Recent developments in multiplexing techniques for immunohistochemistry.

Expert Rev Mol Diagn. 2015;15(9):1171-86

Authors: Dixon AR, Bathany C, Tsuei M, White J, Barald KF, Takayama S

Abstract
Methods to detect immunolabeled molecules at increasingly higher resolutions, even when present at low levels, are revolutionizing immunohistochemistry (IHC). These technologies can be valuable for the management and examination of rare patient tissue specimens, and for improved accuracy of early disease detection. The purpose of this article is to highlight recent multiplexing methods that are candidates for more prevalent use in clinical research and potential translation to the clinic. Multiplex IHC methods, which permit identification of at least 3 and up to 30 discrete antigens, have been divided into whole-section staining and spatially-patterned staining categories. Associated signal enhancement technologies that can enhance performance and throughput of multiplex IHC assays are also discussed. Each multiplex IHC technique, detailed herein, is associated with several advantages as well as tradeoffs that must be taken into consideration for proper evaluation and use of the methods.

PMID: 26289603 [PubMed - indexed for MEDLINE]

[Identification of proteomic biomarkers of preeclampsia using protein microarray and tandem mass spectrometry].

Postepy Hig Med Dosw (Online). 2015;69:562-70

Authors: Charkiewicz K, Jasinska E, Laudanski P

Abstract
Preeclampsia (PE) is the leading cause of death of the fetus and the mother. The exact pathomechanism has not so far been clarified. PE coexists with many other diseases, but it is often difficult to explain the association between them and find a clear reason for their occurrence. There are many predictive factors, but none are highly specific in preeclampsia. The diagnosis of preeclampsia seems to be very complex, which is another argument for the exploration of knowledge on this subject. Although many of the discoveries have hitherto been made in the field of proteomics, still no single specific biomarker of preeclampsia has been discovered. Research at the genome level is important because it can help us understand the genetic predisposition of patients affected by this disease. Nevertheless, researchers have recently become more interested in the pathophysiology of PE, and they are trying to answer the question: what is the real, direct cause of preeclampsia? Thus, the discovery of a protein that is a good predictor of preeclampsia development would significantly accelerate the medical care of pregnant women, and consequently reduce the risk of occurrence of HELLP syndrome and fetal death. Apart from the predictive and diagnostic function, such a discovery would help us to better understand the pathogenesis of preeclampsia and to find in the future a medical drug to suppress this disease. In order to make a breakthrough in this field, scientists need to use the most modern methods of proteomics, which allow for the analysis of small amounts of biological material in the shortest possible time, thereby giving a lot of information about existing proteins in the sample. Such optimization allows two methods, most commonly used by researchers: tandem mass spectrometry and protein microarray technique.

PMID: 25983295 [PubMed - indexed for MEDLINE]

The bovine milk microbiota: insights and perspectives from -omics studies.

Mol Biosyst. 2016 May 24;

Authors: Addis MF, Tanca A, Uzzau S, Oikonomou G, Bicalho RC, Moroni P

Abstract
Recent significant progress in culture-independent techniques, together with the parallel development of -omics technologies and data analysis capabilities, have led to a new perception of the milk microbiota as a complex microbial community with great diversity and multifaceted biological roles, living in an environment that was until recently believed to be sterile. In this review, we summarize and discuss the latest findings on the milk microbiota in dairy cows, with a focus on the role it plays in bovine physiology and health. Following an introduction on microbial communities and the importance of their study, we present an overview of the -omics methods currently available for their characterization, and outline the potential offered by a systems biology approach encompassing metatranscriptomics, metaproteomics, and metametabolomics. Then, we review the recent discoveries on the dairy cow milk microbiome enabled by the application of -omics approaches. Learning from studies in humans and in the mouse model, and after a description of the endogenous route hypothesis, we discuss the role of the milk microbiota in the physiology and health of both the mother and the offspring, and report how it can be changed by farming practices and during infection. In conclusion, we shortly outline the impact of the milk microbiota on the quality of milk and of dairy products.

PMID: 27216801 [PubMed - as supplied by publisher]

Drug Design based on Protein Structure Network.

