Exploring Synthetic and Systems Biology at the University of Edinburgh.

Biochem Soc Trans. 2016 Jun 15;44(3):692-5

Authors: Fletcher L, Rosser S, Elfick A

Abstract
The Centre for Synthetic and Systems Biology ('SynthSys') was originally established in 2007 as the Centre for Integrative Systems Biology, funded by the Biotechnology and Biological Sciences Research Council (BBSRC) and the Engineering and Physical Sciences Research Council (EPSRC). Today, SynthSys embraces an extensive multidisciplinary community of more than 200 researchers from across the University with a common interest in synthetic and systems biology. Our research is broad and deep, addressing a diversity of scientific questions, with wide ranging impact. We bring together the power of synthetic biology and systems approaches to focus on three core thematic areas: industrial biotechnology, agriculture and the environment, and medicine and healthcare. In October 2015, we opened a newly refurbished building as a physical hub for our new U.K. Centre for Mammalian Synthetic Biology funded by the BBSRC/EPSRC/MRC as part of the U.K. Research Councils' Synthetic Biology for Growth programme.

PMID: 27284029 [PubMed - in process]

SYNBIOCHEM-a SynBio foundry for the biosynthesis and sustainable production of fine and speciality chemicals.

Biochem Soc Trans. 2016 Jun 15;44(3):675-7

Authors: Carbonell P, Currin A, Dunstan M, Fellows D, Jervis A, Rattray NJ, Robinson CJ, Swainston N, Vinaixa M, Williams A, Yan C, Barran P, Breitling R, Chen GG, Faulon JL, Goble C, Goodacre R, Kell DB, Feuvre RL, Micklefield J, Scrutton NS, Shapira P, Takano E, Turner NJ

Abstract
The Manchester Synthetic Biology Research Centre (SYNBIOCHEM) is a foundry for the biosynthesis and sustainable production of fine and speciality chemicals. The Centre's integrated technology platforms provide a unique capability to facilitate predictable engineering of microbial bio-factories for chemicals production. An overview of these capabilities is described.

PMID: 27284023 [PubMed - in process]

Abnormal Bone Acquisition with Early-Life HIV Infection: Role of Immune Activation and Senescent Osteogenic Precursors.

J Bone Miner Res. 2016 Jun 10;

Authors: Manavalan JS, Arpadi S, Tharmarajah S, Shah J, Zhang CA, Foca M, Neu N, Bell DL, Nishiyama KK, Kousteni S, Yin MT

Abstract
INTRODUCTION: Chronic immune activation associated with HIV infection may have negative consequences on bone acquisition in individuals infected with HIV early in life.
METHODS: Bone mineral density (BMD) and microarchitecture were characterized in 38 HIV-infected men on antiretroviral therapy (18 perinatally-infected, 20 adolescence-infected) and 20 uninfected men aged 20-25 years by dual energy x-ray absorptiometry (DXA), high resolution peripheral quantitative computed tomography (HRpQCT). Flow cytometry was utilized to measure CD4 + /CD8+ activation (HLADR + CD38 +) and senescence (CD28-CD57 +) and to quantify circulating osteogenic precursor (COP) cells in peripheral blood mononuclear cells using antibodies to Runx2 and osteocalcin (OCN). Telomere lengths were measured in sorted COP cells using qPCR.
RESULTS: DXA derived areal BMD Z-scores and HRpQCT derived volumetric BMD (vBMD) measures were lower in HIV-infected than uninfected men. Proportion of activated and senescent CD4+ and CD8+ T cells were higher in HIV-infected than uninfected men. The percentage of COP cells (Mean ± SEM) was lower in HIV-infected than uninfected (0.19 ± 0.02% vs 0.43 ± 0.06%; p < 0.0001) men, and also lower in perinatally-infected than adolescence-infected men (0.15 ± 0.02% vs 0.22 ± 0.03%; p < 0.04). Higher proportion of COP cells correlated with higher bone stiffness, a measure of bone strength, while higher proportion of activated CD4+ T cells correlated with lower BMD and stiffness and lower proportion of COP cells.
CONCLUSION: T cell activation with HIV-infection was associated with decreased numbers of osteogenic precursors as well as lower peak bone mass and bone strength. This article is protected by copyright. All rights reserved.

PMID: 27283956 [PubMed - as supplied by publisher]

SMT and TOFT: Why and How They are Opposite and Incompatible Paradigms.

