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Background Multiresistance in Gram-negative bacteria is often due to acquisition of several different antibiotic resistance genes, each associated with a different mobile genetic element, that tend to cluster together in complex conglomerations. Accurate, consistent annotation of resistance genes, the boundaries and fragments of mobile elements, and signatures of insertion, such as DR, facilitates comparative analysis of complex multiresistance regions and plasmids to better understand their evolution and how resistance genes spread. Objectives To extend the Repository of Antibiotic resistance Cassettes (RAC) web site, which includes a database of 'features', and the Attacca automatic DNA annotation system, to encompass additional resistance genes and all types of associated mobile elements. Methods Antibiotic resistance genes and mobile elements were added to RAC, from existing registries where possible. Attacca grammars were extended to accommodate the expanded database, to allow overlapping features to be annotated and to identify and annotate features such as composite transposons and DR. Results The Multiple Antibiotic Resistance Annotator (MARA) database includes antibiotic resistance genes and selected mobile elements from Gram-negative bacteria, distinguishing important variants. Sequences can be submitted to the MARA web site for annotation. A list of positions and orientations of annotated features, indicating those that are truncated, DR and potential composite transposons is provided for each sequence, as well as a diagram showing annotated features approximately to scale. Conclusions The MARA web site (http://mara.spokade.com) provides a comprehensive database for mobile antibiotic resistance in Gram-negative bacteria and accurately annotates resistance genes and associated mobile elements in submitted sequences to facilitate comparative analysis.

New approaches to evidence synthesis, which use human effort and machine automation in mutually reinforcing ways, can enhance the feasibility and sustainability of living systematic reviews. Human effort is a scarce and valuable resource, required when automation is impossible or undesirable, and includes contributions from online communities (“crowds”) as well as more conventional contributions from review authors and information specialists. Automation can assist with some systematic review tasks, including searching, eligibility assessment, identification and retrieval of full-text reports, extraction of data, and risk of bias assessment. Workflows can be developed in which human effort and machine automation can each enable the other to operate in more effective and efficient ways, offering substantial enhancement to the productivity of systematic reviews. This paper describes and discusses the potential—and limitations—of new ways of undertaking specific tasks in living systematic reviews, identifying areas where these human/machine “technologies” are already in use, and where further research and development is needed. While the context is living systematic reviews, many of these enabling technologies apply equally to standard approaches to systematic reviewing.

The third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 17–18 October 2017 in London, England. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and widespread acceptance of automated techniques for systematic reviews. The meeting’s conclusion was that the most pressing needs at present are to develop approaches for validating currently available tools and to provide increased access to curated corpora that can be used for validation. To that end, ICASR’s short-term goals in 2018–2019 are to propose and publish protocols for key tasks in systematic reviews and to develop an approach for sharing curated corpora for validating the automation of the key tasks.

Display Omitted Using EMR data to measure downtime effects on clinical processes.A matched case-control design is used to examine downtime effects.The effects of downtime vary with the type of IT problem.Even brief system downtimes may impact patient care.Having application potential to other clinical processes with established workflow. ObjectiveTo introduce and evaluate a method that uses electronic medical record (EMR) data to measure the effects of computer system downtime on clinical processes associated with pathology testing and results reporting. Materials and methodsA matched case-control design was used to examine the effects of five downtime events over 11-months, ranging from 5 to 300min. Four indicator tests representing different laboratory workflows were selected to measure delays and errors: potassium, haemoglobon, troponin and activated partial thromboplastin time. Tests exposed to a downtime were matched to tests during unaffected control periods by test type, time of day and day of week. Measures included clinician read time (CRT), laboratory turnaround time (LTAT), and rates of missed reads, futile searches, duplicate orders, and missing test results. ResultsThe effects of downtime varied with the type of IT problem. When clinicians could not logon to a results reporting system for 17-min, the CRT for potassium and haemoglobon tests was five (10.3 vs. 2.0days) and six times (13.4 vs. 2.1days) longer than control (p=0.01-0.04; p=0.0001-0.003). Clinician follow-up of tests was also delayed by another downtime involving a power outage with a small effect. In contrast, laboratory processing of troponin tests was unaffected by network services and routing problems. Errors including missed reads, futile searches, duplicate orders and missing test results could not be examined because the sample size of affected tests was not sufficient for statistical testing. ConclusionThis study demonstrates the feasibility of using routinely collected EMR data with a matched case-control design to measure the effects of downtime on clinical processes. Even brief system downtimes may impact patient care. The methodology has potential to be applied to other clinical processes with established workflows where tasks are pre-defined such as medications management.

