Understanding and Using the Medical Subject Headings (MeSH) Vocabulary to Perform Literature Searches

Comorbidity Scoring with Causal Disease Networks

In recent years, there has been numerous studies constructing a disease network with diverse sources of data. Many researchers attempted to extend the usage of the disease network by employing machine learning algorithms on various problems such as prediction of comorbidity. The relations between diseases can further be specified into causal relations. When causality is laid on the edges in the network, prediction for comorbid diseases can be more improved. However, not many machine learning algorithms have been developed to concern causality. In this study, we exploit a network based machine learning algorithm that generates comorbidity scores from a causal disease network. In order to find comorbid diseases, semi-supervised scoring for causal networks is proposed. It computes scores of entire nodes in the network when a specific node is labeled. Each score is calculated one at a time and affects to the others along causal edges. The algorithm iterates until it converges. We compared the scoring results of the causal disease network and those of simple association network. As a gold standard, we referenced the values of relative risk from prevalence database, HuDiNe. Scoring by the proposed method provides clearer distinguishability between the top-ranked diseases in the comorbidity list. This is a benefit because it allows the choosing of the most significant ones on an easier fashion. To present typical use of the resulting list, comorbid diseases of Huntington disease and pnuemonia are validated via PubMed literature, respectively.

Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach

BACKGROUND

Blood chemicals are routinely measured in clinical or preclinical research studies to diagnose diseases, assess risks in epidemiological research, or use metabolomic phenotyping in response to treatments. A vast volume of blood-related literature is available via the PubMed database for data mining.

OBJECTIVES

We aimed to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulating system through text mining and database fusion.

METHODS

Using NCBI resources, we retrieved PubMed abstracts, PubChem chemical synonyms, and PMC supplementary tables. We then employed text mining and PubChem crowdsourcing to associate phrases relating to blood with PubChem chemicals. False positives were removed by a phrase pattern and a compound exclusion list.

RESULTS

A query to identify blood-related publications in the PubMed database yielded 1.1 million papers. Matching a total of 15 million synonyms from 6.5 million relevant PubChem chemicals against all blood-related publications yielded 37,514 chemicals and 851,999 publications records. Mapping PubChem compound identifiers to the PubMed database yielded 49,940 unique chemicals linked to 676,643 papers. Analysis of open-access metabolomics papers related to blood phrases in the PMC database yielded 4,039 unique compounds and 204 papers. Consolidating these three approaches summed up to a total of 41,474 achiral structures that were linked to 65,957 PubChem CIDs and to over 878,966 PubMed articles. We mapped these compounds to 50 databases such as those covering metabolites and pathways, governmental and toxicological databases, pharmacology resources, and bioassay repositories. In comparison, HMDB, the Human Metabolome Database, links 1,075 compounds to blood-related primary publications.

CONCLUSION

This new Blood Exposome Database can be used for prioritizing chemicals for systematic reviews, developing target assays in exposome research, identifying compounds in untargeted mass spectrometry, and biological interpretation in metabolomics data. The database is available at http://bloodexposome.org. https://doi.org/10.1289/EHP4713.

Nursing Home Facility-Initiated Involuntary Discharge

Involuntary discharge of nursing home (NH) residents is a prominent reason for NH complaints in the United States, but little is known about facility-initiated involuntary discharge (FID). A literature review was conducted to improve understanding of FID. The findings distinguish between six types of FID, identify populations at risk of FID, and differentiate between legal and unlawful FID practices and processes. The findings also characterize common FID destinations; show how policy, regulatory, and financial factors impact FID; and indicate that FID outcomes are commonly detrimental to the health and well-being of NH residents. Findings highlight challenges with understanding FID, including differentiating legal from unlawful FID. Although more research about NH FID is needed, the current study indicates that FID outcomes are regularly adverse, protections against unlawful FID are needed for Medicaid beneficiaries and NH residents with dementia, and stronger enforcement of existing policies and regulations regarding NH FID-including NH bed-hold requirements-are needed. [Journal of Gerontological Nursing, 45(8), 23-31.].

Disease gene prediction for molecularly uncharacterized diseases

Network medicine approaches have been largely successful at increasing our knowledge of molecularly characterized diseases. Given a set of disease genes associated with a disease, neighbourhood-based methods and random walkers exploit the interactome allowing the prediction of further genes for that disease. In general, however, diseases with no known molecular basis constitute a challenge. Here we present a novel network approach to prioritize gene-disease associations that is able to also predict genes for diseases with no known molecular basis. Our method, which we have called Cardigan (ChARting DIsease Gene AssociatioNs), uses semi-supervised learning and exploits a measure of similarity between disease phenotypes. We evaluated its performance at predicting genes for both molecularly characterized and uncharacterized diseases in OMIM, using both weighted and binary interactomes, and compared it with state-of-the-art methods. Our tests, which use datasets collected at different points in time to replicate the dynamics of the disease gene discovery process, prove that Cardigan is able to accurately predict disease genes for molecularly uncharacterized diseases. Additionally, standard leave-one-out cross validation tests show how our approach outperforms state-of-the-art methods at predicting genes for molecularly characterized diseases by 14%-65%. Cardigan can also be used for disease module prediction, where it outperforms state-of-the-art methods by 87%-299%.

DRUM: Inference of Disease-Associated m6A RNA Methylation Sites From a Multi-Layer Heterogeneous Network

Recent studies have revealed that the RNA N 6-methyladenosine (m6A) modification plays a critical role in a variety of biological processes and associated with multiple diseases including cancers. Till this day, transcriptome-wide m6A RNA methylation sites have been identified by high-throughput sequencing technique combined with computational methods, and the information is publicly available in a few bioinformatics databases; however, the association between individual m6A sites and various diseases are still largely unknown. There are yet computational approaches developed for investigating potential association between individual m6A sites and diseases, which represents a major challenge in the epitranscriptome analysis. Thus, to infer the disease-related m6A sites, we implemented a novel multi-layer heterogeneous network-based approach, which incorporates the associations among diseases, genes and m6A RNA methylation sites from gene expression, RNA methylation and disease similarities data with the Random Walk with Restart (RWR) algorithm. To evaluate the performance of the proposed approach, a ten-fold cross validation is performed, in which our approach achieved a reasonable good performance (overall AUC: 0.827, average AUC 0.867), higher than a hypergeometric test-based approach (overall AUC: 0.7333 and average AUC: 0.723) and a random predictor (overall AUC: 0.550 and average AUC: 0.486). Additionally, we show that a number of predicted cancer-associated m6A sites are supported by existing literatures, suggesting that the proposed approach can effectively uncover the underlying epitranscriptome circuits of disease mechanisms. An online database DRUM, which stands for disease-associated ribonucleic acid methylation, was built to support the query of disease-associated RNA m6A methylation sites, and is freely available at: www.xjtlu.edu.cn/biologicalsciences/drum.

