Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining

Automated extraction of potential migraine biomarkers using a semantic graph

PROBLEM

Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. However, searching these enormous knowledge reservoirs and integrating findings across heterogeneous sources is costly and difficult. Here we demonstrate how semantically integrated knowledge, extracted from biomedical literature and structured databases, can be used to automatically identify potential migraine biomarkers.

METHOD

We used a knowledge graph containing more than 3.5 million biomedical concepts and 68.4 million relationships. Biochemical compound concepts were filtered and ranked by their potential as biomarkers based on their connections to a subgraph of migraine-related concepts. The ranked results were evaluated against the results of a systematic literature review that was performed manually by migraine researchers. Weight points were assigned to these reference compounds to indicate their relative importance.

RESULTS

Ranked results automatically generated by the knowledge graph were highly consistent with results from the manual literature review. Out of 222 reference compounds, 163 (73%) ranked in the top 2000, with 547 out of the 644 (85%) weight points assigned to the reference compounds. For reference compounds that were not in the top of the list, an extensive error analysis has been performed. When evaluating the overall performance, we obtained a ROC-AUC of 0.974.

DISCUSSION

Semantic knowledge graphs composed of information integrated from multiple and varying sources can assist researchers in identifying potential disease biomarkers.

Relationship Between Glucocorticoids and Insulin Resistance in Healthy Individuals

Background

The aim of this study was to determine the correlation between glucocorticoids (GCs) and insulin resistance (IR) in healthy individuals by conducting a systematic meta-analysis.

Material/Methods

A systematic literature review was conducted using 9 electronic databases. Only case-control studies investigating fasting plasma glucose (FPG) and IR were enrolled based on strictly established selection criteria. Statistical analyses were performed by Stata software, version 12.0 (Stata Corporation, College Station, Texas, USA).

Results

Among 496 initially retrieved articles, only 6 papers published in English were eventually included in this meta-analysis. A total of 201 healthy individuals (105 in GC group and 96 in control group) were included in the 6 studies. In 4 of these 6 studies, dexamethasone was used, and in the other 2 studies prednisolone was given. This meta-analysis revealed that the FPG, fasting insulin (FINS), and homeostasis model assessment of insulin resistance (HOMA-IR) levels in the GC group were all significantly higher than that in the control group (FPG: SMD=2.65, 95%CI=1.42~3.88, P<0.001; FINS: SMD=2.48, 95%CI=1.01~3.95, P=0.001; HOMA-IR: SMD=38.30, 95%CI=24.38~52.22, P<0.001).

Conclusions

In conclusion, our present study revealed that therapies using GCs might result in elevated FPG, FINS, and HOMA-IR, and thereby contribute to IR in healthy individuals.

Application of text mining in the biomedical domain

In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for.

Network analysis: a new approach to study endocrine disorders

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.