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Objectives: Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML., Methods: We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise., Results: Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone., Conclusions: Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks., (© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Within well-established fields of biomedical science, we identify "gaps", topical areas of investigation that might be expected to occur but are missing. We define a field by carrying out a topical PubMed query, and analyze Medical Subject Headings by which the set of retrieved articles are indexed. Medical Subject headings (MeSH terms) which occur in >1% of the articles are examined pairwise to see how often they are predicted to co-occur within individual articles (assuming that they are independent of each other). A pair of MeSH terms that are predicted to co-occur in at least 10 articles, yet are not observed to co-occur in any article, are "gaps" and were studied further in a corpus of 10 disease-related article sets and 10 related to biological processes. Overall, articles that filled gaps were cited more heavily than non-gap-filling articles and were 61% more likely to be published in multidisciplinary high-impact journals. Nine different features of these "gaps" were characterized and tested to learn which, if any, correlate with the appearance of one or more articles containing both MeSH terms within the next five years. Several different types of gaps were identified, each having distinct combinations of predictive features: a) those arising as a byproduct of MeSH indexing rules; b) those having little biological meaning; c) those representing "low hanging fruit" for immediate exploitation; and d) those representing gaps across disciplines or sub-disciplines that do not talk to each other or work together. We have built a free, open tool called "Mine the Gap!" that identifies and characterizes the "gaps" for any PubMed query, which can be accessed via the Anne O'Tate value-added PubMed search interface (http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/AnneOTate.cgi)., Competing Interests: Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Medical subject headings (MeSH) are a flexible and useful tool for describing biomedical concepts. Here, we present MeSHier, a tool for assigning MeSH terms to biomedical documents based on abstract similarity and references to MEDLINE records. When applied to PubMedCentral papers, NIH grants, and USPTO patents we find that these two sources of information produce largely disjoint sets of related MEDLINE records, albeit with some overlap in MeSH. When combined they provide an enriched topical annotation that would not have been possible with either alone. MeSHier is available as a demo tool that can take as input IDs of PubMed papers, USPTO patents, and NIH grants: http://abel.lis.illinois.edu/cgi-bin/meshier/search.py.

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.

Objective: For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not complete or flexible enough for the SR process. In this work, highly accurate machine learning predictive models were built that include confidence predictions of whether an article is an RCT., Materials and Methods: The LibSVM classifier was used with forward selection of potential feature sets on a large human-related subset of MEDLINE to create a classification model requiring only the citation, abstract, and MeSH terms for each article., Results: The model achieved an area under the receiver operating characteristic curve of 0.973 and mean squared error of 0.013 on the held out year 2011 data. Accurate confidence estimates were confirmed on a manually reviewed set of test articles. A second model not requiring MeSH terms was also created, and performs almost as well., Discussion: Both models accurately rank and predict article RCT confidence. Using the model and the manually reviewed samples, it is estimated that about 8000 (3%) additional RCTs can be identified in MEDLINE, and that 5% of articles tagged as RCTs in Medline may not be identified., Conclusion: Retagging human-related studies with a continuously valued RCT confidence is potentially more useful for article ranking and review than a simple yes/no prediction. The automated RCT tagging tool should offer significant savings of time and effort during the process of writing SRs, and is a key component of a multistep text mining pipeline that we are building to streamline SR workflow. In addition, the model may be useful for identifying errors in MEDLINE publication types. The RCT confidence predictions described here have been made available to users as a web service with a user query form front end at: http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/RCT_Tagger.cgi., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

In the present paper, we have created several novel journal similarity metrics. The MeSH odds ratio measures the topical similarity of any pair of journals, based on the major MeSH headings assigned to articles in MEDLINE. The second metric employed the 2009 Author-ity author name disambiguation dataset as a gold standard for estimating the author odds ratio. This gives a straightforward, intuitive answer to the question: Given two articles in PubMed that share the same author name (lastname, first initial), how does knowing only the identity of the journals (in which the articles were published) predict the relative likelihood that they are written by the same person vs. different persons? The article pair odds ratio detects the tendency of authors to publish repeatedly in the same journal, as well as in specific pairs of journals. The metrics can be applied not only to estimate the similarity of a pair of journals, but to provide novel profiles of individual journals as well. For example, for each journal, one can define the MeSH cloud as the number of other journals that are topically more similar to it than expected by chance, and the author cloud as the number of other journals that share more authors than expected by chance. These metrics for journal pairs and individual journals have been provided in the form of public datasets that can be readily studied and utilized by others.