Acta Biotheor. 2016 Jun 9;

Authors: Bizzarri M, Cucina A

Abstract
The Somatic Mutation Theory (SMT) has been challenged on its fundamentals by the Tissue Organization Field Theory of Carcinogenesis (TOFT). However, a recent publication has questioned whether TOFT could be a valid alternative theory of carcinogenesis to that presented by SMT. Herein we critically review arguments supporting the irreducible opposition between the two theoretical approaches by highlighting differences regarding the philosophical, methodological and experimental approaches on which they respectively rely. We conclude that SMT has not explained carcinogenesis due to severe epistemological and empirical shortcomings, while TOFT is gaining momentum. The main issue is actually to submit SMT to rigorous testing. This concern includes the imperatives to seek evidence for disproving one's hypothesis, and to consider the whole, and not just selective evidence.

PMID: 27283400 [PubMed - as supplied by publisher]

Systems Biology Approaches for Understanding Genome Architecture.

Methods Mol Biol. 2016;1431:109-26

Authors: Sewitz S, Lipkow K

Abstract
The linear and three-dimensional arrangement and composition of chromatin in eukaryotic genomes underlies the mechanisms directing gene regulation. Understanding this organization requires the integration of many data types and experimental results. Here we describe the approach of integrating genome-wide protein-DNA binding data to determine chromatin states. To investigate spatial aspects of genome organization, we present a detailed description of how to run stochastic simulations of protein movements within a simulated nucleus in 3D. This systems level approach enables the development of novel questions aimed at understanding the basic mechanisms that regulate genome dynamics.

PMID: 27283305 [PubMed - in process]

Systems biology of viral infection.

Virus Res. 2016 Jun 15;218:1

Authors: Kaderali L, Thiel V

PMID: 27282286 [PubMed - in process]

[Stability Analysis of Susceptible-Infected-Recovered Epidemic Model].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2015 Oct;32(5):1013-8

Authors: Pan D, Shi H, Huang M, Yuan D

Abstract
With the range of application of computational biology and systems biology gradually expanding, the complexity of the bioprocess models is also increased. To address this difficult problem, it is required to introduce positive alternative analysis method to cope with it. Taking the dynamic model of the epidemic control process as research object, we established an evaluation model in our laboratory. Firstly, the model was solved with nonlinear programming method. The results were shown to be good. Based on biochemical systems theory, the ODE dynamic model was transformed into S-system. The eigen values of the model showed that the system was stable and contained oscillation phenomenon. Next the sensitivities of rate constant and logarithmic gains of the three key parameters were analyzed, as well as the robust of the system. The result indicated that the biochemical systems theory could be applied in different fields more widely.

PMID: 26964304 [PubMed - indexed for MEDLINE]

TaggerOne: Joint Named Entity Recognition and Normalization with Semi-Markov Models.

Bioinformatics. 2016 Jun 9;

Authors: Leaman R, Lu Z

Abstract
MOTIVATION: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization.
METHODS: We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput.
RESULTS: We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance.
AVAILABILITY: TaggerOne will be made open source upon acceptance. Demonstration available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/demo/TaggerOne/demo.cgi CONTACT: zhiyong.lu@nih.gov SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 27283952 [PubMed - as supplied by publisher]

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("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

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"Cystic Fibrosis"

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Pharmacological exploitation of the phenothiazine antipsychotics to develop novel antitumor agents-A drug repurposing strategy.

Sci Rep. 2016;6:27540

Authors: Wu CH, Bai LY, Tsai MH, Chu PC, Chiu CF, Chen MY, Chiu SJ, Chiang JH, Weng JR

Abstract
Phenothiazines (PTZs) have been used for the antipsychotic drugs for centuries. However, some of these PTZs have been reported to exhibit antitumor effects by targeting various signaling pathways in vitro and in vivo. Thus, this study was aimed at exploiting trifluoperazine, one of PTZs, to develop potent antitumor agents. This effort culminated in A4 [10-(3-(piperazin-1-yl)propyl)-2-(trifluoromethyl)-10H-phenothiazine] which exhibited multi-fold higher apoptosis-inducing activity than the parent compound in oral cancer cells. Compared to trifluoperazine, A4 demonstrated similar regulation on the phosphorylation or expression of multiple molecular targets including Akt, p38, and ERK. In addition, A4 induced autophagy, as evidenced by increased expression of the autophagy biomarkers LC3B-II and Atg5, and autophagosomes formation. The antitumor activity of A4 also related to production of reactive oxygen species and adenosine monophosphate-activated protein kinase. Importantly, the antitumor utility of A4 was extended in vivo as it, administrated at 10 and 20 mg/kg intraperitoneally, suppressed the growth of Ca922 xenograft tumors. In conclusion, the ability of A4 to target diverse aspects of cancer cell growth suggests its value in oral cancer therapy.