Association of Cannabis Use in Adolescence and Risk of Depression, Anxiety, and Suicidality in Young Adulthood: A Systematic Review and Meta-analysis

Importance

Cannabis is the most commonly used drug of abuse by adolescents in the world. While the impact of adolescent cannabis use on the development of psychosis has been investigated in depth, little is known about the impact of cannabis use on mood and suicidality in young adulthood.

Objective

To provide a summary estimate of the extent to which cannabis use during adolescence is associated with the risk of developing subsequent major depression, anxiety, and suicidal behavior.

Data Sources

Medline, Embase, CINAHL, PsycInfo, and Proquest Dissertations and Theses were searched from inception to January 2017.

Study Selection

Longitudinal and prospective studies, assessing cannabis use in adolescents younger than 18 years (at least 1 assessment point) and then ascertaining development of depression in young adulthood (age 18 to 32 years) were selected, and odds ratios (OR) adjusted for the presence of baseline depression and/or anxiety and/or suicidality were extracted.

Data Extraction and Synthesis

Study quality was assessed using the Research Triangle Institute item bank on risk of bias and precision of observational studies. Two reviewers conducted all review stages independently. Selected data were pooled using random-effects meta-analysis.

Main Outcomes and Measures

The studies assessing cannabis use and depression at different points from adolescence to young adulthood and reporting the corresponding OR were included. In the studies selected, depression was diagnosed according to the third or fourth editions of Diagnostic and Statistical Manual of Mental Disorders or by using scales with predetermined cutoff points.

Results

After screening 3142 articles, 269 articles were selected for full-text review, 35 were selected for further review, and 11 studies comprising 23 317 individuals were included in the quantitative analysis. The OR of developing depression for cannabis users in young adulthood compared with nonusers was 1.37 (95% CI, 1.16-1.62; I2 = 0%). The pooled OR for anxiety was not statistically significant: 1.18 (95% CI, 0.84-1.67; I2 = 42%). The pooled OR for suicidal ideation was 1.50 (95% CI, 1.11-2.03; I2 = 0%), and for suicidal attempt was 3.46 (95% CI, 1.53-7.84, I2 = 61.3%).

Conclusions and Relevance

Although individual-level risk remains moderate to low and results from this study should be confirmed in future adequately powered prospective studies, the high prevalence of adolescents consuming cannabis generates a large number of young people who could develop depression and suicidality attributable to cannabis. This is an important public health problem and concern, which should be properly addressed by health care policy.

Conserved Disease Modules Extracted From Multilayer Heterogeneous Disease and Gene Networks for Understanding Disease Mechanisms and Predicting Disease Treatments

Disease relationship studies for understanding the pathogenesis of complex diseases, diagnosis, prognosis, and drug development are important. Traditional approaches consider one type of disease data or aggregating multiple types of disease data into a single network, which results in important temporal- or context-related information loss and may distort the actual organization. Therefore, it is necessary to apply multilayer network model to consider multiple types of relationships between diseases and the important interplays between different relationships. Further, modules extracted from multilayer networks are smaller and have more overlap that better capture the actual organization. Here, we constructed a weighted four-layer disease-disease similarity network to characterize the associations at different levels between diseases. Then, a tensor-based computational framework was used to extract Conserved Disease Modules (CDMs) from the four-layer disease network. After filtering, nine significant CDMs were reserved. The statistical significance test proved the significance of the nine CDMs. Comparing with modules got from four single layer networks, CMDs are smaller, better represent the actual relationships, and contain potential disease-disease relationships. KEGG pathways enrichment analysis and literature mining further contributed to confirm that these CDMs are highly reliable. Furthermore, the CDMs can be applied to predict potential drugs for diseases. The molecular docking techniques were used to provide the direct evidence for drugs to treat related disease. Taking Rheumatoid Arthritis (RA) as a case, we found its three potential drugs Carvedilol, Metoprolol, and Ramipril. And many studies have pointed out that Carvedilol and Ramipril have an effect on RA. Overall, the CMDs extracted from multilayer networks provide us with an impressive understanding disease mechanisms from the perspective of multi-layer network and also provide an effective way to predict potential drugs for diseases based on its neighbors in a same CDM.

FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association

BACKGROUND

In the process of post-transcription, microRNAs (miRNAs) are closely related to various complex human diseases. Traditional verification methods for miRNA-disease associations take a lot of time and expense, so it is especially important to design computational methods for detecting potential associations. Considering the restrictions of previous computational methods for predicting potential miRNAs-disease associations, we develop the model of FKL-Spa-LapRLS (Fast Kernel Learning Sparse kernel Laplacian Regularized Least Squares) to break through the limitations.

RESULT

First, we extract three miRNA similarity kernels and three disease similarity kernels. Then, we combine these kernels into a single kernel through the Fast Kernel Learning (FKL) model, and use sparse kernel (Spa) to eliminate noise in the integrated similarity kernel. Finally, we find the associations via Laplacian Regularized Least Squares (LapRLS). Based on three evaluation methods, global and local leave-one-out cross validation (LOOCV), and 5-fold cross validation, the AUCs of our method achieve 0.9563, 0.8398 and 0.9535, thus it can be seen that our method is reliable. Then, we use case studies of eight neoplasms to further analyze the performance of our method. We find that most of the predicted miRNA-disease associations are confirmed by previous traditional experiments, and some important miRNAs should be paid more attention, which uncover more associations of various neoplasms than other miRNAs.

CONCLUSIONS

Our proposed model can reveal miRNA-disease associations and improve the accuracy of correlation prediction for various diseases. Our method can be also easily extended with more similarity kernels.

