MeSH Ontology Research Articles

PGS-Depot: a comprehensive resource for polygenic scoresconstructed by summary statistics based methods.

Polygenic score (PGS) is an important tool for the genetic prediction of complex traits. However, there are currently no resources providing comprehensive PGSs computed from published summary statistics, and it is difficult to implement and run different PGS methods due to the complexity of their pipelines and parameter settings. To address these issues, we introduce a new resource called PGS-Depot containing the most comprehensive set of publicly available disease-related GWAS summary statistics. PGS-Depot includes 5585 high quality summary statistics (1933 quantitative and 3652 binary trait statistics) curated from 1564 traits in European and East Asian populations. A standardized best-practice pipeline is used to implement 11 summary statistics-based PGS methods, each with different model assumptions and estimation procedures. The prediction performance of each method can be compared for both in- and cross-ancestry populations, and users can also submit their own summary statistics to obtain custom PGS with the available methods. Other features include searching for PGSs by trait name, publication, cohort information, population, or the MeSH ontology treeand searching for trait descriptions with the experimental factor ontology (EFO). All scores, SNP effect sizesand summary statistics can be downloaded via FTP. PGS-Depot is freely available at http://www.pgsdepot.net.

HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey

BackgroundOntology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure.ResultsTo bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra’s algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure.ConclusionsWe introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.

Community Resilience in Belarus and the EU response

Community Resilience in Belarus and the EU response

Experience: Automated Prediction of Experimental Metadata from Scientific Publications

While there exists an abundance of open biomedical data, the lack of high-quality metadata makes it challenging for others to find relevant datasets and to reuse them for another purpose. In particular, metadata are useful to understand the nature and provenance of the data. A common approach to improving the quality of metadata relies on expensive human curation, which itself is time-consuming and also prone to error. Towards improving the quality of metadata, we use scientific publications to automatically predict metadata key:value pairs. For prediction, we use a Convolutional Neural Network (CNN) and a Bidirectional Long-short term memory network (BiLSTM). We focus our attention on the NCBI Disease Corpus, which is used for training the CNN and BiLSTM. We perform two different kinds of experiments with these two architectures: (1) we predict the disease names by using their unique ID in the MeSH ontology and (2) we use the tree structures of MeSH ontology to move up in the hierarchy of these disease terms, which reduces the number of labels. We also perform various multi-label classification techniques for the above-mentioned experiments. We find that in both cases CNN achieves the best results in predicting the superclasses for disease with an accuracy of 83%.

Medical Domain Document Classification via Extraction of Taxonomy Concepts from MeSH Ontology

Medical Domain Document Classification via Extraction of Taxonomy Concepts from MeSH Ontology

Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applications

Clustering of biomedical documents has become a vital research concept due to its importance in the clinical and telemedicine applications. The clustering of the medical documents is being considered as a major issue because of its unstructured nature. This paper focuses on developing an efficient document clustering approach for the medical documents to be utilized in telemedicine applications. Most existing models utilize n-gram techniques for phrase identification and term, concept or semantic based models for clustering applications. However n-gram does not perform well when the original document has been modified while only hybrid models provide relatively improved clustering. The proposed document clustering approach is named as enriched semantic smoothing model which has been developed on the concept of Mesh ontology. As the semantic smoothing model is not effective in handling the density of general words, an improved model with term frequency and inverse gravity moment (TF-IGM) factor and improved background elimination is used. Unlike term frequency and inverse document frequency), TF-IGM precisely measure the class distinguishing power of a term by making use of the fine-grained term distribution across different classes of text in documents. The modified n-gram technique, which detects the cases of substitution and deletion in the documents and averts them, improves the phrases identification. The clustering efficiency of the k-means clustering and hierarchical clustering algorithms is improved by utilizing the proposed model. The experiments are made on Mesh ontology based PubMed documents with similarity measures and cluster validity indexes used for comparisons. The results show that the proposed approach of medical document clustering is highly accurate and thus improves the concepts of clinical practices and telemedicine.

A Study on Some Tasks, Corpus and Resources of Medical Information Retrieval

Background/Objectives: This paper gives an overview of some tasks involved in the retrieval process, corpus and resources of medical information retrieval. Methods/Statistical Analysis: Inverted file representation method is used in the retrieval process for associating documents in the corpus with various search terms. Conventional statistical ranking functions such as Jaccard, Okapi and Euclidean have been widely used for ranking retrieved medical documents. An extractive informative generic mono-lingual single-document summarizer is used to produce medical domain-specific summary. Sentence ranking method is used to include most appropriate sentences in the final summary. Findings: Studies reveal that people are searching the web and read medical related information in order to be informed about their health. In the medical domain, richest and most used source of information is MEDLINE. Because of frequent use of acronyms in the medical literature, using the term that appears in documents as keywords for document indexing would not be effective. Also, using Bag of Words representation could not capture the semantic meaning of terms. Some domain-specific thesauri like UMLS, MeSH and Gene ontology are available for biomedical retrieval. These domain-specific thesauri can provide synonyms, hypernyms and hyponyms of a specific term but it does not look into the context. Therefore, the retrieval results of using domain-specific thesauri are somewhat conflicting. It is possible to identify which lexical variant of specific term should be used under specific context by using Wikipedia as resource for biomedical retrieval. Conventional ranking functions fail to capture the inherent features of natural language text. Evolutionary algorithm based ranking can enhance the retrieval performance. Any domain-specific summarizer must consider similarity between sentences as essential feature for summarization. Applications/Improvements: Improvements in retrieval results is achieved by using context-aware keywords as indexing keywords and highly robust hybrid evolutionary algorithm based ranking function for ordering the retrieved documents.