This study examines the impact of cefepime and APP-β (antipseudomonal penicillin/ β-lactamase inhibitor combinations) on Gram-negative bacterial colonization and resistance in two Australian ICUs. While resistance did not cumulatively increase, cefepime (but not APP-β treatment) was associated with acquisition of antibiotic resistant Enterobacteriaceae, consistent with an ecological effect. Analysis of the resident gut E. coli population in a subset of patients showed an increase in markers of horizontal gene transfer after cefepime exposure that helps explain the increase in APP-β resistance and reminds us that unmeasured impacts on the microbiome are key outcome determinants that need to be fully explored. Electronic supplementary material The online version of this article (10.1186/s40635-018-0185-2) contains supplementary material, which is available to authorized users.

Background Although many aspects of systematic reviews use computational tools, systematic reviewers have been reluctant to adopt machine learning tools. Discussion We discuss that the potential reason for the slow adoption of machine learning tools into systematic reviews is multifactorial. We focus on the current absence of trust in automation and set-up challenges as major barriers to adoption. It is important that reviews produced using automation tools are considered non-inferior or superior to current practice. However, this standard will likely not be sufficient to lead to widespread adoption. As with many technologies, it is important that reviewers see “others” in the review community using automation tools. Adoption will also be slow if the automation tools are not compatible with workflows and tasks currently used to produce reviews. Many automation tools being developed for systematic reviews mimic classification problems. Therefore, the evidence that these automation tools are non-inferior or superior can be presented using methods similar to diagnostic test evaluations, i.e., precision and recall compared to a human reviewer. However, the assessment of automation tools does present unique challenges for investigators and systematic reviewers, including the need to clarify which metrics are of interest to the systematic review community and the unique documentation challenges for reproducible software experiments. Conclusion We discuss adoption barriers with the goal of providing tool developers with guidance as to how to design and report such evaluations and for end users to assess their validity. Further, we discuss approaches to formatting and announcing publicly available datasets suitable for assessment of automation technologies and tools. Making these resources available will increase trust that tools are non-inferior or superior to current practice. Finally, we identify that, even with evidence that automation tools are non-inferior or superior to current practice, substantial set-up challenges remain for main stream integration of automation into the systematic review process.

The second meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 3–4 October 2016 in Philadelphia, Pennsylvania, USA. ICASR is an interdisciplinary group whose aim is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. Having automated tools for systematic review should enable more transparent and timely review, maximizing the potential for identifying and translating research findings to practical application. The meeting brought together multiple stakeholder groups including users of summarized research, methodologists who explore production processes and systematic review quality, and technologists such as software developers, statisticians, and vendors. This diversity of participants was intended to ensure effective communication with numerous stakeholders about progress toward automation of systematic reviews and stimulate discussion about potential solutions to identified challenges. The meeting highlighted challenges, both simple and complex, and raised awareness among participants about ongoing efforts by various stakeholders. An outcome of this forum was to identify several short-term projects that participants felt would advance the automation of tasks in the systematic review workflow including (1) fostering better understanding about available tools, (2) developing validated datasets for testing new tools, (3) determining a standard method to facilitate interoperability of tools such as through an application programming interface or API, and (4) establishing criteria to evaluate the quality of tools’ output. ICASR 2016 provided a beneficial forum to foster focused discussion about tool development and resources and reconfirm ICASR members’ commitment toward systematic reviews’ automation.