If mRNAs were the only RNAs made by a neuron, there would be a simple mapping of mRNAs to proteins. However, microRNAs and other non-coding RNAs (ncRNAs; endo-siRNAs, piRNAs, BC1, BC200, antisense and long ncRNAs, repeat-related transcripts, etc.) regulate mRNAs via effects on protein translation as well as transcriptional and epigenetic mechanisms. Not only are genes ON or OFF, but their ability to be translated can be turned ON or OFF at the level of synapses, supporting an enormous increase in information capacity. Here, I review evidence that ncRNAs are expressed pervasively within dendrites in mammalian brain; that some are activity-dependent and highly enriched near synapses; and that synaptic ncRNAs participate in plasticity responses including learning and memory. Ultimately, ncRNAs can be viewed as the post-it notes of the neuron. They have no literal meaning of their own, but derive their functions from where (and to what) they are stuck. This may explain, in part, why ncRNAs differ so dramatically from protein-coding genes, both in terms of the usual indicators of functionality and in terms of evolutionary constraints. ncRNAs do not appear to be direct mediators of synaptic transmission in the manner of neurotransmitters or receptors, yet they orchestrate synaptic plasticity-and may drive species-specific changes in cognition.

Background: Individuals and groups who write systematic reviews and meta-analyses in evidence-based medicine regularly carry out literature searches across multiple search engines linked to different bibliographic databases, and thus have an urgent need for a suitable metasearch engine to save time spent on repeated searches and to remove duplicate publications from initial consideration. Unlike general users who generally carry out searches to find a few highly relevant (or highly recent) articles, systematic reviewers seek to obtain a comprehensive set of articles on a given topic, satisfying specific criteria. This creates special requirements and challenges for metasearch engine design and implementation., Methods: We created a federated search tool that is connected to five databases: PubMed, EMBASE, CINAHL, PsycINFO, and the Cochrane Central Register of Controlled Trials. Retrieved bibliographic records were shown online; optionally, results could be de-duplicated and exported in both BibTex and XML format., Results: The query interface was extensively modified in response to feedback from users within our team. Besides a general search track and one focused on human-related articles, we also added search tracks optimized to identify case reports and systematic reviews. Although users could modify preset search options, they were rarely if ever altered in practice. Up to several thousand retrieved records could be exported within a few minutes. De-duplication of records returned from multiple databases was carried out in a prioritized fashion that favored retaining citations returned from PubMed., Conclusions: Systematic reviewers are used to formulating complex queries using strategies and search tags that are specific for individual databases. Metta offers a different approach that may save substantial time but which requires modification of current search strategies and better indexing of randomized controlled trial articles. We envision Metta as one piece of a multi-tool pipeline that will assist systematic reviewers in retrieving, filtering and assessing publications. As such, Metta may find wide utility for anyone who is carrying out a comprehensive search of the biomedical literature.

Just as translational medicine follows a long winding path from bench-to-bedside, so can Evidence-Based Medicine be envisioned as comprising a multi-step pipeline, from building evidence from raw data through synthesizing best practices and providing clinical decision support in a process described as the "evidence pyramid". 1 At one end, a heterogeneous mix of clinical and experimental studies including clinical trials, case reports, animal models and retrospective analyses are published as new knowledge. Then, experts collect and assess high-quality relevant evidence on specific issues and publish their conclusions (e.g., regarding efficacy and safety of treatments) as systematic reviews and meta-analyses. Finally, when an expert consensus has been reached, this must reach the attention of policy makers within the profession, the government and insurance companies, resulting in new practice guidelines and altered clinical practice within hospitals and clinics. At each stage, this process requires a large investment of time and effort from many individuals with a wide range of expertise. Our panel will discuss the variety of innovative approaches that are being taken by different informatics research groups to improve each step within the evidence based medicine pipeline. These approaches are, in part, devoted to making existing data collection and synthesis practices faster and more efficient, but they also involve re-imagining and re-engineering the processes by which evidence is accumulated, evaluated and applied.

Many neuroscience studies involve subcellular fractionation to produce isolated or enriched synaptic fractions. Synaptosomes are prepared by flotation of synaptic membranes on sucrose or Percoll gradients. Alternatively, synaptoneurosomes are prepared by filtration of tissue homogenate through a series of filters to obtain a fraction that is enriched in pinched-off dendritic spines. Whereas the protocol for making synaptosomes is reasonably well standardized and well described in the literature, there is (to our knowledge) no detailed lab protocol for making synaptoneurosomes. Here, we give the methods used in our laboratory to produce synaptoneurosomes that are suitable for studying RNAs and proteins.