Disentangling the evolution of MEDLINE bibliographic database: A complex network perspective

Scientific knowledge constitutes a complex system that has recently been the topic of in-depth analysis. Empirical evidence reveals that little is known about the dynamic aspects of human knowledge. Precise dissection of the expansion of scientific knowledge could help us to better understand the evolutionary dynamics of science. In this paper, we analyzed the dynamic properties and growth principles of the MEDLINE bibliographic database using network analysis methodology. The basic assumption of this work is that the scientific evolution of the life sciences can be represented as a list of co-occurrences of MeSH descriptors that are linked to MEDLINE citations. The MEDLINE database was summarized as a complex system, consisting of nodes and edges, where the nodes refer to knowledge concepts and the edges symbolize corresponding relations. We performed an extensive statistical evaluation based on more than 25 million citations in the MEDLINE database, from 1966 until 2014. We based our analysis on node and community level in order to track temporal evolution in the network. The degree distribution of the network follows a stretched exponential distribution which prevents the creation of large hubs. Results showed that the appearance of new MeSH terms does not also imply new connections. The majority of new connections among nodes results from old MeSH descriptors. We suggest a wiring mechanism based on the theory of structural holes, according to which a novel scientific discovery is established when a connection is built among two or more previously disconnected parts of scientific knowledge. Overall, we extracted 142 different evolving communities. It is evident that new communities are constantly born, live for some time, and then die. We also provide a Web-based application that helps characterize and understand the content of extracted communities. This study clearly shows that the evolution of MEDLINE knowledge correlates with the network's structural and temporal characteristics.

MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association

Identifying accurate associations between miRNAs and diseases is beneficial for diagnosis and treatment of human diseases. It is especially important to develop an efficient method to detect the association between miRNA and disease. Traditional experimental method has high precision, but its process is complicated and time-consuming. Various computational methods have been developed to uncover potential associations based on an assumption that similar miRNAs are always related to similar diseases. In this paper, we propose an accurate method, MDA-SKF, to uncover potential miRNA-disease associations. We first extract three miRNA similarity kernels (miRNA functional similarity, miRNA sequence similarity, Hamming profile similarity for miRNA) and three disease similarity kernels (disease semantic similarity, disease functional similarity, Hamming profile similarity for disease) in two subspaces, respectively. Then, due to limitations that some initial information may be lost in the process and some noises may be exist in integrated similarity kernel, we propose a novel Similarity Kernel Fusion (SKF) method to integrate multiple similarity kernels. Finally, we utilize the Laplacian Regularized Least Squares (LapRLS) method on the integrated kernel to find potential associations. MDA-SKF is evaluated by three evaluation methods, including global leave-one-out cross validation (LOOCV) and local LOOCV and 5-fold cross validation (CV), and achieves AUCs of 0.9576, 0.8356, and 0.9557, respectively. Compared with existing seven methods, MDA-SKF has outstanding performance on global LOOCV and 5-fold. We also test case studies to further analyze the performance of MDA-SKF on 32 diseases. Furthermore, 3200 candidate associations are obtained and a majority of them can be confirmed. It demonstrates that MDA-SKF is an accurate and efficient computational tool for guiding traditional experiments.

Discovering themes in biomedical literature using a projection-based algorithm

Background

The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously.

Results

We present an algorithm to analyze document collections that is based on a notion of a theme, defined as a dual representation based on a set of documents and key terms. In this work, a novel vector space mechanism is proposed for computing themes. Starting with a single document, the theme algorithm treats terms and documents as explicit components, and iteratively uses each representation to refine the other until the theme is detected. The method heavily relies on an optimization routine that we refer to as the projection algorithm which, under specific conditions, is guaranteed to converge to the first singular vector of a data matrix. We apply our algorithm to a collection of about sixty thousand PubMed ^Ⓡ documents examining the subject of Single Nucleotide Polymorphism, evaluate the results and show the effectiveness and scalability of the proposed method.

Conclusions

This study presents a contribution on theoretical and algorithmic levels, as well as demonstrates the feasibility of the method for large scale applications. The evaluation of our system on benchmark datasets demonstrates that our method compares favorably with the current state-of-the-art methods in computing clusters of documents with coherent topic terms.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2240-0) contains supplementary material, which is available to authorized users.

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.

Four Decades of Research in Alzheimer's Disease (1975-2014): A Bibliometric and Scientometric Analysis

BACKGROUND

Bibliometric and scientometric methods can be applied to the study of a research field.

OBJECTIVE

We hypothesized that a bibliometric and scientometric analysis of the Alzheimer's disease (AD) research field could render trends that provide researchers and funding agencies valuable insight into the history of the field, current tendencies, and potential future directions.

METHODS

We performed searches in publicly available databases including PubMed, Scopus, Web of Science, and Alzheimer's Funding Analyzer for the period 1975-2014, and conducted a curve fitting analysis with non-linear regression.

RESULTS

While the rate and impact of publications continue to increase, the number of patents per year is currently declining after peaking in the late 2000s, and the funding budget has plateaued in the last 5-10 years analyzed. Genetics is the area growing at a fastest pace, whereas pathophysiology and therapy have not grown further in the last decade. Among the targets of pathophysiology research, amyloid-β continues to be the focus of greatest interest, with tau and apolipoprotein E stagnant after a surge in the 1990s. The role of inflammation, microglia, and the synapse are other research topics with growing interest. Regarding preventative strategies, education attainment, diet, and exercise are recently gaining some momentum, whereas NSAIDs and statins have lost the spotlight they once had.

CONCLUSION

Our bibliometric and scientometric analysis provides distinct trends in AD research in the last four decades, including publication and patent output, funding, impact, and topics. Our findings could inform the decision-making of research funding agencies in the near future.

PORTER: a Prototype System for Patient-Oriented Radiology Reporting

To empower patients to participate in their medical care and decision-making, effective communication is critical. In radiology, the clinical report is the primary medium of communication. Although radiologists historically have authored reports with the referring provider as the intended reader, patients increasingly access the reports through portals to electronic health record systems. We developed a system named PORTER (Patient-Oriented Radiology Reporter) to augment radiology reports with lay-language definitions. Our IRB-approved, HIPAA-compliant study protocol analyzed 100 knee MRI reports from an academic medical center to identify the most commonly utilized terms. A glossary of 313 terms was constructed to include definitions of the terms and, where available, links to reference sources and public-domain images. Flesch-Kincaid readability scores were computed to assure that definitions were readable at or below 10th-grade reading level. The system provided an interactive web site to view outpatient knee MRI exams. After logging in with their exam ID number and date of birth, patients viewed their report annotated with definitions from the glossary. Applicable images were displayed when the user's mouse hovered over a glossary term. This patient-oriented system can help empower patients to better understand their radiology results.

