A question of medicine answering

Biomedicine although institutionally powerful in Sri Lanka has not been able to depersonalize illness or promote a notion of treatment efficacy disconnected from social relations. An ideology of healing crosscuts the trend toward health commodification. This paper focuses on three concepts fundamental to the interactive dynamics of treatment efficacy: constitution, habit, and power of the hand. A movement between two distinct types of health care seeking behavior is described. One is inspired by finding the right medicine fix, the other by finding a practitioner having a sensitivity toward one's sense of person and all this entails.     

Extraction of semantic biomedical relations from text using conditional random fields

Background  

The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.     

A cost-consequences analysis of a primary care librarian question and answering service

Background

Cost consequences analysis was completed from randomized controlled trial (RCT) data for the Just-in-time (JIT) librarian consultation service in primary care that ran from October 2005 to April 2006. The service was aimed at providing answers to clinical questions arising during the clinical encounter while the patient waits. Cost saving and cost avoidance were also analyzed. The data comes from eighty-eight primary care providers in the Ottawa area working in Family Health Networks (FHNs) and Family Health Groups (FHGs).

Methods

We conducted a cost consequences analysis based on data from the JIT project [1]. We also estimated the potential economic benefit of JIT librarian consultation service to the health care system.

Results

The results show that the cost per question for the JIT service was $38.20. The cost could be as low as $5.70 per question for a regular service. Nationally, if this service was implemented and if family physicians saw additional patients when the JIT service saved them time, up to 61,100 extra patients could be seen annually. A conservative estimate of the cost savings and cost avoidance per question for JIT was $11.55.

Conclusions

The cost per question, if the librarian service was used at full capacity, is quite low. Financial savings to the health care system might exceed the cost of the service. Saving physician''s time during their day could potentially lead to better access to family physicians by patients. Implementing a librarian consultation service can happen quickly as the time required to train professional librarians to do this service is short.     

Biomedical therapy using synthetic WKYMVm hexapeptide

WKYMVm hexapeptide has been identified as a strong FPR2 agonist through a library screening of synthetic peptides. The FPR2 has been reported to play a crucial role in inflammation and angiogenic responses via stimulation of chemotaxis, migration, cell proliferation, wound healing and vessel growth. Recently, the therapeutic effects of WKYMVm have been reported in various disease models. In cutaneous wound model in diabetic mice, WKYMVm facilitated wound healing processes by stimulating the formation of capillary and arteriole and re-epithelialization. In coronary artery stenosis model, WKYMVm coating on stent promoted re-endothelialization and lowered restenosis rate. In hindlimb ischemia mouse model, intramuscular injection of WKYMVm promoted homing of exogenously transplanted endothelial colony-forming cells and neovascularization, resulting in salvaging hindlimb. Furthermore, a single injection of WKYMVm encapsulated in poly (lactide-co-glycolide) microspheres was demonstrated to be as efficient as multiple injections of WKYMVm in restoring blood flow in hindlimb ischemia model. These observations may open up promising biomedical applications of WKYMVm for tissue repairs and regenerations.     

The question of the validity of haptoglobin/G-streptococci relations in nonhuman sera]

The relationship between haptoglobin type and the agglutinability of some group G streptococci, which was observed in humans, could also be demonstrated in animal sera. In humans the haptoglobin type Hp 1-1 is correlated with a lacking or very low agglutinability. Sera of 10 rhesus monkeys showed Hp-type 1-1 and agglutinated the streptococci only to dilutions of the serum up to 1:4. The sera of 48 pigs contained an Hp 1-1 like type and a streptococcus agglutination titer of max. 1:10. Seven ahaptoglobinaemic sera gave the same values. Fifty examined sera of rabbits showed also an Hp 1-1 like pattern and did not agglutinate or in undiluted state only.     

Computational drug repositioning using meta-path-based semantic network analysis

Background

Drug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. Many network-based approaches have been proposed for drug repositioning based on similarity networks, which integrate multiple sources of drugs and diseases. However, these methods may simply view nodes as the same-typed and neglect the semantic meanings of different meta-paths in the heterogeneous network. Therefore, it is urgent to develop a rational method to infer new indications for approved drugs.

Results

In this study, we proposed a novel methodology named HeteSim_DrugDisease (HSDD) for the prediction of drug repositioning. Firstly, we build the drug-drug similarity network and disease-disease similarity network by integrating the information of drugs and diseases. Secondly, a drug-disease heterogeneous network is constructed, which combines the drug similarity network, disease similarity network as well as the known drug-disease association network. Finally, HSDD predicts novel drug-disease associations based on the HeteSim scores of different meta-paths. The experimental results show that HSDD performs significantly better than the existing state-of-the-art approaches. HSDD achieves an AUC score of 0.8994 in the leave-one-out cross validation experiment. Moreover, case studies for selected drugs further illustrate the practical usefulness of HSDD.