A decade ago, RNA interference was proposed to serve as a physiologic means of regulating long-term gene expression in the mammalian brain. However, during the intervening years, this hypothesis appeared to be contradicted by both experimental data and theoretical considerations. More recently, the advent of deep sequencing technology has permitted a re-assessment of this issue. As reviewed here, a large population of small RNAs having features characteristic of endogenous siRNAs are detected within adult mouse hippocampus, which derive from genes involved in synaptic structure and signaling, and which show a significant, though modest (16-22%) up-regulation during olfactory discrimination training. Small RNAs derived from abundant cellular noncoding RNAs are also detected; in particular, a subpopulation of RNAs 25-30 nt. in length shows very large (>100 fold) up-regulation during olfactory discrimination training. Preliminary data suggest that the 25-30 nt. RNAs may associate with MIWI rather than Argonaute 1-4 homologues. I conclude that, despite their apparent low abundance, endogenous siRNAs and noncoding RNA-derived small RNAs are likely to play an important role in regulating synaptic plasticity., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Background: Transcription factors are thought to regulate the transcription of microRNA genes in a manner similar to that of protein-coding genes; that is, by binding to conventional transcription factor binding site DNA sequences located in or near promoter regions that lie upstream of the microRNA genes. However, in the course of analyzing the genomics of human microRNA genes, we noticed that annotated transcription factor binding sites commonly lie within 70- to 110-nt long microRNA small hairpin precursor sequences., Results: We report that about 45% of all human small hairpin microRNA (pre-miR) sequences contain at least one predicted transcription factor binding site motif that is conserved across human, mouse and rat, and this rises to over 75% if one excludes primate-specific pre-miRs. The association is robust and has extremely strong statistical significance; it affects both intergenic and intronic pre-miRs and both isolated and clustered microRNA genes. We also confirmed and extended this finding using a separate analysis that examined all human pre-miR sequences regardless of conservation across species., Conclusions: The transcription factor binding sites localized within small hairpin microRNA precursor sequences may possibly regulate their transcription. Transcription factors may also possibly bind directly to nascent primary microRNA gene transcripts or small hairpin microRNA precursors and regulate their processing.

The Arrowsmith two-node search is a strategy that is designed to assist biomedical investigators in formulating and assessing scientific hypotheses. More generally, it allows users to identify biologically meaningful links between any two sets of articles A and C in PubMed, even when these share no articles or authors in common and represent disparate topics or disciplines. The key idea is to relate the two sets of articles via title words and phrases (B-terms) that they share. We have created a free, public web-based version of the two-node search tool (http://arrowsmith.psych.uic.edu), have described its development and implementation, and have presented analyses of individual two-node searches. In this paper, we provide an updated tutorial intended for end-users, that covers the use of the tool for a variety of potential scientific use case scenarios. For example, one can assess a recent experimental, clinical or epidemiologic finding that connects two disparate fields of inquiry--identifying likely mechanisms to explain the finding, and choosing promising follow-up lines of investigation. Alternatively, one can assess whether the existing scientific literature lends indirect support to a hypothesis posed by the user that has not yet been investigated. One can also employ two-node searches to search for novel hypotheses. Arrowsmith provides a service that cannot be carried out feasibly via standard PubMed searches or by other available text mining tools.

microRNAs play an important role in regulating synaptic plasticity. For example, microRNAs target (and are targeted by) plasticity mediators such as CREB, MECP2, and FMRP. As well, specific microRNAs have been shown to be expressed within dendrites, where they regulate protein translation of targets mediating dendritic growth. Components of the RISC machinery have been implicated in long-term memory in Drosophila. Here, we review evidence from studies of adult mouse forebrain supporting a model wherein synaptic stimulation (above a threshold value) increases calcium within dendritic spines, activates calpain, and activates and releases dicer from the postsynaptic density. Dicer processes local pre-miRs into mature miRNAs that are incorporated into RISC complexes within or near the dendritic spine, and that bind available target mRNAs in the vicinity. These may repress protein translation under resting conditions, yet permit a phasic burst of translation to occur transiently following subsequent synaptic activity. Loaded RISC complexes that are not bound to local mRNAs may serve to bind and trap mRNAs that are being transported down dendrites. Thus, locally formed microRNAs may mark the location of previously activated synapses and perform a type of synaptic tagging and capture.

For many microRNAs, in many normal tissues and in cancer cells, the cellular levels of mature microRNAs are not simply determined by transcription of microRNA genes. This mini-review will discuss how microRNA biogenesis and function can be regulated by various nuclear and cytoplasmic processing events, including emerging evidence that microRNA pathway components can be selectively regulated by control of their subcellular localization and by modifications that occur during dynamic cellular signaling. Finally, I will briefly summarize studies of microRNAs in synaptic fractions of adult mouse forebrain, which may serve as a model for other cell types as well.