Background Screening candidate studies for inclusion in a systematic review is time-consuming when conducted manually. Automation tools could reduce the human effort devoted to screening. Existing methods use supervised machine learning which train classifiers to identify relevant words in the abstracts of candidate articles that have previously been labelled by a human reviewer for inclusion or exclusion. Such classifiers typically reduce the number of abstracts requiring manual screening by about 50%. Methods We extracted four key characteristics of observational studies (population, exposure, confounders and outcomes) from the text of titles and abstracts for all articles retrieved using search strategies from systematic reviews. Our screening method excluded studies if they did not meet a predefined set of characteristics. The method was evaluated using three systematic reviews. Screening results were compared to the actual inclusion list of the reviews. Results The best screening threshold rule identified studies that mentioned both exposure (E) and outcome (O) in the study abstract. This screening rule excluded 93.7% of retrieved studies with a recall of 98%. Conclusions Filtering studies for inclusion in a systematic review based on the detection of key study characteristics in abstracts significantly outperformed standard approaches to automated screening and appears worthy of further development and evaluation.

While it is important for the evidence supporting practice guidelines to be current, that is often not the case. The advent of living systematic reviews has made the concept of "living guidelines" realistic, with the promise to provide timely, up-to-date and high-quality guidance to target users. We define living guidelines as an optimization of the guideline development process to allow updating individual recommendations as soon as new relevant evidence becomes available. A major implication of that definition is that the unit of update is the individual recommendation and not the whole guideline. We then discuss when living guidelines are appropriate, the workflows required to support them, the collaboration between living systematic reviews and living guideline teams, the thresholds for changing recommendations, and potential approaches to publication and dissemination. The success and sustainability of the concept of living guideline will depend on those of its major pillar, the living systematic review. We conclude that guideline developers should both experiment with and research the process of living guidelines.

Genome is the carrier of somatic mutations during the development of cancer. The catalogue of simple somatic mutations (SSM) is a subgroup of somatic mutations. It includes single base substitutions, small deletions and insertions of T substitutions at NpCpG trinucleotide contexts. Skin cancer showed distinct mutational spectra. Breast cancer, bladder cancer, and cervical cancer were found to have similar mutational spectra. Acute myeloid leukemia and lung cancer from South Korea, and colorectal cancer from China show high density of single base substitutions per mega base in chromosome Y. To sum up, our study and findings will be thought provoking in studying SSM in cancer.

A living systematic review (LSR) should keep the review current as new research evidence emerges. Any meta-analyses included in the review will also need updating as new material is identified. If the aim of the review is solely to present the best current evidence standard meta-analysis may be sufficient, provided reviewers are aware that results may change at later updates. If the review is used in a decision-making context, more caution may be needed. When using standard meta-analysis methods, the chance of incorrectly concluding that any updated meta-analysis is statistically significant when there is no effect (the type I error) increases rapidly as more updates are performed. Inaccurate estimation of any heterogeneity across studies may also lead to inappropriate conclusions. This paper considers four methods to avoid some of these statistical problems when updating meta-analyses: two methods, that is, law of the iterated logarithm and the Shuster method control primarily for inflation of type I error and two other methods, that is, trial sequential analysis and sequential meta-analysis control for type I and II errors (failing to detect a genuine effect) and take account of heterogeneity. This paper compares the methods and considers how they could be applied to LSRs.

Systematic reviews are difficult to keep up to date, but failure to do so leads to a decay in review currency, accuracy, and utility. We are developing a novel approach to systematic review updating termed "Living systematic review" (LSR): systematic reviews that are continually updated, incorporating relevant new evidence as it becomes available. LSRs may be particularly important in fields where research evidence is emerging rapidly, current evidence is uncertain, and new research may change policy or practice decisions. We hypothesize that a continual approach to updating will achieve greater currency and validity, and increase the benefits to end users, with feasible resource requirements over time.