Multidimensional knowledge-based framework is an essential step in the categorization of gene sets in complex disorders

In complex disorders, collaborative role of several genes accounts for the multitude of symptoms and the discovery of molecular mechanisms requires proper understanding of pertinent genes. Majority of the recent techniques utilize either single information or consolidate the independent outlook from multiple knowledge sources for assisting the discovery of candidate genes. In any case, given that various sorts of heterogeneous sources are possibly significant for quality gene prioritization, every source bearing data not conveyed by another, we assert that a perfect strategy ought to give approaches to observe among them in a genuine integrative style that catches the degree of each, instead of utilizing a straightforward mix of sources. We propose a flexible approach that empowers multi-source information reconciliation for quality gene prioritization that augments the complementary nature of various learning sources so as to utilize the maximum information of aggregated data. To illustrate the proposed approach, we took Autism Spectrum Disorder (ASD) as a case study and validated the framework on benchmark studies. We observed that the combined ranking based on integrated knowledge reduces the false positive observations and boosts the performance when compared with individual rankings. The clinical phenotype validation for ASD shows that there is a significant linkage between top positioned genes and endophenotypes of ASD. Categorization of genes based on endophenotype associations by this method will be useful for further hypothesis generation leading to clinical and translational analysis. This approach may also be useful in other complex neurological and psychiatric disorders with a strong genetic component.

A framework for exploring associations between biomedical terms in PubMed

Co-occurrence relationships in PubMed between terms accelerate the recognition of term associations. The lack of manually curated relationships in vocabularies and the rapid increase of biomedical literatures highlight the importance of co-occurrence relationships. Here we proposed a framework to explore term associations based on a standard procedure that comprises multiple tools of text mining and relationship degree calculation methods. The text of PubMed were segmented into sentences by Apache OpenNLP first, and then terms of sentences were recognized by MGREP. After that two terms occurring in a common sentence were identified as a co-occurrence relationship. The relationship degree is then calculated using Normalized MEDLINE Distance (NMD) or relationship-scaled score (RSS) method. The framework was utilized in exploring associations between terms of Gene Ontology (GO) and Disease Ontology (DO) based on co-occurrence relationship. Results show that pairs of terms with more co-occurrence relationships indicate shared more semantic relationships of ontology and genes. The identified association terms based on co-occurrence relationships were applied in constructing a disease association network (DAN). The small giant component confirms with the observation that diseases in the same class have more linkage than diseases in different classes.

Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets

Metabolomics answers a fundamental question in biology: How does metabolism respond to genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 800 structurally identified metabolites. However, biological interpretations of metabolic regulation in these datasets are hindered by inherent limits of pathway enrichment statistics. We have developed ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes structure similarity and chemical ontologies to map all known metabolites and name metabolic modules. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where p-values do not rely on the size of a background database. We demonstrate ChemRICH’s efficiency on a public metabolomics data set discerning the development of type 1 diabetes in a non-obese diabetic mouse model. ChemRICH is available at www.chemrich.fiehnlab.ucdavis.edu

Mimvec: a deep learning approach for analyzing the human phenome

Background

The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing.

Results

We converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones.

Conclusions

Our method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future.

Preventing gender-based violence victimization in adolescent girls in lower-income countries: Systematic review of reviews

This systematic review of reviews synthesizes evidence on the impact of interventions to prevent violence against adolescent girls and young women 10-24 years (VAWG) in low- and middle-income countries (LMICs). Theories of women's empowerment and the social ecology of multifaceted violence frame the review. Child abuse, female genital mutilation/cutting (FGMC), child marriage, intimate partner violence (IPV), and sexual violence were focal outcomes. Our review followed the Assessment of Multiple Systematic Reviews (AMSTAR) for the systematic review of reviews, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for a systematic review of recent intervention studies. Of 35 reviews identified between June 7 and July 20, 2016, 18 were non-duplicate systematic reviews of medium-to-high quality. Half of these 18 reviews focused on interventions to prevent IPV. Only four focused on adolescents, of which three focused on child marriage and one compared findings across early and late adolescence. None focused on interventions to prevent child abuse or sexual violence in adolescent/young women. From these 18 reviews and the supplemental systematic review of intervention studies, data were extracted on 34 experimental or quasi-experimental intervention studies describing 28 interventions. Almost all intervention studies measured impacts on one form of VAWG. Most studies assessed impacts on child marriage (n = 13), then IPV (n = 8), sexual violence (n = 4), child abuse (n = 3), and FGMC (n = 3). Interventions included 1-6 components, involving skills to enhance voice/agency (n = 17), social networks (n = 14), human resources like schooling (n = 10), economic incentives (n = 9), community engagement (n = 11) and community infrastructure development (n = 6). Bundled individual-level interventions and multilevel interventions had more favorable impacts on VAWG. Interventions involving community engagement, skill-building to enhance voice/agency, and social-network expansion show promise to reduce VAWG. Future interventions should target poly-victimization, compare impacts across adolescence, and include urban, out-of-school, married, and displaced/conflict-affected populations in LMICs, where VAWG may be heightened.

Towards a more molecular taxonomy of disease

Background

Disease taxonomies have been designed for many applications, but they tend not to fully incorporate the growing amount of molecular-level knowledge of disease processes, inhibiting research efforts. Understanding the degree to which we can infer disease relationships from molecular data alone may yield insights into how to ultimately construct more modern taxonomies that integrate both physiological and molecular information.

Results

We introduce a new technique we call Parent Promotion to infer hierarchical relationships between disease terms using disease-gene data. We compare this technique with both an established ontology inference method (CliXO) and a minimum weight spanning tree approach. Because there is no gold standard molecular disease taxonomy available, we compare our inferred hierarchies to both the Medical Subject Headings (MeSH) category C forest of diseases and to subnetworks of the Disease Ontology (DO). This comparison provides insights about the inference algorithms, choices of evaluation metrics, and the existing molecular content of various subnetworks of MeSH and the DO. Our results suggest that the Parent Promotion method performs well in most cases. Performance across MeSH trees is also correlated between inference methods. Specifically, inferred relationships are more consistent with those in smaller MeSH disease trees than larger ones, but there are some notable exceptions that may correlate with higher molecular content in MeSH.

Conclusions

Our experiments provide insights about learning relationships between diseases from disease genes alone. Future work should explore the prospect of disease term discovery from molecular data and how best to integrate molecular data with anatomical and clinical knowledge. This study nonetheless suggests that disease gene information has the potential to form an important part of the foundation for future representations of the disease landscape.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-017-0134-0) contains supplementary material, which is available to authorized users.

Evaluation of the informatician perspective: determining types of research papers preferred by clinicians

Background

To deliver evidence-based medicine, clinicians often reference resources that are useful to their respective medical practices. Owing to their busy schedules, however, clinicians typically find it challenging to locate these relevant resources out of the rapidly growing number of journals and articles currently being published. The literature-recommender system may provide a possible solution to this issue if the individual needs of clinicians can be identified and applied.