PMID: 27277973 [PubMed - in process]

Association of colonic regulatory T cells with hepatitis C virus pathogenesis and liver pathology.

J Gastroenterol Hepatol. 2015 Oct;30(10):1543-51

Authors: Hetta HF, Mekky MA, Khalil NK, Mohamed WA, El-Feky MA, Ahmed SH, Daef EA, Nassar MI, Medhat A, Sherman KE, Shata MT

Abstract
BACKGROUND AND AIM: Forkhead box protein P3 (FoxP3)(+) regulatory T (Treg ) cells play a fundamental role in maintaining the balance between the tissue-damaging and protective immune response to chronic hepatitis C (CHC) infection. Herein, we investigated the frequency of Treg cells in the colon and their potential relationship to the various CHC outcomes and hepatic histopathology.
METHODS: Colonic biopsies were collected from three groups with CHC: treatment naïve (TN; n = 20), non-responders (NR; n = 20), sustained virologic response (SVR; n = 20), and a fourth healthy control group (n = 10). The plasma viral loads and cytokines levels were determined by quantitative real-time polymerase chain reaction, and ELISA, respectively. Liver biopsies were examined to assess inflammatory score and fibrosis stage. Colonic Treg frequency was estimated by immunohistochemistry using confocal microscopy.
RESULTS: A significant increase in the frequency of colonic Treg was found in TN, and NR groups compared with the control and SVR group. The frequency of colonic Treg , plasma interleukin (IL)-10 and IL-4 levels were significantly positively correlated with viral load and negatively correlated with METAVIR inflammatory score, and fibrosis stages.
CONCLUSION: Colonic Treg cells are negatively correlated with liver inflammation and hepatitis C virus (HCV) viral load, which suggests a strong linkage between gut-derived Treg cell populations and HCV infection.

PMID: 25708446 [PubMed - indexed for MEDLINE]

Effects of CYP2B6 and CYP1A2 Genetic Variation on Nevirapine Plasma Concentration and Pharmacodynamics as Measured by CD4 Cell Count in Zimbabwean HIV-Infected Patients.

OMICS. 2015 Sep;19(9):553-62

Authors: Mhandire D, Lacerda M, Castel S, Mhandire K, Zhou D, Swart M, Shamu T, Smith P, Musingwini T, Wiesner L, Stray-Pedersen B, Dandara C

Abstract
The extremely high prevalence of HIV/AIDS in sub-Saharan Africa and limitations of current antiretroviral medicines demand new tools to optimize therapy such as pharmacogenomics for person-to-person variations. African populations exhibit greater genetic diversity than other world populations, thus making it difficult to extrapolate findings from one population to another. Nevirapine, an antiretroviral medicine, displays large plasma concentration variability which adversely impacts therapeutic virological response. This study, therefore, aimed to identify sources of variability in nevirapine pharmacokinetics and pharmacodynamics, focusing on genetic variation in CYP2B6 and CYP1A2. Using a cross-sectional study design, 118 HIV-infected adult Zimbabwean patients on nevirapine-containing highly active antiretroviral therapy (HAART) were characterized for three key functional single nucleotide polymorphisms (SNPs), CYP2B6 c.516G>T (rs3745274), CYP2B6 c.983T>C (rs28399499), and CYP1A2 g.-163C>A (rs762551). We investigated whether genotypes at these loci were associated with nevirapine plasma concentration, a therapeutic biomarker, and CD4 cell count, a biomarker of disease progression. CYP2B6 and CYP1A2 were chosen as the candidate genes based on reports in literature, as well as their prominence in the metabolism of efavirenz, a drug in the same class with nevirapine. Nevirapine plasma concentration was determined using LC-MS/MS. The mean nevirapine concentration for CYP2B6 c.516T/T genotype differed significantly from that of 516G/G (p < 0.001) and 516G/T (p < 0.01) genotypes, respectively. There were also significant differences in mean nevirapine concentration between CYP2B6 c.983T > C genotypes (p = 0.04). Importantly, the CYP1A2 g.-163C>A SNP was significantly associated with the pharmacodynamics endpoint, the CD4 cell count (p = 0.012). Variant allele frequencies for the three SNPs observed in this Zimbabwean group were similar to other African population groups but different to observations among Caucasian and Asian populations. We conclude that CYP2B6 c.516G>T and CYP2B6 c.983T>C could be important sources of nevirapine pharmacokinetic variability that could be considered for dosage optimization, while CYP1A2 g.-163C>A seems to be associated with HIV disease progression. These inter- and intra-population pharmacokinetic and pharmacodynamics differences suggest that a single prescribed dosage may not be appropriate for the treatment of disease. Further research into a personalized nevirapine regimen is required.