Objective: Quality improvement of health care requires robust measurable indicators to track performance. However identifying which indicators are supported by strong clinical evidence, typically from clinical trials, is often laborious. This study tests a novel method for automatically linking indicators to clinical trial registrations. Design: A set of 522 quality of care indicators for 22 common conditions drawn from the CareTrack study were automatically mapped to outcome measures reported in 13 971 trials from ClinicalTrials.gov. Intervention: Text mining methods extracted phrases mentioning indicators and outcome phrases, and these were compared using the Levenshtein edit distance ratio to measure similarity. Main Outcome Measure: Number of care indicators that mapped to outcome measures in clinical trials. Results: While only 13% of the 522 CareTrack indicators were thought to have Level I or II evidence behind them, 353 (68%) could be directly linked to randomized controlled trials. Within these 522, 50 of 70 (71%) Level I and II evidence-based indicators, and 268 of 370 (72%) Level V (consensus-based) indicators could be linked to evidence. Of the indicators known to have evidence behind them, only 5.7% (4 of 70) were mentioned in the trial reports but were missed by our method. Conclusions: We automatically linked indicators to clinical trial registrations with high precision. Whilst the majority of quality indicators studied could be directly linked to research evidence, a small portion could not and these require closer scrutiny. It is feasible to support the process of indicator development using automated methods to identify research evidence. Refereed/Peer-reviewed

Display Omitted Implemented a generic rule-based method to extract epidemiological characteristics.Created environmental health exposure specific dictionaries.Precision ranging from 81% to 100% across six characteristics.Automated extraction is a feasible approach towards automation of systematic reviews. IntroductionMost data extraction efforts in epidemiology are focused on obtaining targeted information from clinical trials. In contrast, limited research has been conducted on the identification of information from observational studies, a major source for human evidence in many fields, including environmental health. The recognition of key epidemiological information (e.g., exposures) through text mining techniques can assist in the automation of systematic reviews and other evidence summaries. MethodWe designed and applied a knowledge-driven, rule-based approach to identify targeted information (study design, participant population, exposure, outcome, confounding factors, and the country where the study was conducted) from abstracts of epidemiological studies included in several systematic reviews of environmental health exposures. The rules were based on common syntactical patterns observed in text and are thus not specific to any systematic review. To validate the general applicability of our approach, we compared the data extracted using our approach versus hand curation for 35 epidemiological study abstracts manually selected for inclusion in two systematic reviews. ResultsThe returned F-score, precision, and recall ranged from 70% to 98%, 81% to 100%, and 54% to 97%, respectively. The highest precision was observed for exposure, outcome and population (100%) while recall was best for exposure and study design with 97% and 89%, respectively. The lowest recall was observed for the population (54%), which also had the lowest F-score (70%). ConclusionThe generated performance of our text-mining approach demonstrated encouraging results for the identification of targeted information from observational epidemiological study abstracts related to environmental exposures. We have demonstrated that rules based on generic syntactic patterns in one corpus can be applied to other observational study design by simple interchanging the dictionaries aiming to identify certain characteristics (i.e., outcomes, exposures). At the document level, the recognised information can assist in the selection and categorization of studies included in a systematic review.

Modern wide field-of-view atom probes permit observation of a wide range of crystallographic features that can be used to calibrate the tomographic reconstruction of the analyzed volume. In this study, methodologies to determine values of the geometric parameters involved in the tomographic reconstruction of atom probe data sets are presented and discussed. The influence of the tip to electrode distance and specimen temperature on these parameters is explored. Significantly, their influence is demonstrated to be very limited, indicating a relatively wide regime of experimental parameters space for sound atom probe tomography (APT) experiments. These methods have been used on several specimens and material types, and the results indicate that the reconstruction parameters are specific to each specimen. Finally, it is shown how an accurate calibration of the reconstruction enables improvements to the quality and reliability of the microscopy and microanalysis capabilities of the atom probe. © 2009 American Institute of Physics.