Methods

We thus collected from the CiteULike website a sample of 96 clinicians and 6,221 scientific articles that they read. We examined the journal distributions, publication types, reading times, and geographic locations. We then compared the distributions of MeSH terms associated with these articles with those of randomly sampled MEDLINE articles using two-sample Z-test and multiple comparison correction, in order to identify the important topics relevant to clinicians.

Results

We determined that the sampled clinicians followed the latest literature in a timely manner and read papers that are considered landmarks in medical research history. They preferred to read scientific discoveries from human experiments instead of molecular-, cellular- or animal-model-based experiments. Furthermore, the country of publication may impact reading preferences, particularly for clinicians from Egypt, India, Norway, Senegal, and South Africa.

Conclusion

These findings provide useful guidance for developing personalized literature-recommender systems for clinicians.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0463-z) contains supplementary material, which is available to authorized users.

Modeling Integration and Reuse of Heterogeneous Terminologies in Faceted Browsing Systems

We integrate heterogeneous terminologies into our category-theoretic model of faceted browsing and show that existing terminologies and vocabularies can be reused as facets in a cohesive, interactive system. Commonly found in online search engines and digital libraries, faceted browsing systems depend upon one or more taxonomies which outline the structure and content of the facets available for user interaction. Controlled vocabularies or terminologies are often externally curated and are available as a reusable resource across systems. We demonstrated previously that category theory can abstractly model faceted browsing in a way that supports the development of interfaces capable of reusing and integrating multiple models of faceted browsing. We extend this model by illustrating that terminologies can be reused and integrated as facets across systems with examples from the biomedical domain.

Integrated network analysis reveals potentially novel molecular mechanisms and therapeutic targets of refractory epilepsies

Epilepsy is a complex neurological disorder and a significant health problem. The pathogenesis of epilepsy remains obscure in a significant number of patients and the current treatment options are not adequate in about a third of individuals which were known as refractory epilepsies (RE). Network medicine provides an effective approach for studying the molecular mechanisms underlying complex diseases. Here we integrated 1876 disease-gene associations of RE and located those genes to human protein-protein interaction (PPI) network to obtain 42 significant RE-associated disease modules. The functional analysis of these disease modules showed novel molecular pathological mechanisms of RE, such as the novel enriched pathways (e.g., "presynaptic nicotinic acetylcholine receptors", "signaling by insulin receptor"). Further analysis on the relationships between current drug targets and the RE-related disease genes showed the rational mechanisms of most antiepileptic drugs. In addition, we detected ten potential novel drug targets (e.g., KCNA1, KCNA4-6, KCNC3, KCND2, KCNMA1, CAMK2G, CACNB4 and GRM1) located in three RE related disease modules, which might provide novel insights into the new drug discovery for RE therapy.

Text mining for improved exposure assessment

Chemical exposure assessments are based on information collected via different methods, such as biomonitoring, personal monitoring, environmental monitoring and questionnaires. The vast amount of chemical-specific exposure information available from web-based databases, such as PubMed, is undoubtedly a great asset to the scientific community. However, manual retrieval of relevant published information is an extremely time consuming task and overviewing the data is nearly impossible. Here, we present the development of an automatic classifier for chemical exposure information. First, nearly 3700 abstracts were manually annotated by an expert in exposure sciences according to a taxonomy exclusively created for exposure information. Natural Language Processing (NLP) techniques were used to extract semantic and syntactic features relevant to chemical exposure text. Using these features, we trained a supervised machine learning algorithm to automatically classify PubMed abstracts according to the exposure taxonomy. The resulting classifier demonstrates good performance in the intrinsic evaluation. We also show that the classifier improves information retrieval of chemical exposure data compared to keyword-based PubMed searches. Case studies demonstrate that the classifier can be used to assist researchers by facilitating information retrieval and classification, enabling data gap recognition and overviewing available scientific literature using chemical-specific publication profiles. Finally, we identify challenges to be addressed in future development of the system.

Ontology-Driven Search and Triage: Design of a Web-Based Visual Interface for MEDLINE

Background

Diverse users need to search health and medical literature to satisfy open-ended goals such as making evidence-based decisions and updating their knowledge. However, doing so is challenging due to at least two major difficulties: (1) articulating information needs using accurate vocabulary and (2) dealing with large document sets returned from searches. Common search interfaces such as PubMed do not provide adequate support for exploratory search tasks.

Objective

Our objective was to improve support for exploratory search tasks by combining two strategies in the design of an interactive visual interface by (1) using a formal ontology to help users build domain-specific knowledge and vocabulary and (2) providing multi-stage triaging support to help mitigate the information overload problem.

Methods

We developed a Web-based tool, Ontology-Driven Visual Search and Triage Interface for MEDLINE (OVERT-MED), to test our design ideas. We implemented a custom searchable index of MEDLINE, which comprises approximately 25 million document citations. We chose a popular biomedical ontology, the Human Phenotype Ontology (HPO), to test our solution to the vocabulary problem. We implemented multistage triaging support in OVERT-MED, with the aid of interactive visualization techniques, to help users deal with large document sets returned from searches.

Results

Formative evaluation suggests that the design features in OVERT-MED are helpful in addressing the two major difficulties described above. Using a formal ontology seems to help users articulate their information needs with more accurate vocabulary. In addition, multistage triaging combined with interactive visualizations shows promise in mitigating the information overload problem.

Conclusions

Our strategies appear to be valuable in addressing the two major problems in exploratory search. Although we tested OVERT-MED with a particular ontology and document collection, we anticipate that our strategies can be transferred successfully to other contexts.

A Review of the Three Types of Biomedical Literature and the Systematic Approach to Answer a Drug Information Request

The pharmacist is relied upon to provide drug information on a daily basis for patients and health care professionals. Performing drug information tasks requires the ability to efficiently search, critically analyze, and objectively evaluate the biomedical literature. Pharmacists and pharmacy students need to understand the biomedical literature and an organized method to answer drug information questions. Therefore, the tertiary, secondary, and primary literature resources are defined and examples are presented. In addition, the modified systematic approach to answer a drug information request is reviewed. Understanding the different types of literature and applying the systematic approach assists practitioners in efficiently supplying drug information. The purpose of this article is to assist the pharmacist and pharmacy student in determining the strengths and limitations of the various types of literature and applying the systematic approach to a drug information inquiry.

Generating disease-pertinent treatment vocabularies from MEDLINE citations

OBJECTIVE

Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies.

METHODS

A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches.