An Efficient Ontology Based Concept Indexing and Clustering for Biomedical Documents

Conventional Document clustering techniques aim to group the documents into different semantic classes based on the cluster hypothesis. Most of the existing techniques are based on either single term keyword with its frequency analysis or phrase based approach using n-gram techniques of the document. Accurate clustering is infeasible in document clustering because of the curse of dimensionality due to the high dimensionality space of it. For the successful clustering of text documents, a two step process is proposed in this paper. This proposed method involves with concept based indexing with the domain ontology as background knowledge for concept extraction and clustering of documents. The results of the proposed method is compared with the traditional indexing technique, Latent Semantic Indexing (LSI). In order to prove the efficiency of the proposed technique, biomedical domain is chosen with MeSH ontology. The experimental results show that the proposed method outperforms traditional term-base method and LSI.

Evaluating the effect of unbalanced data in biomedical document classification.

Nowadays, document classification has become an interesting research field. Partly, this is due to the increasing availability of biomedical information in digital form which is necessary to catalogue and organize. In this context, machine learning techniques are usually applied to text classification by using a general inductive process that automatically builds a text classifier from a set of pre-classified documents. Related with this domain, imbalanced data is a well-known problem in many practical applications of knowledge discovery and its effects on the performance of standard classifiers are remarkable. In this paper, we investigate the application of a Bayesian Network (BN) model for the triage of documents, which are represented by the association of different MeSH terms. Our results show that BNs are adequate for describing conditional independencies between MeSH terms and that MeSH ontology is a valuable resource for representing Medline documents at different abstraction levels. Moreover, we perform an extensive experimental evaluation to investigate if the classification of Medline documents using a BN classifier poses additional challenges when dealing with class-imbalanced prediction. The evaluation involves two methods, under-sampling and cost-sensitive learning. We conclude that BN classifier is sensitive to both balancing strategies and existing techniques can improve its overall performance.

PubMine: An Ontology-Based Text Mining System for Deducing Relationships among Biological Entities

Background: Published manuscripts are the main source of biological knowledge. Since the manual examination is almost impossible due to the huge volume of literature data (approximately 19 million abstracts in PubMed), intelligent text mining systems are of great utility for knowledge discovery. However, most of current text mining tools have limited applicability because of i) providing abstract-based search rather than sentence-based search, ii) improper use or lack of ontology terms, iii) the design to be used for specific subjects, or iv) slow response time that hampers web services and real time applications. Results: We introduce an advanced text mining system called PubMine that supports intelligent knowledge discovery based on diverse bio-ontologies. PubMine improves query accuracy and flexibility with advanced search capabilities of fuzzy search, wildcard search, proximity search, range search, and the Boolean combinations. Furthermore, PubMine allows users to extract multi-dimensional relationships between genes, diseases, and chemical compounds by using OLAP (On-Line Analytical Processing) techniques. The HUGO gene symbols and the MeSH ontology for diseases, chemical compounds, and anatomy have been included in the current version of PubMine, which is freely available at http://pubmine.kobic.re.kr. Conclusions: PubMine is a unique bio-text mining system that provides flexible searches and analysis of biological entity relationships. We believe that PubMine would serve as a key bioinformatics utility due to its rapid response to enable web services for community and to the flexibility to accommodate general ontology.

A knowledge-driven approach to biomedical document conceptualization

Biomedical document conceptualization is the process of clustering biomedical documents based on ontology-represented domain knowledge. The result of this process is the representation of the biomedical documents by a set of key concepts and their relationships. Most of clustering methods cluster documents based on invariant domain knowledge. The objective of this work is to develop an effective method to cluster biomedical documents based on various user-specified ontologies, so that users can exploit the concept structures of documents more effectively. We develop a flexible framework to allow users to specify the knowledge bases, in the form of ontologies. Based on the user-specified ontologies, we develop a key concept induction algorithm, which uses latent semantic analysis to identify key concepts and cluster documents. A corpus-related ontology generation algorithm is developed to generate the concept structures of documents. Based on two biomedical datasets, we evaluate the proposed method and five other clustering algorithms. The clustering results of the proposed method outperform the five other algorithms, in terms of key concept identification. With respect to the first biomedical dataset, our method has the F-measure values 0.7294 and 0.5294 based on the MeSH ontology and gene ontology (GO), respectively. With respect to the second biomedical dataset, our method has the F-measure values 0.6751 and 0.6746 based on the MeSH ontology and GO, respectively. Both results outperforms the five other algorithms in terms of F-measure. Based on the MeSH ontology and GO, the generated corpus-related ontologies show informative conceptual structures. The proposed method enables users to specify the domain knowledge to exploit the conceptual structures of biomedical document collections. In addition, the proposed method is able to extract the key concepts and cluster the documents with a relatively high precision.