PMID: 26348712 [PubMed - indexed for MEDLINE]

Genetic polymorphism of pharmacogenomic VIP variants in the Deng people from the Himalayas in Southeast Tibet.

Biomarkers. 2015;20(5):275-86

Authors: Shi X, Wang L, Du S, Wang H, Feng T, Jin T, Kang L

Abstract
Little is known about polymorphic distribution of pharmacogenes among ethnicities, including the Deng people. In this study, we recruited 100 unrelated, healthy Deng people and genotyped them with respect to 76 different single-nucleotide polymorphisms by the PharmGKB database. Our results first indicated that the polymorphic distribution of pharmacogenes of the Deng people is most similar to CHD, suggesting that Deng people have a closest genetic relationship with CHD. Our data will enrich the database of pharmacogenomics and provide a theoretical basis for safer drug administration and individualized treatment plans, promoting the development of personalized medicine.

PMID: 26329523 [PubMed - indexed for MEDLINE]

BIG: A large-scale data integration tool for renal physiology.

Am J Physiol Renal Physiol. 2016 Jun 8;:ajprenal.00249.2016

Authors: Zhao Y, Yang CR, Raghuram V, Parulekar J, Knepper MA

Abstract
Due to recent advances in high throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

PMID: 27279488 [PubMed - as supplied by publisher]

A systems biology and proteomics-based approach identifies SRC and VEGFA as biomarkers in risk factor mediated coronary heart disease.

Mol Biosyst. 2016 Jun 9;

Authors: V A, Nayar PG, Murugesan R, S S, Krishnan J, Ahmed SS

Abstract
Coronary heart disease (CHD) is the most common cause of death worldwide. The burden of CHD increases with risk factors such as smoking, hypertension, obesity and diabetes. Several studies have demonstrated the association of these classical risk factors with CHD. However, the mechanisms of these associations remain largely unclear due to the complexity of disease pathophysiology and the lack of an integrative approach that fails to provide a definite understanding of molecular linkage. To overcome these problems, we propose a novel systems biology approach that relates causative genes, interactomes and pathways to elucidate the risk factors mediating the molecular mechanisms and biomarkers for feasible diagnosis. The literature was mined to retrieve the causative genes of each risk factor and CHD to construct protein interactomes. The interactomes were examined to identify 298 common molecular signatures. The common signatures were mapped to the tissue network to synthesize a sub-network consisting of 82 proteins. Further, the dissection of the sub-network provides functional modules representing a diverse range of molecular functions, including the AKT/p13k, MAPK and wnt pathways. Also, the prioritization of functional modules identifies SRC, VEGFA and HIF1A as potential candidate markers. Further, we validate these candidates with the existing markers CRP, NOS3 and VCAM1 in the serum of 63 individuals, 33 with CHD and 30 controls, using ELISA. SRC, VEGFA, H1F1A, CRP and NOS3 were significantly altered in patients compared to controls. These results support the utility of these candidate markers for the diagnosis of CHD. Overall, our molecular observations indicate the influence of risk factors in the pathophysiology of CHD and identify serum markers for diagnosis.

PMID: 27279347 [PubMed - as supplied by publisher]

Global, quantitative and dynamic mapping of protein subcellular localization.

Elife. 2016 Jun 9;5

Authors: Itzhak DN, Tyanova S, Cox J, Borner GH

Abstract
Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8,700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology.

PMID: 27278775 [PubMed - as supplied by publisher]

Applying NGS Data to Find Evolutionary Network Biomarkers from the Early and Late Stages of Hepatocellular Carcinoma.

Biomed Res Int. 2015;2015:391475

Authors: Wong YH, Wu CC, Lin CL, Chen TS, Chang TH, Chen BS

Abstract
Hepatocellular carcinoma (HCC) is a major liver tumor (~80%), besides hepatoblastomas, angiosarcomas, and cholangiocarcinomas. In this study, we used a systems biology approach to construct protein-protein interaction networks (PPINs) for early-stage and late-stage liver cancer. By comparing the networks of these two stages, we found that the two networks showed some common mechanisms and some significantly different mechanisms. To obtain differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two stages of liver cancer by systems biology method using NGS data from cancer cells and adjacent noncancer cells. Using carcinogenesis relevance values (CRVs), we identified 43 and 80 significant proteins and their PPINs (network markers) for early-stage and late-stage liver cancer. To investigate the evolution of network biomarkers in the carcinogenesis process, a primary pathway analysis showed that common pathways of the early and late stages were those related to ordinary cancer mechanisms. A pathway specific to the early stage was the mismatch repair pathway, while pathways specific to the late stage were the spliceosome pathway, lysine degradation pathway, and progesterone-mediated oocyte maturation pathway. This study provides a new direction for cancer-targeted therapies at different stages.

