Empirical study using network of semantically related associations in bridging the knowledge gap (original) (raw)
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ARIANA: Adaptive robust and integrative analysis for finding novel associations
We have developed an integrated system, called ARIANA (Adaptive Robust and Integrative Analysis for finding Novel Associations). It is an efficient and scalable knowledge discovery tool designed to provide a range of services in the general areas of text analytics in biomedicine. It integrates literature mining and ontology mapping to find network of semantically related entities. The source for the literature data is PubMed and the ontology is from the MeSH database. Empirical studies were performed to evaluate the performance of ARIANA. Based on subjective and objective measures of evaluation ARIANA was able to discover knowledge relevant to the query.
ASSOCIATION MINING FROM BIOMEDICAL TEXT WITH NETWORK ANALYSIS
Discovery of genes that are responsible for various diseases, becomes an important task. Since the genes are related with many diseases, the gene-disease association should be discovered. To obtain this gene-disease association from available biomedical literature, the relation type between the gene and disease is extracted from the biomedical literature. So, this becomes more and more important to deal with the extraction problem from the biomedical texts in an automatic way. Then the gene-disease association is visualized by network construction and association score matrix is constructed to calculate the gene-disease association score. The gene-disease relation type is identified and then the association score is calculated by integrating disease similarity network and protein-protein interaction network. The candidate genes for the particular disease and the novel genes for various diseases can also be found by calculating the association score and visualizing the dataset network.
Biomolecules
Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format.
2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013
A huge amount of association relationships among biological entities (e.g., diseases, drugs, and genes) are scattered in biomedical literature. How to extract and analyze such heterogeneous data still remains a challenging task for most researchers in the biomedical field. Natural language processing (NLP) has the potential in extracting associations among biological entities from literature. However, association information extracted through NLP can be large, noisy, and redundant which poses significant challenges to biomedical researchers to use such information. To address this challenge, we propose a computational framework to facilitate the use of NLP results. We apply Latent Dirichlet Allocation (LDA) to discover topics based on associations. The networks extracted from each topic provide a disease-specific network for downstream bioinformatics analysis of associations for each topic. We illustrated the framework through the construction of disease-specific networks from Semantic MEDLINE, an NLP-generated association database, followed by the analysis of network properties, such as hub nodes and degree distribution. The results demonstrate that (1) LDA-based approach can group related diseases into the same disease topic; (2) the disease-specific association network follows the scale-free network property, in which hub nodes are enriched in related diseases, genes and drugs.
BMC Bioinformatics, 2015
Background: Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. Results: By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. Conclusions: BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information.
Content-rich biological network constructed by mining PubMed abstracts
The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public.
2007
The promises of the post-genome era disease-related discoveries and advances have yet to be fully realized, with many opportunities for discovery hiding in the millions of biomedical papers published since. Public databases give access to data extracted from the literature by teams of experts, but their coverage is often limited and lags behind recent discoveries. We present a computational method that combines data extracted from the literature with data from curated sources in order to uncover possible gene-disease relationships that are not directly stated or were missed by the initial mining. Method: An initial set of genes and proteins is obtained from gene-disease relationships extracted from PubMed abstracts using natural language processing. Interactions involving the corresponding proteins are similarly extracted and integrated with interactions from curated databases (such as BIND and DIP), assigning a confidence measure to each interaction depending on its source. The augmented list of genes and gene products is then ranked combining two scores: one that reflects the strength of the relationship with the initial set of genes and incorporates user-defined weights and another that reflects the importance of the gene in maintaining the connectivity of the network. We applied the method to atherosclerosis to assess its effectiveness. Results: Top-ranked proteins from the method are related to atherosclerosis with accuracy between 0.85 to 1.00 for the top 20 and 0.64 to 0.80 for the top 90 if duplicates are ignored, with 45% of the top 20 and 75% of the top 90 derived by the method, not extracted from text. Thus, though the initial gene set and interactions were automatically extracted from text (and subject to the impreciseness of automatic extraction), their use for further hypothesis generation is valuable given adequate computational analysis.
PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries
BMC Bioinformatics, 2011
Background: Biological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBI's PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation. Results: To help the concept-oriented exploration of such data we developed PESCADOR, a web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. We illustrate its use in exploring protein aggregation in neurodegenerative disease and in the expansion of pathways associated to colon cancer.
2008
Motivation: Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining diseaserelated genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network. Results: The proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study.
Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases
PLoS Computational Biology, 2010
The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also wellsuited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.