Mini Rev Med Chem. 2016 May 24;

Authors: Liang Z, Hu G

Abstract
Although structure-based drug design (SBDD) has become an indispensable tool in drug discovery for a long time, it continues to pose major challenges to date. With the advancement of "omics" techniques, systems biology has enriched SBDD into a new era, called polypharmacology, in which multi-targets drug or drug combination is designed to fight complex diseases. As a preliminary tool in systems biology, protein structure networks (PSNs) treat a protein as a set of residues linked by edges corresponding to the intramolecular interactions existing in folded structures between the residues. The PSN offers a computationally efficient tool to study the structure and function of proteins, and thus may facilitate structure-based drug design. Herein, we provide an overview of recent advances in PSNs, from predicting functionally important residues, to charactering protein-protein interactions and allosteric communication paths. Furthermore, we discuss potential pharmacological applications of PSN concepts and tools, and highlight the application to two families of drug targets, GPCRs and Hsp90. Although the application of PSNs as a framework for computer-aided drug discovery has been limited to date, we put forward the potential utility value in the near future and propose the PSNs could also serve as a new tool for polypharmacology research.

PMID: 27215941 [PubMed - as supplied by publisher]

[Big Biology : Supersizing Wissenschaft zu Beginn des 21. Jahrhunderts].

NTM. 2016 May 23;

Authors: Vermeulen N

PMID: 27215209 [PubMed - as supplied by publisher]

SWIFT-Review: a text-mining workbench for systematic review.

Syst Rev. 2016;5(1):87

Authors: Howard BE, Phillips J, Miller K, Tandon A, Mav D, Shah MR, Holmgren S, Pelch KE, Walker V, Rooney AA, Macleod M, Shah RR, Thayer K

Abstract
BACKGROUND: There is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem formulation phase of systematic review can be challenging, especially for topics having a large literature base. Here, we assess the performance of the SWIFT-Review priority ranking algorithm for identifying studies relevant to a given research question. We also explore the use of SWIFT-Review during problem formulation to identify, categorize, and visualize research areas that are data rich/data poor within a large literature corpus.
METHODS: Twenty case studies, including 15 public data sets, representing a range of complexity and size, were used to assess the priority ranking performance of SWIFT-Review. For each study, seed sets of manually annotated included and excluded titles and abstracts were used for machine training. The remaining references were then ranked for relevance using an algorithm that considers term frequency and latent Dirichlet allocation (LDA) topic modeling. This ranking was evaluated with respect to (1) the number of studies screened in order to identify 95 % of known relevant studies and (2) the "Work Saved over Sampling" (WSS) performance metric. To assess SWIFT-Review for use in problem formulation, PubMed literature search results for 171 chemicals implicated as EDCs were uploaded into SWIFT-Review (264,588 studies) and categorized based on evidence stream and health outcome. Patterns of search results were surveyed and visualized using a variety of interactive graphics.
RESULTS: Compared with the reported performance of other tools using the same datasets, the SWIFT-Review ranking procedure obtained the highest scores on 11 out of 15 of the public datasets. Overall, these results suggest that using machine learning to triage documents for screening has the potential to save, on average, more than 50 % of the screening effort ordinarily required when using un-ordered document lists. In addition, the tagging and annotation capabilities of SWIFT-Review can be useful during the activities of scoping and problem formulation.
CONCLUSIONS: Text-mining and machine learning software such as SWIFT-Review can be valuable tools to reduce the human screening burden and assist in problem formulation.

PMID: 27216467 [PubMed - in process]

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

J Biomed Semantics. 2016;7(1):9

Authors: Gupta S, Ross KE, Tudor CO, Wu CH, Schmidt CJ, Vijay-Shanker K

Abstract
BACKGROUND: MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation.
METHODS: We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence.
RESULTS: miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD . We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46-90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator's ranked list.
CONCLUSIONS: miRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as "In what diseases does microRNA regulation of apoptosis play a role?" or "Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?"."

PMID: 27216254 [PubMed - in process]

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Effective Management of Advanced Angiosarcoma by the Synergistic Combination of Propranolol and Vinblastine-based Metronomic Chemotherapy: A Bench to Bedside Study.

EBioMedicine. 2016 Apr;6:87-95

Authors: Pasquier E, André N, Street J, Chougule A, Rekhi B, Ghosh J, Philip DS, Meurer M, MacKenzie KL, Kavallaris M, Banavali SD

