查看更多>>摘要：The identification of gene-phenotype relationships is very important for the treatment of human diseases. Studies have shown that genes causing the same or similar phenotypes tend to interact with each other in a protein-protein interaction (PPI) network. Thus, many identification methods based on the PPI network model have achieved good results. However, in the PPI network, some interactions between the proteins encoded by candidate gene and the proteins encoded by known disease genes are very weak. Therefore, some studies have combined the PPI network with other genomic information and reported good predictive performances. However, we believe that the results could be further improved. In this paper, we propose a new method that uses the semantic similarity between the candidate gene and known disease genes to set the initial probability vector of a random walk with a restart algorithm in a human PPI network. The effectiveness of our method was demonstrated by leave-one-out cross-validation, and the experimental results indicated that our method outperformed other methods. Additionally, our method can predict new causative genes of multifactor diseases, including Parkinson's disease, breast cancer and obesity. The top predictions were good and consistent with the findings in the literature, which further illustrates the effectiveness of our method. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Polarity classification is the main subtask of sentiment analysis and opinion mining, well-known problems in natural language processing that have attracted increasing attention in recent years. Existing approaches mainly rely on the subjective part of text in which sentiment is expressed explicitly through specific words, called sentiment words. These approaches, however, are still far from being good in the polarity classification of patients' experiences since they are often expressed without any explicit expression of sentiment, but an undesirable or desirable effect of the experience implicitly indicates a positive or negative sentiment.
查看更多>>摘要：Based on the latest statistics on trends in cancer incidence and mortality worldwide, cancer burden is growing at an alarming pace. Many anticancer drugs have been proved effective against cancer cells as well as toxic to human tissues, which prevents sufficient doses from being administered to obtain a complete cure. In this paper we build an optimal control model to optimize the scheduling problem along one cycle of chemotherapy treatment using a single anticancer drug etoposide (VP-16). In the model, three mathematic models are adopted to mimic physiological response of body under chemotherapy: (i) Pharmacokinetic model of anticancer drug; (ii) A two-compartment tumor growth dynamic model under the influence of cell-cycle-specific anticancer drugs; and (iii) A semi-mechanistic model for myelosuppression. In this new integrated model clinically relevant objectives are proposed to gain a trade-off between efficacy and toxicity. Simulation results of clinical protocols are consistent with real-life clinical data. Furthermore, we find a new optimal drug regimen which can improve the efficacy without the risk of severe toxicity. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Background: Identifying key variables such as disorders within the clinical narratives in electronic health records has wide-ranging applications within clinical practice and biomedical research. Previous research has demonstrated reduced performance of disorder named entity recognition (NER) and normalization (or grounding) in clinical narratives than in biomedical publications. In this work, we aim to identify the cause for this performance difference and introduce general solutions.
查看更多>>摘要：Genome-wide association studies (GWAS) are a powerful tool for pathogenetic studies of complex diseases. The rich genetic information of GWAS data is mostly not fully utilized. In this study, we developed a sliding window-based genotype dependence testing tool SWGDT. SWGDT can be applied to GWAS data for genome-wide susceptibility gene scan utilizing known causal gene information. To evaluate the performance of SWGDT, a real GWAS dataset of Kashin-Beck disease (KBD) was analyzed. Immunohistochemistry was also performed to validate the relevance of identified gene with KBD. SWGDT analysis of KBD GWAS data identified a novel candidate gene TACR1 for KBD. Immunohistochemistry observed that the expression level of TACR1 protein in KBD articular cartilage was significantly higher than that in healthy articular cartilage. The real GWAS data analysis results illustrate the performance of SWGDT for genome-wide susceptibility gene scan. SWGDT can help to identify novel disease genes that may be missed by GWAS. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Introduction: A common bottleneck during ontology evaluation is knowledge acquisition from domain experts for gold standard creation. This paper contributes a novel semi-automated method for evaluating the concept coverage and accuracy of biomedical ontologies by complementing expert knowledge with knowledge automatically extracted from clinical practice guidelines and electronic health records, which minimizes reliance on expensive domain expertise for gold standards generation.
查看更多>>摘要：The physical spaces within which the work of health occurs - the home, the intensive care unit, the emergency room, even the bedroom - influence the manner in which behaviors unfold, and may contribute to efficacy and effectiveness of health interventions. Yet the study of such complex workspaces is difficult. Health care environments are complex, chaotic workspaces that do not lend themselves to the typical assessment approaches used in other industrial settings. This paper provides two methodological advances for studying internal health care environments: a strategy to capture salient aspects of the physical environment and a suite of approaches to visualize and analyze that physical environment. We used a FaroTM laser scanner to obtain point cloud data sets of the internal aspects of home environments. The point cloud enables precise measurement, including the location of physical boundaries and object perimeters, color, and light, in an interior space that can be translated later for visualization on a variety of platforms. The work was motivated by vizHOME, a multi-year program to intensively examine the home context of personal health information management in a way that minimizes repeated, intrusive, and potentially disruptive in vivo assessments. Thus, we illustrate how to capture, process, display, and analyze point clouds using the home as a specific example of a health care environment. Our work presages a time when emerging technologies facilitate inexpensive capture and efficient management of point cloud data, thus enabling visual and analytical tools for enhanced discharge planning, new insights for designers of consumer-facing clinical informatics solutions, and a robust approach to context-based studies of health-related work environments. (C) 2015 Elsevier Inc. All rights reserved.
查看更多>>摘要：Objective: To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models.
查看更多>>摘要：Introduction: Despite considerable research efforts, the process of metastasis formation is still a subject of intense discussion, and even established models differ considerably in basic details and in the conclusions drawn from them. Mathematical and computational models add a new perspective to the research as they can quantitatively investigate the processes of metastasis and the effects of treatment. However, existing models look at only one treatment option at a time.
查看更多>>摘要：Efficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM's initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements.
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