Conclusions

HSDD can be an effective and feasible way to infer the associations between drugs and diseases using on meta-path-based semantic network analysis.

     

Assessing drug target association using semantic linked data

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.     

Enhancing biomedical text summarization using semantic relation extraction

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.     

Identification of MFS proteins in sorghum using semantic similarity

The antiporters, uniporters and symporters are the functional classes of MFS that play major role in ions homeostasis, regulation of pumps and channels, membrane structure, transporters activity in tolerance to abiotic stresses. Major facilitator superfamily (MFS) encodes Na⁺/H⁺ antiporter that are considered as being sensors of the molecule transports. A large number of MFS proteins have been identified in several plants, rice, maize, Arabidopsis etc. However, the majority of proteins in sorghum are described as putative, uncharacterized till date. This suggested that identified proteins of MFS in sorghum are far from saturation. Hence, we developed gene ontology (GO) terms semantic similarity based method using GOSemSim measure of R package. As a result, total 2,568 high (100 %) semantic similar orthologous proteins from 7 plant species were obtained. These data were used to predict function of 257 putative uncharacterized proteins from 18 families of MFS in Sorghum. Consequently, the identified proteins belonged to the function of regulation of pumps and channels, membrane structure, transporters activity, ions homeostasis, transporter mechanisms and binding process. These identified functions appear to have a distinct mechanism of salt-stress adaptation in plants. The proposed method will help in further identifying new proteins that can help in the development of agronomically and economically important plants.     

Hierarchical semantic composition of biosimulation models using bond graphs

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.     

Construction of protein semantic networks using PubMed/MEDLINE

A method for constructing protein semantic networks using MEDLINE abstracts is proposed. The publications retrieved by the context search for protein names (relevant) and the related publications were used. The proposed method is based on estimation of semantic connectivity between proteins. The connectivity score was calculated as a function of the number of relevant or related papers found for a pair of proteins. This score was used to construct a semantic network for 150 human proteins belonging to five different metabolic pathways. Analysis of the network demonstrated that the proteins involved in associated molecular processes formed the subgraphs with a high edge density.     

Improving life sciences information retrieval using semantic web technology

The ability to retrieve relevant information is at the heart of every aspect of research and development in the life sciences industry. Information is often distributed across multiple systems and recorded in a way that makes it difficult to piece together the complete picture. Differences in data formats, naming schemes and network protocols amongst information sources, both public and private, must be overcome, and user interfaces not only need to be able to tap into these diverse information sources but must also assist users in filtering out extraneous information and highlighting the key relationships hidden within an aggregated set of information. The Semantic Web community has made great strides in proposing solutions to these problems, and many efforts are underway to apply Semantic Web techniques to the problem of information retrieval in the life sciences space. This article gives an overview of the principles underlying a Semantic Web-enabled information retrieval system: creating a unified abstraction for knowledge using the RDF semantic network model; designing semantic lenses that extract contextually relevant subsets of information; and assembling semantic lenses into powerful information displays. Furthermore, concrete examples of how these principles can be applied to life science problems including a scenario involving a drug discovery dashboard prototype called BioDash are provided.     

Contextualization of drug-mediator relations using evidence networks

Background

Genomic analysis of drug response can provide unique insights into therapies that can be used to match the “right drug to the right patient.” However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results.

Methods

In this study, we address this challenge by constructing evidence networks built with protein and drug interactions from the STITCH and STRING interaction databases. STITCH and STRING are sister databases that catalog known and predicted drug-protein interactions and protein-protein interactions, respectively. Using these two databases, we have developed a method to construct evidence networks to “explain” the relation between a drug and a mediator. 

Results

We applied this approach to drug-mediator relations discovered in EDDY-CTRP analysis and identified evidence networks for ~70% of drug-mediator pairs where most mediators were not known direct targets for the drug. Constructed evidence networks enable researchers to contextualize the drug-mediator pair with current research and knowledge. Using evidence networks, we were able to improve the interpretability of the EDDY-CTRP results by linking the drugs and mediators with genes associated with both the drug and the mediator.

Conclusion

We anticipate that these evidence networks will help inform EDDY-CTRP results and enhance the generation of important insights to drug sensitivity that will lead to improved precision medicine applications.