Abstract
BACKGROUND: Angiosarcomas are rare malignant tumors of vascular origin that represent a genuine therapeutic challenge. Recently, the combination of metronomic chemotherapy and drug repositioning has been proposed as an attractive alternative for cancer patients living in developing countries.
METHODS: In vitro experiments with transformed endothelial cells were used to identify synergistic interactions between anti-hypertensive drug propranolol and chemotherapeutics. This led to the design of a pilot treatment protocol combining oral propranolol and metronomic chemotherapy. Seven consecutive patients with advanced/metastatic/recurrent angiosarcoma were treated with this combination for up to 12months, followed by propranolol-containing maintenance therapy.
FINDINGS: Gene expression analysis showed expression of ADRB1 and ADRB2 adrenergic receptor genes in transformed endothelial cells and in angiosarcoma tumors. Propranolol strongly synergized with the microtubule-targeting agent vinblastine in vitro, but only displayed additivity or slight antagonism with paclitaxel and doxorubicin. A combination treatment using bi-daily propranolol (40mg) and weekly metronomic vinblastine (6mg/m(2)) and methotrexate (35mg/m(2)) was designed and used in 7 patients with advanced angiosarcoma. Treatment was well tolerated and resulted in 100% response rate, including 1 complete response and 3 very good partial responses, based on RECIST criteria. Median progression-free and overall survival was 11months (range 5-24) and 16months (range 10-30), respectively.
INTERPRETATION: Our results provide a strong rationale for the combination of β-blockers and vinblastine-based metronomic chemotherapy for the treatment of advanced angiosarcoma. Furthermore, our study highlights the potential of drug repositioning in combination with metronomic chemotherapy in low- and middle-income country setting.
FUNDING: This study was funded by institutional and philanthropic grants.

PMID: 27211551 [PubMed - in process]

Drug repositioning in sarcomas and other rare tumors.

EBioMedicine. 2016 Apr;6:4-5

Authors: Lee AT, Huang PH, Pollack SM, Jones RL

PMID: 27211532 [PubMed - in process]

Implementing Pharmacogenomics at Your Institution: Establishment and Overcoming Implementation Challenges.

Clin Transl Sci. 2016 May 23;

Authors: Arwood MJ, Chumnumwat S, Cavallari LH, Nutescu EA, Duarte JD

Abstract
With advancements in pharmacogenomics research and genotyping technology, implementation of pharmacogenomics into clinical practice is now feasible. The aim of this publication is to serve as a tutorial for institutions interested in developing pharmacogenomics services. Topics covered include resources needed, clinical decision support establishment, choosing a genotyping platform, and challenges faced with pharmacogenomics service implementation. This tutorial provides practical advice, drawing upon experience of two established clinical pharmacogenomics services. This article is protected by copyright. All rights reserved.

PMID: 27214750 [PubMed - as supplied by publisher]

Exome Sequencing of Extreme Clopidogrel Response Phenotypes Identifies B4GALT2 as a Determinant of On-treatment Platelet Reactivity.

Clin Pharmacol Ther. 2016 May 23;

Authors: Scott SA, Collet JP, Baber U, Yang Y, Peter I, Linderman M, Sload J, Qiao W, Kini AS, Sharma SK, Desnick RJ, Fuster V, Hajjar RJ, Montalescot G, Hulot JS

Abstract
Interindividual variability in platelet aggregation is common among patients treated with clopidogrel, and both high and low (LTPR) on-treatment platelet reactivity increase risks for adverse clinical outcomes. CYP2C19 influences clopidogrel response but only accounts for ∼12% of the variability in platelet reactivity. To identify novel variants implicated in on-treatment platelet reactivity, coronary artery disease (CAD) patients with extreme pharmacodynamic responses to clopidogrel and wild-type CYP2C19 were subjected to exome sequencing. Candidate variants that clustered in the LTPR subgroup subsequently were genotyped across the discovery cohort (n=636). Importantly, carriers of B4GALT2 c.909C>T had lower on-treatment P2Y12 reaction units (PRU; p=0.0077) and residual platelet aggregation (p=0.0008) compared to non-carriers, which remained significant after adjusting for CYP2C19 and other clinical variables in both the discovery (p=0.0298) and replication (n=160; PRU: p=0.0001) cohorts. B4GALT2 is a platelet-expressed galactosyltransferase, indicating that B4GALT2 c.909C>T may influence clopidogrel sensitivity through atypical cell-surface glycoprotein processing and platelet adhesion. This article is protected by copyright. All rights reserved.

PMID: 27213804 [PubMed - as supplied by publisher]

Systems biology in kidney transplantation: The application of multi-omics to a complex model.