RESULTS

The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines.

CONCLUSIONS

The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.

Inferring Disease Associated Phosphorylation Sites via Random Walk on Multi-Layer Heterogeneous Network

As protein phosphorylation plays an important role in numerous cellular processes, many studies have been undertaken to analyze phosphorylation-related activities for drug design and disease treatment. However, although progresses have been made in illustrating the relationship between phosphorylation and diseases, no existing method focuses on disease-associated phosphorylation sites prediction. In this work, we proposed a multi-layer heterogeneous network model that makes use of the kinase information to infer disease-phosphorylation site relationship and implemented random walk on the heterogeneous network. Experimental results reveal that multi-layer heterogeneous network model with kinase layer is superior in inferring disease-phosphorylation site relationship when comparing with existing random walk model and common used classification methods.

Fusing literature and full network data improves disease similarity computation

Background

Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.

Results

Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively.

Conclusions

Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http://www.digintelli.com:8000/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1205-4) contains supplementary material, which is available to authorized users.

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science.

Search Strategies for Retrieving Complementary and Alternative Medicine Clinical Trials in Oncology

Purpose: Several Medline search strategies exist to retrieve complementary and alternative medicine (CAM) literature related to oncology. The objective of this study was to compare different search methods to ascertain the most optimal strategy. Methods: All clinical trials with CAM interventions in patients with cancer, published from 1965 to 2004, were abstracted using 4 different approaches. In the CAM filter search, the PubMed complementary medicine filter was used. The Ovid search was performed using a complex search strategy with the Ovid search engine. CAM keyword and not phytogenic searches involved the CAM filter search with the addition of the search terms “AND (complementary OR alternative)” and “NOT (antineoplastic agents, phytogenic), respectively. Articles were evaluated by 3 reviewers to ascertain whether they were clinical trials, the study intervention was related to CAM, and the condition prevented/treated was cancer related (inclusion criteria). Results: The CAM filter search retrieved 10 718 citations, Ovid retrieved 1190, CAM keyword retrieved 2895, and not phytogenic retrieved 1806. Compared to the CAM filter search, all other methods had significantly lower sensitivity (Ovid 48.3% ± 3.2%, CAM keyword 5.8% ± 1.5%, and not phytogenic 77.9% ± 2.7%, P < .001). The specificity of Ovid (38.4% ± 2.8%) and not phytogenic (40.8% ± 2.3%) searches was significantly higher ( P < .001) compared to CAM filter (8.8% ± 0.5%) and CAM keyword searches (1.9% ± 0.5%). Conclusion: The search strategy using PubMed’s complementary medicine filter, although comprehensive, lacks specificity; other methods, although more specific, lack sensitivity. Future indexing of all CAM clinical trials with a common medical subject heading term complementary medicine would enhance efficient retrieval of relevant citations.

Visualization in information retrieval: a three-level analysis

Visualization in information retrieval (IR) is a rapidly emerging area in which human-computer interaction (HCI) is adapted to the design of interfaces for IR systems. There are numerous proposals for the development of new visual IR systems. In parallel with the advent of advanced software and hardware, the attempts to design visualization-based IR systems are stimulated with the promise that information visualization applies visual processing to abstract information. Under this rapid development in the research area, there are few attempts to conceptualize this new field of study. This paper intends to classify the works of visualization for IR system design in a systematic way, by providing a mosaic view of the emerging area of visualization in IR system design. The paper proposes a three-level approach to analyzing visualization in IR system design: (i) component level analysis, (ii) technique level analysis and (iii) interaction level analysis. This three-level approach helps us to understand the state of visualization for the design of IR systems in multiple ways. The three-level approach also enables us in further serious investigation in this emerging field that has been created by the intersection of visualization and IR, by allowing us to look at different layers of visualization in IR that have both technical and theoretical concerns.

Another fine MeSH: clinical medicine meets information science

Introduction: evidence-based medicine (EBM) requires clinicians to tackle patient management by making more systematic use of databases like MEDLINE and using more sophisticated search strategies, to optimise the retrieval of relevant papers.

Aim: to collect empirical data on genuine requests for information and search strategies.

Methods: collection of literature search requests for staff using computerised biomedical databases at the libraries in district general hospitals, teaching hospitals and a research unit; the ‘intervention’ of introducing a common structured form explicitly asking about the patient/intervention/outcome/comparison axis; comparison of the structured and unstructured forms for completion of these four components; coding of the requests, using a modified Scott Richardson classification.

Results: the structured form promotes the use of components from the EBM anatomy and increases the total number of these in each search.

Conclusion: building prompts, like an EBM anatomy, into request forms improves the precision of literature searching without impairing recall. For clinical problems, rather than educational environments, the Scott Richardson classification needs modification.

A Cross-Species Analysis of Animal Models for the Investigation of Preterm Birth Mechanisms

BACKGROUND

Spontaneous preterm birth is the leading cause of neonatal morbidity and mortality worldwide. The ability to examine the exact mechanisms underlying this syndrome in humans is limited. Therefore, the study of animal models is critical to unraveling the key physiologic mechanisms that control the timing of birth. The purpose of this review is to facilitate enhanced assimilation of the literature on animal models of preterm birth by a broad range of investigators.

METHODS

Using classical systematic and informatics search techniques of the available literature through 2012, a database of intact animal models was generated. Research librarians generated a list of articles using multiple databases. From these articles, a comprehensive list of Medical Subject Headings (MeSH) was created. Using mathematical modeling, significant MeSH descriptors were determined, and a MEDLINE search algorithm was created. The articles were reviewed for mechanism of labor induction categorized by species.

RESULTS

Existing animal models of preterm birth comprise specific interventions to induce preterm birth, as no animal model was identified that exhibits natural spontaneous preterm birth at an incidence comparable to that of the humans. A search algorithm was developed which when used results in a comprehensive list of agents used to induce preterm delivery in a host of animal species. The evolution of 3 specific animal models--sheep, mice, and rats--has demonstrated a clear shift in focus in the literature from endocrine to inflammatory agents of preterm birth induction.

CONCLUSION

The process of developing a search algorithm to provide efficient access to information on animal models of preterm birth illustrates the need for a more precise organization of the literature to allow the investigator to focus on distinctly maternal versus fetal outcomes.

40 years of terrorist bombings - A meta-analysis of the casualty and injury profile

INTRODUCTION

Terrorists have used the explosive device successfully globally, with their effects extending beyond the resulting injuries. Suicide bombings, in particular, are being increasingly deployed due to the devastating effect of a combination of high lethality and target accuracy. The aim of this study was to identify trends and analyse the demographics and casualty figures of terrorist bombings worldwide.