Motivation: Larger than gene structures (LGS) are DNA segments that include at least one gene and often other segments such as inverted repeats and gene promoters. Mobile genetic elements (MGE) such as integrons are LGS that play an important role in horizontal gene transfer, primarily in Gram-negative organisms. Known LGS have a profound effect on organism virulence, antibiotic resistance and other properties of the organism due to the number of genes involved. Expert-compiled grammars have been shown to be an effective computational representation of LGS, well suited to automating annotation, and supporting de novo gene discovery. However, development of LGS grammars by experts is labour intensive and restricted to known LGS. Objectives: This study uses computational grammar inference methods to automate LGS discovery. We compare the ability of six algorithms to infer LGS grammars from DNA sequences annotated with genes and other short sequences. We compared the predictive power of learned grammars against an expert-developed grammar for gene cassette arrays found in Class 1, 2 and 3 integrons, which are modular LGS containing up to 9 of about 240 cassette types. Results: Using a Bayesian generalization algorithm our inferred grammar was able to predict > 95% of MGE structures in a corpus of 1760 sequences obtained from Genbank (F-score 75%). Even with 100% noise added to the training and test sets, we obtained an F-score of 68%, indicating that the method is robust and has the potential to predict de novo LGS structures when the underlying gene features are known. Availability: http://www2.chi.unsw.edu.au/attacca. Contact: guyt@unsw.edu.au

Gene cassettes are small mobile elements, consisting of little more than a single gene and recombination site, which are captured by larger elements called integrons. Several cassettes may be inserted into the same integron forming a tandem array. The discovery of integrons in the chromosome of many species has led to the identification of thousands of gene cassettes, mostly of unknown function, while integrons associated with transposons and plasmids carry mainly antibiotic resistance genes and constitute an important means of spreading resistance. An updated compilation of gene cassettes found in sequences of such ‘mobile resistance integrons’ in GenBank was facilitated by a specially developed automated annotation system. At least 130 different (

The complexity of quantitative biomedical models, and the rate at which they are published, is increasing to a point where managing the information has become all but impossible without automation. International efforts are underway to standardise representation languages for a number of mathematical entities that represent a wide variety of physiological systems. This paper presents the Field Representation Language (FRL), a portable representation of values that change over space and/or time. FRL is an extensible mark-up language (XML) derivative with support for large numeric data sets in Hierarchical Data Format version 5 (HDF5). Components of FRL can be reused through unified resource identifiers (URI) that point to external resources such as custom basis functions, boundary geometries and numerical data. To demonstrate the use of FRL as an interchange we present three models that study hyperthermia cancer treatment: a fractal model of liver tumour microvasculature; a probabilistic model simulating the deposition of magnetic microspheres throughout it; and a finite element model of hyperthermic treatment. The microsphere distribution field was used to compute the heat generation rate field around the tumour. We used FRL to convey results from the microsphere simulation to the treatment model. FRL facilitated the conversion of the coordinate systems and approximated the integral over regions of the microsphere deposition field.

SummaryBuilding a sustainable health system in the 21st Century will require the reinvention of much of the present day system, and the intelligent use of information and communication technologies (ICT) to deliver high quality, safe, efficient and affordable health care. The Centre for Health Informatics (CHI) is Australia’s largest academic research group in this emerging discipline.Our research is underpinned by a planning process, based on different future scenarios for the health system, which helps us identify longer-term problems needing a sustained research effort. A research competency matrix is used to ensure that the Centre has the requisite core capabilities in the research methods and tools needed to pursue our research program.The Centre’s work is internationally recognized for its contributions in the development of intelligent search systems to support evidence-based healthcare, developing evaluation methodologies for ICT, and in understanding how communication shapes the safety and quality of health care delivery. Centre researchers also are working on safety models and standards for ICT in healthcare, mining complex gene micro array, medical literature and medical record data, building health system simulation methods to model the impact of health policy changes, and developing novel computational methods to automate the diagnosis of 3-D medical images.Any individual research group like CHI must necessarily focus on a few areas to allow it to develop sufficient research capacity to make novel and internationally significant contributions. As CHI approaches the end of its first decade, it is becoming clear that developing capacity becomes increasingly challenging as the research territory changes under our feet, and that the Centre will continue to evolve and shift its focus in the years to come.

Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time.