Am J Transplant. 2016 May 23;

Authors: Bontha SV, Maluf DG, Mueller TF, Mas VR

Abstract
In spite of reduction of rejection rates and improvement in short term survival post kidney transplantation, modest progress has occurred in long-term graft attrition over the years. Timely identification of molecular events that precede clinical and histopathological changes might help in early intervention and thereby increase the graft half-life. Evolution of -omics tools has enabled systemic investigation of the influence of whole genome, epigenome, transcriptome, proteome, microbiome on transplant function and survival. In this 'omics' era, systemic approaches, in-depth clinical phenotyping and use of strict validation methods is the key for further understanding of the complex mechanisms associated with graft function. Systems biology is an inter-disciplinary holistic approach that focuses on complex and dynamic interactions within biological systems. The complexity of human kidney transplant is unlikely to be captured by a reductionist approach. It appears essential to integrate multi - omics data that can elucidate the multidimensional and multilayered regulation of the underlying heterogeneous and complex kidney transplant model. Here, we discuss studies which focus on genetic biomarkers, emerging technologies and systems biology approaches, which should increase the ability to discover biomarkers, understand mechanisms and stratify patients and responses post kidney transplantation. This article is protected by copyright. All rights reserved.

PMID: 27214826 [PubMed - as supplied by publisher]

Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism.

Metab Eng. 2016 May 19;

Authors: Long CP, Gonzalez JE, Sandoval NR, Antoniewicz MR

Abstract
Understanding the impact of gene knockouts on cellular physiology, and metabolism in particular, is centrally important to quantitative systems biology and metabolic engineering. Here, we present a comprehensive physiological characterization of wild-type Escherichia coli and 22 knockouts of enzymes in the upper part of central carbon metabolism, including the PTS system, glycolysis, pentose phosphate pathway and Entner-Doudoroff pathway. Our results reveal significant metabolic changes that are affected by specific gene knockouts. Analysis of collective trends and correlations in the data using principal component analysis (PCA) provide new, and sometimes surprising, insights into E. coli physiology. Additionally, by comparing the data-to-model predictions from constraint-based approaches such as FBA, MOMA and RELATCH we demonstrate the important role of less well-understood kinetic and regulatory effects in central carbon metabolism.

PMID: 27212692 [PubMed - as supplied by publisher]

Multi-Omics of Single Cells: Strategies and Applications.

Trends Biotechnol. 2016 May 19;

Authors: Bock C, Farlik M, Sheffield NC

Abstract
Most genome-wide assays provide averages across large numbers of cells, but recent technological advances promise to overcome this limitation. Pioneering single-cell assays are now available for genome, epigenome, transcriptome, proteome, and metabolome profiling. Here, we describe how these different dimensions can be combined into multi-omics assays that provide comprehensive profiles of the same cell.

PMID: 27212022 [PubMed - as supplied by publisher]

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow.

Cell Syst. 2016 May 19;

Authors: Brunk E, George KW, Alonso-Gutierrez J, Thompson M, Baidoo E, Wang G, Petzold CJ, McCloskey D, Monk J, Yang L, O'Brien EJ, Batth TS, Martin HG, Feist A, Adams PD, Keasling JD, Palsson BO, Lee TS

Abstract
Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proof of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks.

PMID: 27211860 [PubMed - as supplied by publisher]

Systems toxicology-based assessment of the candidate modified risk tobacco product THS2.2 for the adhesion of monocytic cells to human coronary arterial endothelial cells.

Toxicology. 2016 Jan 2;339:73-86

Authors: Poussin C, Laurent A, Peitsch MC, Hoeng J, De Leon H

Abstract
Alterations of endothelial adhesive properties by cigarette smoke (CS) can progressively favor the development of atherosclerosis which may cause cardiovascular disorders. Modified risk tobacco products (MRTPs) are tobacco products developed to reduce smoking-related risks. A systems biology/toxicology approach combined with a functional in vitro adhesion assay was used to assess the impact of a candidate heat-not-burn technology-based MRTP, Tobacco Heating System (THS) 2.2, on the adhesion of monocytic cells to human coronary arterial endothelial cells (HCAECs) compared with a reference cigarette (3R4F). HCAECs were treated for 4h with conditioned media of human monocytic Mono Mac 6 (MM6) cells preincubated with low or high concentrations of aqueous extracts from THS2.2 aerosol or 3R4F smoke for 2h (indirect treatment), unconditioned media (direct treatment), or fresh aqueous aerosol/smoke extracts (fresh direct treatment). Functional and molecular investigations revealed that aqueous 3R4F smoke extract promoted the adhesion of MM6 cells to HCAECs via distinct direct and indirect concentration-dependent mechanisms. Using the same approach, we identified significantly reduced effects of aqueous THS2.2 aerosol extract on MM6 cell-HCAEC adhesion, and reduced molecular changes in endothelial and monocytic cells. Ten- and 20-fold increased concentrations of aqueous THS2.2 aerosol extract were necessary to elicit similar effects to those measured with 3R4F in both fresh direct and indirect exposure modalities, respectively. Our systems toxicology study demonstrated reduced effects of an aqueous aerosol extract from the candidate MRTP, THS2.2, using the adhesion of monocytic cells to human coronary endothelial cells as a surrogate pathophysiologically relevant event in atherogenesis.

