporosis and increasing morbidity, mortality of osteoporotic fractures, osteoporosis has become a major health burden in aging societies. Current researches for identifying specific loci or genes involved in osteoporosis contribute to a greater understanding of the pathogenesis of osteoporosis and the development of better diagnosis, prevention and treatment strategies. However, little is known about how most causal genes work and interact to influence osteoporosis. Therefore, it is greatly significant to collect and analyze the studies involved in osteoporosis-related genes. Unfortunately, the information about all these osteoporosis-related genes is scattered in a large amount of extensive literature. Currently, there is no specialized database for easily accessing relevant information about osteoporosis-related genes and miRNAs. Methods We extracted data from literature abstracts in PubMed by text-mining and manual curation. Moreover, a local MySQL database containing all the data was developed with PHP on a Windows server. Results OsteoporosAtlas (http://biokb.ncpsb.org/osteoporosis/), the first specialized database for easily accessing relevant information such as osteoporosis-related genes and miRNAs, was constructed and served for researchers. OsteoporosAtlas enables users to retrieve, browse and download osteoporosis-related genes and miRNAs. Gene ontology and pathway analyses were integrated into OsteoporosAtlas. It currently includes 617 human encoding genes, 131 human non-coding miRNAs, and 128 functional roles. We think that OsteoporosAtlas will be an important bioinformatics resource to facilitate a better understanding of the pathogenesis of osteoporosis and developing better diagnosis, prevention and treatment strategies.

The rapid production of high-throughput cancer omics data provides valuable data resources for revealing the pathogenesis, prognosis prediction and treatment strategies of cancers. However, the huge data scale brings great challenges to data analysis. Therefore, we applied the representation learning method to the joint analysis of biomedical network and omics data. According to the protein expression profile of patients with early-stage hepatocellular carcinoma, 15 dimensional embedding vectors of 101 samples were obtained. Unsupervised learning was then used to cluster the embedded vectors of the samples, and we found that the clustering of the embedded vectors of the samples was consistent with the clustering of the original data. Therefore, the spatial distribution of embedded vectors can maintain the similarity of samples. New pan-cancer subtypes were obtained by joint embedding the expression profile of pan-cancer proteomic and pathway network data. Nine hunded and forty four proteins such as KIF2C, AURKA, ATP1B1, BDH1 and C6ORF106 were found to be significantly related to these subtypes, and 143 biological pathways or processes such as p53 signaling pathway, nucleotide synthesis, immune diseases, metabolism, cholesterol synthesis and transportation were found to be significantly related to these subtypes. These results show that the representation learning system developed can realize the seamless connection between the omics data and the pathway network. Our method is expected to help mine the biological knowledge contained in the omics data and provide a new perspective for further explanation of the molecular mechanism.