METHODS

Analysis of the Global Terrorism Database (GTD) and a PubMed/Embase literature search (keywords "terrorist", and/or "suicide", and/or "bombing") from 1970 to 2014 was performed.

RESULTS

58,095 terrorist explosions worldwide were identified in the GTD. 5.08% were suicide bombings. Incidents per year are increasing (P<0.01). Mean casualty statistics per incidents was 1.14 deaths and 3.45 wounded from non-suicide incidents, and 10.16 and 24.16 from suicide bombings (p<0.05). The kill:wounded ratio was statistically higher in suicide attacks than non-suicide attacks, 1:1.3 and 1:1.24 respectively (p<0.05). The Middle East witnessed the most incidents (26.9%), with Europe (13.2%) ranked 4th. The literature search identified 41 publications reporting 167 incidents of which 3.9% detailed building collapse (BC), 60.8% confined space (CS), 23.5% open space (OS) and 11.8% semi-confined space (SC) attacks. 60.4% reported on suicide terrorist attacks. Overall 32 deaths and 180 injuries per incident were seen, however significantly more deaths occurred in explosions associated with a BC. Comparing OS and CS no difference in the deaths per incident was seen, 14.2(SD±17.828) and 15.63 (SD±10.071) respectively. However OS explosions resulted in significantly more injuries, 192.7 (SD±141.147), compared to CS, 79.20 (SD±59.8). Extremity related wounds were the commonest injuries seen (32%).

DISCUSSION/CONCLUSION

Terrorist bombings continue to be a threat and are increasing particularly in the Middle East. Initial reports, generated immediately at the scene by experienced coordination, on the type of detonation (suicide versus non-suicide), the environment of detonation (confined, open, building collapse) and the number of fatalities, and utilising the Kill:Wounded ratios found in this meta-analysis, can be used to predict the number of casualties and their likely injury profile of survivors to guide the immediate response by the medical services and the workload in the coming days.

Prediction of drugs having opposite effects on disease genes in a directed network

Background

Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information.

Methods

We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations.

Results

We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main.

Conclusions

Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0243-2) contains supplementary material, which is available to authorized users.

Identification of pleiotropic genes and gene sets underlying growth and immunity traits: a case study on Meishan pigs

Both growth and immune capacity are important traits in animal breeding. The animal quantitative trait loci (QTL) database is a valuable resource and can be used for interpreting the genetic mechanisms that underlie growth and immune traits. However, QTL intervals often involve too many candidate genes to find the true causal genes. Therefore, the aim of this study was to provide an effective annotation pipeline that can make full use of the information of Gene Ontology terms annotation, linkage gene blocks and pathways to further identify pleiotropic genes and gene sets in the overlapping intervals of growth-related and immunity-related QTLs. In total, 55 non-redundant QTL overlapping intervals were identified, 1893 growth-related genes and 713 immunity-related genes were further classified into overlapping intervals and 405 pleiotropic genes shared by the two gene sets were determined. In addition, 19 pleiotropic gene linkage blocks and 67 pathways related to immunity and growth traits were discovered. A total of 343 growth-related genes and 144 immunity-related genes involved in pleiotropic pathways were also identified, respectively. We also sequenced and genotyped 284 individuals from Chinese Meishan pigs and European pigs and mapped the single nucleotide polymorphisms (SNPs) to the pleiotropic genes and gene sets that we identified. A total of 971 high-confidence SNPs were mapped to the pleiotropic genes and gene sets that we identified, and among them 743 SNPs were statistically significant in allele frequency between Meishan and European pigs. This study explores the relationship between growth and immunity traits from the view of QTL overlapping intervals and can be generalized to explore the relationships between other traits.

Using Semantic Association to Extend and Infer Literature-Oriented Relativity Between Terms

Relative terms often appear together in the literature. Methods have been presented for weighting relativity of pairwise terms by their co-occurring literature and inferring new relationship. Terms in the literature are also in the directed acyclic graph of ontologies, such as Gene Ontology and Disease Ontology. Therefore, semantic association between terms may help for establishing relativities between terms in literature. However, current methods do not use these associations. In this paper, an adjusted R-scaled score (ARSS) based on information content (ARSSIC) method is introduced to infer new relationship between terms. First, set inclusion relationship between terms of ontology was exploited to extend relationships between these terms and literature. Next, the ARSS method was presented to measure relativity between terms across ontologies according to these extensional relationships. Then, the ARSSIC method using ratios of information shared of term's ancestors was designed to infer new relationship between terms across ontologies. The result of the experiment shows that ARSS identified more pairs of statistically significant terms based on corresponding gene sets than other methods. And the high average area under the receiver operating characteristic curve (0.9293) shows that ARSSIC achieved a high true positive rate and a low false positive rate. Data is available at http://mlg.hit.edu.cn/ARSSIC/.

Lost in translation: Review of identification bias, translation bias and research waste in dentistry

OBJECTIVE

To review how articles are retrieved from bibliographic databases, what article identification and translation problems have affected research, and how these problems can contribute to research waste and affect clinical practice.

METHODS

This literature review sought and appraised articles regarding identification- and translation-bias in the medical and dental literature, which limit the ability of users to find research articles and to use these in practice.

RESULTS

Articles can be retrieved from bibliographic databases by performing a word or index-term (for example, MeSH for MEDLINE) search. Identification of articles is challenging when it is not clear which words are most relevant, and which terms have been allocated to indexing fields. Poor reporting quality of abstracts and articles has been reported across the medical literature at large. Specifically in dentistry, research regarding time-to-event survival analyses found the allocation of MeSH terms to be inconsistent and inaccurate, important words were omitted from abstracts by authors, and the quality of reporting in the body of articles was generally poor. These shortcomings mean that articles will be difficult to identify, and difficult to understand if found. Use of specialized electronic search strategies can decrease identification bias, and use of tailored reporting guidelines can decrease translation bias. Research that cannot be found, or cannot be used results in research waste, and undermines clinical practice.

SIGNIFICANCE

Identification- and translation-bias have been shown to affect time-to-event dental articles, are likely affect other fields of research, and are largely unrecognized by authors and evidence seekers alike. By understanding that the problems exist, solutions can be sought to improve identification and translation of our research.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria—that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text—thus suffering from synonymy and polysemy—and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge—concretely Wikipedia—in order to create bag-of-concepts (BoC) representations of documents, understanding concept as “unit of meaning”, and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus.