Open Biomedical Ontology-based Medline exploration

BackgroundEffective Medline database exploration is critical for the understanding of high throughput experimental results and the development of novel hypotheses about the mechanisms underlying the targeted biological processes. While existing solutions enhance Medline exploration through different approaches such as document clustering, network presentations of underlying conceptual relationships and the mapping of search results to MeSH and Gene Ontology trees, we believe the use of multiple ontologies from the Open Biomedical Ontology can greatly help researchers to explore literature from different perspectives as well as to quickly locate the most relevant Medline records for further investigation.ResultsWe developed an ontology-based interactive Medline exploration solution called PubOnto to enable the interactive exploration and filtering of search results through the use of multiple ontologies from the OBO foundry. The PubOnto program is a rich internet application based on the FLEX platform. It contains a number of interactive tools, visualization capabilities, an open service architecture, and a customizable user interface. It is freely accessible at: .

Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes.

How do you choose your literature search tool(s)?

AbstractWith increasing number of bio-literature search engines, scientists and health professionals either make a subjective choice of tool(s) or face a challenge of analyzing multiple features of a plethora of bibliographic software. There is an urgent need for a thorough comparative analysis of the available literature scanning tools, from the user’s perspective. We report results of the first time semi-quantitative comparison of 21 search programs, which can search published (partial or full text) documents in life science areas. The observations can assist life science researchers and medical professionals to make an informed selection among the programs, depending on their search objectives. Some of the important findings are: 1. Most of the hits obtained from Scopus, ReleMed, EBImed, CiteXplore, and HighWire Press were usually relevant (i.e., these tools showed a better precision than other tools). 2. But a very high number of relevant citations were retrieved by HighWire Press, Google Scholar, CiteXplore and Pubmed Central (they had better recall). 3. HWP and CiteXplore seemed to have a good balance of precision and recall efficiencies. 4. PubMed Central, PubMed and Scopus provided the most useful query systems. 5. GoPubMed, BioAsk, EBIMed, ClusterMed could be more useful among the tools that can automatically process the retrieved citations for further scanning of bio-entities such as proteins, diseases, tissues, molecular interactions etc). The authors suggest the use of PubMed, Scopus, Google Scholar and HighWire Press - for better coverage, and GoPubMed - to view the hits categorized based on the MeSH and gene ontology terms.

A comparative analysis of 21 literature search engines

AbstractWith increasing number of bibliographic software, scientists and health professionals either make a subjective choice of tool(s) that could suit their needs or face a challenge of analyzing multiple features of a plethora of search programs. There is an urgent need for a thorough comparative analysis of the available bio-literature scanning tools, from the user’s perspective. We report results of the first time semi-quantitative comparison of 21 programs, which can search published (partial or full text) documents in life science areas. The observations can assist life science researchers and medical professionals to make an informed selection among the programs, depending on their search objectives. Some of the important findings are: 1. Most of the hits obtained from Scopus, ReleMed, EBImed, CiteXplore, and HighWire Press were usually relevant (i.e. these tools show a better precision than other tools). 2. But a very high number of relevant citations were retrieved by HighWire Press, Google Scholar, CiteXplore and Pubmed Central (they had better recall). 3. HWP and CiteXplore seemed to have a good balance of precision and recall efficiencies. 4. PubMed Central, PubMed and Scopus provided the most useful query systems. 5. GoPubMed, BioAsk, EBIMed, ClusterMed could be more useful among the tools that can automatically process the retrieved citations for further scanning of bio-entities such as proteins, diseases, tissues, molecular interactions, etc. The authors suggest the use of PubMed, Scopus, Google Scholar and HighWire Press - for better coverage, and GoPubMed - to view the hits categorized based on the MeSH and gene ontology terms. The article is relavant to all life science subjects.

Information Retrieval by Semantic Similarity

Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity, we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising performance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-the-art semantic similarity retrieval methods utilizing ontologies.

Combination of text-mining algorithms increases the performance

Recently, several information extraction systems have been developed to retrieve relevant information out of biomedical text. However, these methods represent individual efforts. In this paper, we show that by combining different algorithms and their outcome, the results improve significantly. For this reason, CONAN has been created, a system which combines different programs and their outcome. Its methods include tagging of gene/protein names, finding interaction and mutation data, tagging of biological concepts and linking to MeSH and Gene Ontology terms. In this paper, we will present data that show that combining different text-mining algorithms significantly improves the results. Not only is CONAN a full-scale approach that will ultimately cover all of PubMed/MEDLINE, we also show that this universality has no effect on quality: our system performs as well as or better than existing systems. The LDD corpus presented is available by request to the author. The system will be available shortly. For information and updates on CONAN please visit http://www.cs.uu.nl/people/rainer/conan.html.