Graphical abstractDisplay Omitted Knowledge-based functional enrichment for gene prioritization of high throughput data.Automatic ontology generation from MEDLINE.Novel and fully automatic literature-based discovery.Literature ontologies perform better than expert-derived ones. MotivationGene set enrichment analysis (GSEA) annotates gene microarray data with functional information from the biomedical literature to improve gene-disease association prediction. We hypothesize that supplementing GSEA with comprehensive gene function catalogs built automatically using information extracted from the scientific literature will significantly enhance GSEA prediction quality. MethodsGold standard gene sets for breast cancer (BrCa) and colorectal cancer (CRC) were derived from the literature. Two gene function catalogs (CMeSH and CUMLS) were automatically generated. 1. By using Entrez Gene to associate all recorded human genes with PubMed article IDs. 2. Using the genes mentioned in each PubMed article and associating each with the article's MeSH terms (in CMeSH) and extracted UMLS concepts (in CUMLS). Microarray data from the Gene Expression Omnibus for BrCa and CRC was then annotated using CMeSH and CUMLS and for comparison, also with several pre-existing catalogs (C2, C4 and C5 from the Molecular Signatures Database). Ranking was done using, a standard GSEA implementation (GSEA-p). Gene function predictions for enriched array data were evaluated against the gold standard by measuring area under the receiver operating characteristic curve (AUC). ResultsComparison of ranking using the literature enrichment catalogs, the pre-existing catalogs as well as five randomly generated catalogs show the literature derived enrichment catalogs are more effective. The AUC for BrCa using the unenriched gene expression dataset was 0.43, increasing to 0.89 after gene set enrichment with CUMLS. The AUC for CRC using the unenriched gene expression dataset was 0.54, increasing to 0.9 after enrichment with CMeSH. C2 increased AUC (BrCa 0.76, CRC 0.71) but C4 and C5 performed poorly (between 0.35 and 0.5). The randomly generated catalogs also performed poorly, equivalent to random guessing. DiscussionGene set enrichment significantly improved prediction of gene-disease association. Selection of enrichment catalog had a substantial effect on prediction accuracy. The literature based catalogs performed better than the MSigDB catalogs, possibly because they are more recent. Catalogs generated automatically from the literature can be kept up to date. ConclusionPrediction of gene-disease association is a fundamental task in biomedical research. GSEA provides a promising method when using literature-based enrichment catalogs. AvailabilityThe literature based catalogs generated and used in this study are available from http://www2.chi.unsw.edu.au/literature-enrichment.

Antibiotic resistance in bacteria is often due to acquisition of resistance genes associated with different mobile genetic elements. In Gram-negative bacteria, many resistance genes are found as part of small mobile genetic elements called gene cassettes, generally found integrated into larger elements called integrons. Integrons carrying antibiotic resistance gene cassettes are often associated with mobile elements and here are designated 'mobile resistance integrons' (MRIs). More than one cassette can be inserted in the same integron to create arrays that contribute to the spread of multi-resistance. In many sequences in databases such as GenBank, only the genes within cassettes, rather than whole cassettes, are annotated and the same gene/cassette may be given different names in different entries, hampering analysis. We have developed the Repository of Antibiotic resistance Cassettes (RAC) website to provide an archive of gene cassettes that includes alternative gene names from multiple nomenclature systems and allows the community to contribute new cassettes. RAC also offers an additional function that allows users to submit sequences containing cassettes or arrays for annotation using the automatic annotation system Attacca. Attacca recognizes features (gene cassettes, integron regions) and identifies cassette arrays as patterns of features and can also distinguish minor cassette variants that may encode different resistance phenotypes (aacA4 cassettes and bla cassettes-encoding β-lactamases). Gaps in annotations are manually reviewed and those found to correspond to novel cassettes are assigned unique names. While there are other websites dedicated to integrons or antibiotic resistance genes, none includes a complete list of antibiotic resistance gene cassettes in MRI or offers consistent annotation and appropriate naming of all of these cassettes in submitted sequences. RAC thus provides a unique resource for researchers, which should reduce confusion and improve the quality of annotations of gene cassettes in integrons associated with antibiotic resistance. DATABASE URL: http://www2.chi.unsw.edu.au/rac.