Prediction of human disease-associated phosphorylation sites with combined feature selection approach and support vector machine

Phosphorylation is a crucial post-translational modification, which regulates almost all cellular processes in life. It has long been recognised that protein phosphorylation has close relationship with diseases, and therefore many researches are undertaken to predict phosphorylation sites for disease treatment and drug design. However, despite the success achieved by these approaches, no method focuses on disease-associated phosphorylation sites prediction. Herein, for the first time the authors propose a novel approach that is specially designed to identify associations between phosphorylation sites and human diseases. To take full advantage of local sequence information, a combined feature selection method-based support vector machine (CFS-SVM) that incorporates minimum-redundancy-maximum-relevance filtering process and forward feature selection process is developed. Performance evaluation shows that CFS-SVM is significantly better than the widely used classifiers including Bayesian decision theory, k nearest neighbour and random forest. With the extremely high specificity of 99%, CFS-SVM can still achieve a high sensitivity. Besides, tests on extra data confirm the effectiveness and general applicability of CFS-SVM approach on a variety of diseases. Finally, the analysis of selected features and corresponding kinases also help the understanding of the potential mechanism of disease-phosphorylation relationships and guide further experimental validations.

[Prevention of medical device-related adverse events in hospitals: Specifying the recommendations of the German Coalition for Patient Safety (APS) for users and operators of anaesthesia equipment]

BACKGROUND

The use and organisation of medical technology has an important role to play for patient and user safety in anaesthesia.

OBJECTIVES

Specification of the recommendations of the German Coalition for Patient Safety (APS) for users and operators of anaesthesia equipment, explore opportunities and challenges for the safe use and organisation of anaesthesia devices.

METHODS

We conducted a literature search in Medline/PubMed for studies dealing with the APS recommendations for the prevention of medical device-related risks in the context of anaesthesia. In addition, we performed an internet search for reports and recommendations focusing on the use and organisation of medical devices in anaesthesia. Identified studies were grouped and assigned to the recommendations. The division into users and operators was maintained.

RESULTS

Instruction and training in anaesthesia machines is sometimes of minor importance. Failure to perform functional testing seems to be a common cause of critical incidents in anaesthesia. There is a potential for reporting to the federal authority. Starting points for the safe operation of anaesthetic devices can be identified, in particular, at the interface of staff, organisation, and (anaesthesia) technology.

CONCLUSIONS

The APS recommendations provide valuable information on promoting the safe use of medical devices and organisation in anaesthesia. The focus will be on risks relating to the application as well as on principles and materials for the safe operation of anaesthesia equipment.

Understanding and improving low bystander CPR rates: a systematic review of the literature

Objectives:

Cardiopulmonary resuscitation (CPR) is a crucial yet weak link in the chain of survival for out-of-hospital cardiac arrest. We sought to understand the determinants of bystander CPR and the factors associated with successful training.

Methods:

For this systematic review, we searched 11 electronic databases, 1 trial registry and 9 scientific websites. We performed hand searches and contacted 6 content experts. We reviewed without restriction all communications pertaining to who should learn CPR, what should be taught, when to repeat training, where to give CPR instructions and why people lack the motivation to learn and perform CPR. We used standardized forms to review papers for inclusion, quality and data extraction. We grouped publications by category and classified recommendations using a standardized classification system that was based on level of evidence.

Results:

We reviewed 2409 articles and selected 411 for complete evaluation. We included 252 of the 411 papers in this systematic review. Differences in their study design precluded a meta-analysis. We classified 22 recommendations; those with the highest scores were 1) 9-1-1 dispatch-assisted CPR instructions, 2) teaching CPR to family members of cardiac patients, 3) Braslow's self-training video, 4) maximizing time spent using manikins and 5) teaching the concepts of ambiguity and diffusion of responsibility. Recommendations not supported by evidence include mass training events, pulse taking prior to CPR by laymen and CPR using chest compressions alone.

Conclusion:

We evaluated and classified the potential impact of interventions that have been proposed to improve bystander CPR rates. Our results may help communities design interventions to improve their bystander CPR rates.

Hegemonic structure of basic, clinical and patented knowledge on Ebola research: a US army reductionist initiative

Background

Ebola hemorrhagic fever (Ebola) is still a highly lethal infectious disease long affecting mainly neglected populations in sub-Saharan Africa. Moreover, this disease is now considered a potential worldwide threat. In this paper, we present an approach to understand how the basic, clinical and patent knowledge on Ebola is organized and intercommunicated and what leading factor could be shaping the evolution of the knowledge translation process for this disease.

Methodology

A combination of citation network analysis; analysis of Medical heading Subject (MeSH) and Gene Ontology (GO) terms, and quantitative content analysis for patents and scientific literature, aimed to map the organization of Ebola research was carried out.

Results

We found six putative research fronts (i.e. clusters of high interconnected papers). Three research fronts are basic research on Ebola virus structural proteins: glycoprotein, VP40 and VP35, respectively. There is a fourth research front of basic research papers on pathogenesis, which is the organizing hub of Ebola research. A fifth research front is pre-clinical research focused on vaccines and glycoproteins. Finally, a clinical-epidemiology research front related to the disease outbreaks was identified. The network structure of patent families shows that the dominant design is the use of Ebola virus proteins as targets of vaccines and other immunological treatments. Therefore, patents network organization resembles the organization of the scientific literature. Specifically, the knowledge on Ebola would flow from higher (clinical-epidemiology) to intermediated (cellular-tissular pathogenesis) to lower (molecular interactions) levels of organization.

Conclusion

Our results suggest a strong reductionist approach for Ebola research probably influenced by the lethality of the disease. On the other hand, the ownership profile of the patent families network and the main researches relationship with the United State Army suggest a strong involvement of this military institution in Ebola research.

GlycoRDF: an ontology to standardize glycomics data in RDF

MOTIVATION

Over the last decades several glycomics-based bioinformatics resources and databases have been created and released to the public. Unfortunately, there is no common standard in the representation of the stored information or a common machine-readable interface allowing bioinformatics groups to easily extract and cross-reference the stored information.

RESULTS

An international group of bioinformatics experts in the field of glycomics have worked together to create a standard Resource Description Framework (RDF) representation for glycomics data, focused on glycan sequences and related biological source, publications and experimental data. This RDF standard is defined by the GlycoRDF ontology and will be used by database providers to generate common machine-readable exports of the data stored in their databases.

AVAILABILITY AND IMPLEMENTATION

The ontology, supporting documentation and source code used by database providers to generate standardized RDF are available online (http://www.glycoinfo.org/GlycoRDF/).