Unlabelled: Within mouse forebrain, a subset of microRNAs are significantly enriched in synaptoneurosomes (a synaptic fraction containing pinched-off dendritic spines) and a subset are significantly depleted relative to total forebrain homogenate. Here I show that, as a group, the pre-miR hairpin precursors of synaptically enriched microRNAs exhibit significantly different structural features than those that are non-enriched or depleted. Precursors of synaptically enriched microRNAs tend to have a) shorter uninterrupted double-stranded stem segments, and b) more symmetrical bulges containing a single nucleotide on each side. These structural differences may provide a basis for the differential binding of proteins that mediate dendritic transport of pre-miRs, or that prevent pre-miRs from being prematurely processed into mature miRNAs during the transport process., Reviewers: This article was reviewed by I. King Jordan and Jerzy Jurka.

Background: Many cell types have been reported to secrete small vesicles called exosomes, that are derived from multivesicular bodies and that can also form from endocytic-like lipid raft domains of the plasma membrane. Secretory exosomes contain a characteristic composition of proteins, and a recent report indicates that mast cell exosomes harbor a variety of mRNAs and microRNAs as well. Exosomes express cell recognition molecules on their surface that facilitate their selective targeting and uptake into recipient cells., Results: In this review, I suggest that exosomal secretion of proteins and RNAs may be a fundamental mode of communication within the nervous system, supplementing the known mechanisms of anterograde and retrograde signaling across synapses. In one specific scenario, exosomes are proposed to bud from the lipid raft region of the postsynaptic membrane adjacent to the postsynaptic density, in a manner that is stimulated by stimuli that elicit long-term potentiation. The exosomes would then transfer newly synthesized synaptic proteins (such as CAM kinase II alpha) and synaptic RNAs to the presynaptic terminal, where they would contribute to synaptic plasticity., Conclusion: The model is consistent with the known cellular and molecular features of synaptic neurobiology and makes a number of predictions that can be tested in vitro and in vivo.

Background: Identifying information that implicitly links two disparate sets of articles is a fundamental and intuitive data mining strategy that can help investigators address real scientific questions. The Arrowsmith two-node search finds title words and phrases (so-called B-terms) that are shared across two sets of articles within MEDLINE and displays them in a manner that facilitates human assessment. A serious stumbling-block has been the lack of a quantitative model for predicting which of the hundreds if not thousands of B-terms computed for a given search are most likely to be relevant to the investigator., Methodology/principal Findings: Using a public two-node search interface, field testers devised a set of two-node searches under real life conditions and a certain number of B-terms were marked relevant. These were employed as 'gold standards;' each B-term was characterized according to eight complementary features that were strongly correlated with relevance. A logistic regression model was developed that permits one to estimate the probability of relevance for each B-term, to rank B-terms according to their likely relevance, and to estimate the overall number of relevant B-terms inherent in a given two-node search., Conclusions/significance: The model greatly simplifies and streamlines the process of carrying out a two-node search, and may be applicable to a number of other literature-based discovery applications, including the so-called one-node search and related gene-centric strategies that incorporate implicit links to predict how genes may be related to each other and to human diseases. This should encourage much wider exploration of text mining for implicit information among the general scientific community., Availability: Two-node searches can be carried out freely at http://arrowsmith.psych.uic.edu., Supplementary Information: Supplementary data are available at Bioinformatics online.

Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface http://arrowsmith.psych.uic.edu in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community.

This informal tutorial is intended for investigators and students who would like to understand the workings of information retrieval systems, including the most frequently used search engines: PubMed and Google. Having a basic knowledge of the terms and concepts of information retrieval should improve the efficiency and productivity of searches. As well, this knowledge is needed in order to follow current research efforts in biomedical information retrieval and text mining that are developing new systems not only for finding documents on a given topic, but extracting and integrating knowledge across documents.

In this chapter, we review evidence that at least three different types of microRNA (miRNA)-messenger RNA (mRNA) target interactions exist in mammals: short seeds, long seeds, and "perfect" hits (allowing G:U matches). Because new types of miRNAs are still being discovered, this list may not yet be complete.

We present a model for automatically generating training sets and estimating the probability that a pair of Medline records sharing a last and first name initial are authored by the same individual, based on shared title words, journal name, co-authors, medical subject headings, language, and affiliation, as well as distinctive features of the name itself (i.e., presence of middle initial, suffix, and prevalence in Medline).