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An analysis of disease-gene relationship from Medline abstracts by DigSee.

ABSTRACT: Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational modification. Many studies of text mining attempted to identify the relationship between gene and disease by mining the literature, but they did not consider the biological events in which genes show abnormal behaviour in response to diseases. In this study, we propose to identify disease-related genes that are involved in the development of disease through biological events from Medline abstracts. We identified associations between 13,054 genes and 4,494 disease types, which cover more disease-related genes than manually curated databases for all disease types (e.g., Online Mendelian Inheritance in Man) and also than those for specific diseases (e.g., Alzheimer's disease and hypertension). We show that the text mining findings are reliable, as per the PubMed scale, in that the disease-disease relationships inferred from the literature-wide findings are similar to those inferred from manually curated databases in a well-known study. In addition, literature-wide distribution of biological events across disease types reveals different characteristics of disease types.

SUBMITTER: Kim J

PROVIDER: S-EPMC5215527 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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An analysis of disease-gene relationship from Medline abstracts by DigSee.

Kim Jeongkyun J Kim Jung-Jae JJ Lee Hyunju H

Scientific reports 20170105

Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational modification. Many studies of text mining attempted to identify the relationship between gene and disease by mining the literature, but they did not consider the biological events in which genes show abnormal behaviour in response to diseases. In this study, we propose to identify disease-related genes that are involved in the develo ...[more]

PMID: 28054646

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Project description:BACKGROUND: Time delays are important factors that are often neglected in gene regulatory network (GRN) inference models. Validating time delays from knowledge bases is a challenge since the vast majority of biological databases do not record temporal information of gene regulations. Biological knowledge and facts on gene regulations are typically extracted from bio-literature with specialized methods that depend on the regulation task. In this paper, we mine evidences for time delays related to the transcriptional regulation of yeast from the PubMed abstracts. RESULTS: Since the vast majority of abstracts lack quantitative time information, we can only collect qualitative evidences of time delays. Specifically, the speed-up or delay in transcriptional regulation rate can provide evidences for time delays (shorter or longer) in GRN. Thus, we focus on deriving events related to rate changes in transcriptional regulation. A corpus of yeast regulation related abstracts was manually labeled with such events. In order to capture these events automatically, we create an ontology of sub-processes that are likely to result in transcription rate changes by combining textual patterns and biological knowledge. We also propose effective feature extraction methods based on the created ontology to identify the direct evidences with specific details of these events. Our ontologies outperform existing state-of-the-art gene regulation ontologies in the automatic rule learning method applied to our corpus. The proposed deterministic ontology rule-based method can achieve comparable performance to the automatic rule learning method based on decision trees. This demonstrates the effectiveness of our ontology in identifying rate-changing events. We also tested the effectiveness of the proposed feature mining methods on detecting direct evidence of events. Experimental results show that the machine learning method on these features achieves an F1-score of 71.43%. CONCLUSIONS: The manually labeled corpus of events relating to rate changes in transcriptional regulation for yeast is available in https://sites.google.com/site/wentingntu/data. The created ontologies summarized both biological causes of rate changes in transcriptional regulation and corresponding positive and negative textual patterns from the corpus. They are demonstrated to be effective in identifying rate-changing events, which shows the benefits of combining textual patterns and biological knowledge on extracting complex biological events.

Project description:BackgroundA huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner.ResultsWe propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between diseases, drugs and genes in Semantic MEDLINE using a domain pattern driven strategy. A Resource Description Framework (RDF)-linked data approach is used to re-organize the data to increase the flexibility of data integration, the interoperability within domain ontologies, and the efficiency of data storage. Unique associations among drugs, diseases, and genes are extracted for downstream network-based analysis. The second step is to apply a network-based approach to mine the local network structure of this heterogeneous network. Significant network motifs are then identified as the backbone of the network. A simplified network based on those significant motifs is then constructed to facilitate discovery. We implemented our computational framework and identified five network motifs, each of which corresponds to specific biological meanings. Three case studies demonstrate that novel associations are derived from the network topology analysis of reconstructed networks of significant network motifs, further validated by expert knowledge and functional enrichment analyses.ConclusionsWe have developed a novel network-based computational approach to investigate the heterogeneous drug-gene-disease network extracted from Semantic MEDLINE. We demonstrate the power of this approach by prioritizing candidate disease genes, inferring potential disease relationships, and proposing novel drug targets, within the context of the entire knowledge. The results indicate that such approach will facilitate the formulization of novel research hypotheses, which is critical for translational medicine research and personalized medicine.

Project description:BackgroundFor years, Uniform Resource Locator (URL) decay or "link rot" has been a growing concern in the field of biomedical sciences. This paper addresses this issue by examining the status of the URLs published in MEDLINE abstracts, establishing current availability and estimating URL decay in these records from 1994 to 2006. We also reviewed the information provided by the URL to determine if the context that the author cited in writing the paper is the same information presently available in the URL. Lastly, with all the documented recommended methods to preserve URL links, we determined which among them have gained acceptance among authors and publishers.MethodsMEDLINE records from 1994 to 2006 from the National Library of Medicine in Extensible Mark-up Language (XML) format were processed yielding 10,208 URL addresses. These were accessed once daily at random times for 30 days. Titles and abstracts were also searched for the presence of archival tools such as WebCite, Persistent URL (PURL) and Digital Object Identifier (DOI).ResultsResults showed that the average URL length ranged from 13 to 425 characters with a mean length of 35 characters [Standard Deviation (SD) = 13.51; 95% confidence interval (CI) 13.25 to 13.77]. The most common top-level domains were ".org" and ".edu", each with 34%. About 81% of the URL pool was available 90% to 100% of the time, but only 78% of these contained the actual information mentioned in the MEDLINE record. "Dead" URLs constituted 16% of the total. Finally, a survey of archival tool usage showed that since its introduction in 1998, only 519 of all abstracts reviewed had incorporated DOI addresses in their MEDLINE abstracts.ConclusionURL persistence parallels previous studies which showed approximately 81% general availability during the 1-month study period. As peer-reviewed literature remains to be the main source of information in biomedicine, we need to ensure the accuracy and preservation of these links.

Dataset Information

An analysis of disease-gene relationship from Medline abstracts by DigSee.

Publications

An analysis of disease-gene relationship from Medline abstracts by DigSee.

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