Cancer is a heterogeneous disease caused, in part, by genetic and epigenetic alterations. These changes have been explored in studies of the pathogenesis of colorectal cancer (CRC) and have led to the identification of many biomarkers of disease progression. However, the number of biomarkers that have been incorporated into clinical practice is surprisingly small. We review the genetic and epigenetic mechanisms of colorectal cancer and discuss molecular markers recommended for use in early detection, screening, diagnosis, determination of prognosis, and prediction of treatment outcomes. We also review important areas for future research.

Background Gene discovery algorithms typically examine sequence data for low level patterns. A novel method to computationally discover higher order DNA structures is presented, using a context sensitive grammar. The algorithm was applied to the discovery of gene cassettes associated with integrons. The discovery and annotation of antibiotic resistance genes in such cassettes is essential for effective monitoring of antibiotic resistance patterns and formulation of public health antibiotic prescription policies. Results We discovered two new putative gene cassettes using the method, from 276 integron features and 978 GenBank sequences. The system achieved κ = 0.972 annotation agreement with an expert gold standard of 300 sequences. In rediscovery experiments, we deleted 789,196 cassette instances over 2030 experiments and correctly relabelled 85.6% (α ≥ 95%, E ≤ 1%, mean sensitivity = 0.86, specificity = 1, F-score = 0.93), with no false positives. Error analysis demonstrated that for 72,338 missed deletions, two adjacent deleted cassettes were labeled as a single cassette, increasing performance to 94.8% (mean sensitivity = 0.92, specificity = 1, F-score = 0.96). Conclusion Using grammars we were able to represent heuristic background knowledge about large and complex structures in DNA. Importantly, we were also able to use the context embedded in the model to discover new putative antibiotic resistance gene cassettes. The method is complementary to existing automatic annotation systems which operate at the sequence level.

Computational support of clinical decisions frequently requires the integration of data in a variety of formats and from multiple sources and domains. Some impressive multiscale computational models of biological phenomena have been developed as part of the study of disease and healthcare systems. One can now contemplate harnessing these models arising from computational biology and using highly interconnected clinical data to support clinical decision-making. Indeed, understanding how to build computational systems able to reason across heterogeneous models and datasets is one of the major and perhaps foundational challenges of translational biomedical informatics. In this paper, the authors examine the use of multimodels (models composed of several daughter models) and explore three major research challenges to reasoning across multiple models: model selection, model composition, and computer aided model construction.

Introduction: Clinical quality indicators are necessary to monitor the performance of healthcare services. The development of indicators should, wherever possible, be based on research evidence to minimise the risk of bias which may be introduced during their development, because of logistic, ethical or financial constraints alone. The development of automated methods to identify the evidence base for candidate indicators should improve the process of indicator development. The objective of this study is to explore the relationship between clinical quality indicators for asthma management in children with outcome and process measurements extracted from randomised controlled clinical trial reports. Methods and analysis: National-level indicators for asthma management in children will be extracted from the National Quality Measures Clearinghouse (NQMC) database and the National Institute for Health and Care Excellence (NICE) quality standards. Outcome measures will be extracted from published English language randomised controlled trial (RCT) reports for asthma management in children aged below 12 years. The two sets of measures will be compared to assess any overlap. The study will provide insights into the relationship between clinical quality indicators and measurements in RCTs. This study will also yield a list of measurements used in RCTs for asthma management in children, and will find RCT evidence for indicators used in practice. Ethics and dissemination: Ethical approval is not necessary because this study will not include patient data. Findings will be disseminated through peer-reviewed publications. Refereed/Peer-reviewed

Background: Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing’s effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Objective: Our goal was to evaluate an automatic method for citation snowballing’s capacity to identify and retrieve the full text and/or abstracts of cited articles. Methods: Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F 1 score. Results: The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F 1 score=79.3%; as proportion of citations in MAS: recall=85.5%, F 1 score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F 1 score=87.3%) of the 633 correctly retrieved citations. Conclusions: The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews. [J Med Internet Res 2014;16(10):e223]