PMID: 26655683 [PubMed - indexed for MEDLINE]

Network Modeling Reveals Cross Talk of MAP Kinases during Adaptation to Caspofungin Stress in Aspergillus fumigatus.

PLoS One. 2015;10(9):e0136932

Authors: Altwasser R, Baldin C, Weber J, Guthke R, Kniemeyer O, Brakhage AA, Linde J, Valiante V

Abstract
Mitogen activated protein kinases (MAPKs) are highly conserved in eukaryotic organisms. In pathogenic fungi, their activities were assigned to different physiological functions including drug adaptation and resistance. Aspergillus fumigatus is a human pathogenic fungus, which causes life-threatening invasive infections. Therapeutic options against invasive mycoses are still limited. One of the clinically used drugs is caspofungin, which specifically targets the fungal cell wall biosynthesis. A systems biology approach, based on comprehensive transcriptome data sets and mathematical modeling, was employed to infer a regulatory network and identify key interactions during adaptation to caspofungin stress in A. fumigatus. Mathematical modeling and experimental validations confirmed an intimate cross talk occurring between the cell wall-integrity and the high osmolarity-glycerol signaling pathways. Specifically, increased concentrations of caspofungin promoted activation of these signalings. Moreover, caspofungin affected the intracellular transport, which caused an additional osmotic stress that is independent of glucan inhibition. High concentrations of caspofungin reduced this osmotic stress, and thus decreased its toxic activity. Our results demonstrated that MAPK signaling pathways play a key role during caspofungin adaptation and are contributing to the paradoxical effect exerted by this drug.

PMID: 26356475 [PubMed - indexed for MEDLINE]

Improving Biochemical Named Entity Recognition Performance Using PSO Classifier Selection and Bayesian Combination Method.

IEEE/ACM Trans Comput Biol Bioinform. 2016 May 18;

Authors: Akkasi A, Varoglu E

Abstract
Named Entity Recognition (NER) is a basic step for large number of consequent text mining tasks in the biochemical domain. Increasing the performance of such recognition systems is of high importance and always poses a challenge. In this study, a new community based decision making system is proposed which aims at increasing the efficiency of NER systems in the chemical/drug name context. Particle Swarm Optimization (PSO) algorithm is chosen as the expert selection strategy along with the Bayesian combination method to merge the outputs of the selected classifiers as well as evaluate the fitness of the selected candidates. The proposed system performs in two steps. The first step is focuses on creating various numbers of baseline classifiers for NER with different features sets using the Conditional Random Fields (CRFs). The second step involves the selection and efficient combination of the classifiers using PSO and Bayesisan combination. Two comprehensive corpora from BioCreative events, namely ChemDNER and CEMP, are used for the experiments conducted. Results show that the ensemble of classifiers selected by means of the proposed approach perform better than the single best classifier as well as ensembles formed using other popular selection/combination strategies for both corpora. Furthermore, the proposed method outperforms the best performing system at the Biocreative IV ChemDNER track by achieving an F-score of 87.95%.

PMID: 27214909 [PubMed - as supplied by publisher]

Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation.

J Biomed Discov Collab. 2016;7:e1

Authors: Smalheiser NR, Bonifield G

Abstract
In the present paper, we have created and characterized several similarity metrics for relating any two Medical Subject Headings (MeSH terms) to each other. The article-based metric measures the tendency of two MeSH terms to appear in the MEDLINE record of the same article. The author-based metric measures the tendency of two MeSH terms to appear in the body of articles written by the same individual (using the 2009 Author-ity author name disambiguation dataset as a gold standard). The two metrics are only modestly correlated with each other (r = 0.50), indicating that they capture different aspects of term usage. The article-based metric provides a measure of semantic relatedness, and MeSH term pairs that co-occur more often than expected by chance may reflect relations between the two terms. In contrast, the author metric is indicative of how individuals practice science, and may have value for author name disambiguation and studies of scientific discovery. We have calculated article metrics for all MeSH terms appearing in at least 25 articles in MEDLINE (as of 2014) and author metrics for MeSH terms published as of 2009. The dataset is freely available for download and can be queried at http://arrowsmith.psych.uic.edu/arrowsmith_uic/mesh_pair_metrics.html. Handling editor: Elizabeth Workman, MLIS, PhD.

PMID: 27213780 [PubMed - as supplied by publisher]