PMID: 26366411 [PubMed - indexed for MEDLINE]

Mining clinical attributes of genomic variants through assisted literature curation in Egas.

Database (Oxford). 2016;2016

Authors: Matos S, Campos D, Pinho R, Silva RM, Mort M, Cooper DN, Oliveira JL

Abstract
The veritable deluge of biological data over recent years has led to the establishment of a considerable number of knowledge resources that compile curated information extracted from the literature and store it in structured form, facilitating its use and exploitation. In this article, we focus on the curation of inherited genetic variants and associated clinical attributes, such as zygosity, penetrance or inheritance mode, and describe the use of Egas for this task. Egas is a web-based platform for text-mining assisted literature curation that focuses on usability through modern design solutions and simple user interactions. Egas offers a flexible and customizable tool that allows defining the concept types and relations of interest for a given annotation task, as well as the ontologies used for normalizing each concept type. Further, annotations may be performed on raw documents or on the results of automated concept identification and relation extraction tools. Users can inspect, correct or remove automatic text-mining results, manually add new annotations, and export the results to standard formats. Egas is compatible with the most recent versions of Google Chrome, Mozilla Firefox, Internet Explorer and Safari and is available for use at https://demo.bmd-software.com/egas/Database URL: https://demo.bmd-software.com/egas/.

PMID: 27278817 [PubMed - in process]

Overlap in drug-disease associations between clinical practice guidelines and drug structured product label indications.

J Biomed Semantics. 2016;7:37

Authors: Leung TI, Dumontier M

Abstract
BACKGROUND: Clinical practice guidelines (CPGs) recommend pharmacologic treatments for clinical conditions, and drug structured product labels (SPLs) summarize approved treatment indications. Both resources are intended to promote evidence-based medical practices and guide clinicians' prescribing decisions. However, it is unclear how well CPG recommendations about pharmacologic therapies match SPL indications for recommended drugs. In this study, we perform text mining of CPG summaries to examine drug-disease associations in CPG recommendations and in SPL treatment indications for 15 common chronic conditions.
METHODS: We constructed an initial text corpus of guideline summaries from the National Guideline Clearinghouse (NGC) from a set of manually selected ICD-9 codes for each of the 15 conditions. We obtained 377 relevant guideline summaries and their Major Recommendations section, which excludes guidelines for pediatric patients, pregnant or breastfeeding women, or for medical diagnoses not meeting inclusion criteria. A vocabulary of drug terms was derived from five medical taxonomies. We used named entity recognition, in combination with dictionary-based and ontology-based methods, to identify drug term occurrences in the text corpus and construct drug-disease associations. The ATC (Anatomical Therapeutic Chemical Classification) was utilized to perform drug name and drug class matching to construct the drug-disease associations from CPGs. We then obtained drug-disease associations from SPLs using conditions mentioned in their Indications section in SIDER. The primary outcomes were the frequency of drug-disease associations in CPGs and SPLs, and the frequency of overlap between the two sets of drug-disease associations, with and without using taxonomic information from ATC.
RESULTS: Without taxonomic information, we identified 1444 drug-disease associations across CPGs and SPLs for 15 common chronic conditions. Of these, 195 drug-disease associations overlapped between CPGs and SPLs, 917 associations occurred in CPGs only and 332 associations occurred in SPLs only. With taxonomic information, 859 unique drug-disease associations were identified, of which 152 of these drug-disease associations overlapped between CPGs and SPLs, 541 associations occurred in CPGs only, and 166 associations occurred in SPLs only.
CONCLUSIONS: Our results suggest that CPG-recommended pharmacologic therapies and SPL indications do not overlap frequently when identifying drug-disease associations using named entity recognition, although incorporating taxonomic relationships between drug names and drug classes into the approach improves the overlap. This has important implications in practice because conflicting or inconsistent evidence may complicate clinical decision making and implementation or measurement of best practices.

PMID: 27277160 [PubMed - in process]