Would lead to best currently available evidence at the push of a button The Cochrane handbook stipulates that systematic reviews should be examined every two years and updated if needed,1 but time and resource constraints mean that this occurs for only a third of reviews.2 Indeed, it may take as much time to update a review as it did to produce the original review. If this effort were redirected at developing methods to automate reviews, then updating might one day become almost effortless, immediate, and universal. In his novel Player Piano , Kurt Vonnegut Jr described machines that record the hand motions of artisans and replay them to reproduce a perfect copy of the artefact, more quickly and more economically. Such automation is needed in the update and even creation of systematic reviews, because the capability of the human machinery for review increasingly lags behind our capacity to produce primary evidence.3 The current reality is that many reviews are missing or outdated,4 and it is hard to imagine a solution that does not involve some automation.5 Technology has advanced such that software can be used …

Background The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest. Results BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task. Conclusions BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.

Background In Evidence-Based Medicine, clinical practice guidelines and systematic reviews are crucial devices for medical practitioners in making clinical decision. Clinical practice guidelines are systematically developed statements to support health care decisions for specific circumstances whereas systematic reviews are summaries of evidence on clearly formulated clinical questions. Biomarkers are biological measurements (primarily molecular) that are used to diagnose, predict treatment outcomes and prognosticate disease and are increasingly used in randomized controlled trials (RCT). Methods We search PubMed for systematic reviews, RCTs, case reports and non-systematic reviews with and without mentions of biomarkers between years 1990–2011. We compared the frequency and growth rate of biomarkers and non-biomarkers publications. We also compared the growth of the proportion of biomarker-based RCTs with the growth of the proportion of biomarker-based systematic reviews. Results With 147,774 systematic reviews indexed in PubMed from 1990 to 2011 (accessed on 18/10/2012), only 4,431 (3%) are dedicated to biomarkers. The annual growth rate of biomarkers publications is consistently higher than non-biomarkers publications, showing the growth in biomarkers research. From 20 years of systematic review publications indexed in PubMed, we identified a bias in systematic reviews against the inclusion of biomarker-based RCTs. Conclusions With the realisation of genome-based personalised medicine, biomarkers are becoming important for clinical decision making. The bias against the inclusion of biomarkers in systematic reviews leads to medical practitioners deprive of important information they require to address clinical questions. Sparse or weak evidence and lack of genetic training for systematic reviewers may contribute to this trend.

Summary Objectives To review the recent research literature in clinical decision support systems (CDSS). Methods A review of recent literature was undertaken, focussing on CDSS evaluation, consumers and public health, the impact of translational bioinformatics on CDSS design, and CDSS safety. Results In recent years, researchers have concentrated much less on the development of decision technologies, and have focussed more on the impact of CDSS in the clinical world. Recent work highlights that traditional process measures of CDSS effectiveness, such as document relevance are poor proxy measures for decision outcomes. Measuring the dynamics of decision making, for example via decision velocity, may produce a more accurate picture of effectiveness. Another trend is the broadening of user base for CDSS beyond front line clinicians. Consumers are now a major focus for biomedical informatics, as are public health officials, tasked with detecting and managing disease outbreaks at a health system, rather than individual patient level. Bioinformatics is also changing the nature of CDSS. Apart from personalisation of therapy recommendations, translational bioinformatics is creating new challenges in the interpretation of the meaning of genetic data. Finally, there is much recent interest in the safety and effectiveness of computerised physicianorderentry (CPOE) systems, given that prescribing and administration errors are a significant cause of morbidity and mortality. Of note, there is still much controversy surrounding the contention that poorly designed, implemented or used CDSS may actually lead to harm. Conclusions CDSS research remains an active and evolving area of research, as CDSS penetrate more widely beyond their traditional domain into consumer decision support, and as decisions become more complex, for example by involving sequence level genetic data.