bluuebunny/crossref_metadata_2025_split huggingface dataset:
Statistical, Computational and Visualization Methodologies to Unveil Gene Primary Structure Features
Authors: Pinheiro M. , Afreixo V. , Moura G. , Freitas A. , Santos M. A. S. , Oliveira J. L. 
Published: March 2018

Summary

Objectives:
Gene sequence features such as codon bias, codon context, and codon expansion (e.g. tri-nucleotide repeats) can be better understood at the genomic scale level by combining statistical methodologies with advanced computer algorithms and data visualization through sophisticated graphical interfaces. This paper presents the ANACONDA system, a bioinformatics application for gene primary structure analysis.

Methods:
Codon usage tables using absolute metrics and software for multivariate analysis of codon and amino acid usage are available in public databases. However, they do not provide easy computational and statistical tools to carry out detailed gene primary structure analysis on a genomic scale. We propose the usage of several statistical methods – contingency table analysis, residual analysis, multivariate analysis (cluster analysis) – to analyze the codon bias under various aspects (degree of association, contexts and clustering).

Results:
The developed solution is a software application that provides a user-guided analysis of codon sequences considering several contexts and codon usage on a genomic scale. The utilization of this tool in our molecular biology laboratory is focused on particular genomes, especially those from Saccharomyces cerevisiae, Candida albicansand Escherichia coli. In order to illustrate the applicability and output layouts of the software these species are herein used as examples.

Conclusions:
The statistical tools incorporated in the system are allowing to obtain global views of important sequence features. It is expected that the results obtained will permit identification of general rules that govern codon context and codon usage in any genome. Additionally, identification of genes containing expanded codons that arise as a consequence of erroneous DNA replication events will permit uncovering new genes associated with human disease.
Hypertrophic Cardiomyopathy
Authors: Škvor J. , Čapek P. 
Published: March 2018

Summary

Objectives:
Our research is a pilot study that specializes in the molecular genetic investigation of the TNNT2 gene in Czech patients with HCM/FHC disease. This study was initiated with exons 9 and 11 of TNNT2 because of their crucial role in the binding ability of cardiac troponin T to α-tropomyosin, and continued with analyses in other regions of the gene.

Methods:
Hundred and eighty-one Czech probands with HCM/FHC were enrolled in this study. The study group consisted of 24 families with FHC and probands without FHC history but with HCM diagnosis. The clinical diagnosis was based on echocardiography. DNA was isolated from peripheral blood lymphocytes and subsequently analyzed by the polymerase chain reaction (PCR), followed by DNA sequencing analyses, which were cross-sequenced.

Results:
The ΔGlu160 mutation was observed in a sequence of the TNNT2 gene in a patient with the severe form of hypertrophic cardiomyopathy. No sequence alteration was found in exons 9 and 11 of the TNNT2 gene found in the rest of the DNA samples.

Conclusion:
The ΔGlu160 mutation was observed in patients with severe forms of hypertrophic cardiomyopathy. This region is responsible for binding troponin T to α-tropomyosin. This mutation may lead to functional and structural effects on the troponin T protein. Mutations in this region are reported relatively rarely and therefore it was unique to observe the ΔGlu160 mutation in our study.
Developing an NLP and IR-based Algorithm for Analyzing Gene-disease Relationships
Authors: Yen Y. T. , Chen B. , Chiu H. W. , Lee Y. C. , Li Y. C. , Hsu C. Y. 
Published: March 2018

Summary

Objectives:
High-throughput techniques such as cDNA microarray, oligonucleotide arrays, and serial analysis of gene expression (SAGE) have been developed and used to automatically screen huge amounts of gene expression data. However, researchers usually spend lots of time and money on discovering gene-disease relationships by utilizing these techniques. We prototypically implemented an algorithm that can provide some kind of predicted results for biological researchers before they proceed with experiments, and it is very helpful for them to discover gene-disease relationships more efficiently.

Methods:
Due to the fast development of computer technology, many information retrieval techniques have been applied to analyze huge digital biomedical databases available worldwide. Therefore we highly expect that we can apply information retrieval (IR) technique to extract useful information for the relationship of specific diseases and genes from MEDLINE articles. Furthermore, we also applied natural language processing (NLP) methods to do the semantic analysis for the relevant articles to discover the relationships between genes and diseases.

Results:
We have extracted gene symbols from our literature collection according to disease MeSH classifications. We have also built an IR-based retrieval system, “Biomedical Literature Retrieval System (BLRS)“ and applied the N-gram model to extract the relationship features which can reveal the relationship between genes and diseases. Finally, a relationship network of a specific disease has been built to represent the gene-disease relationships.

Conclusions:
A relationship feature is a functional word that can reveal the relationship between one single gene and a disease. By incorporating many modern IR techniques, we found that BLRS is a very powerful information discovery tool for literature searching. A relationship network which contains the information on gene symbol, relationship feature, and disease MeSH term can provide an integrated view to discover gene-disease relationships.
A Global Socio-economic-medico-legal Model for the Sustainability of Longitudinal Electronic Health Records
Authors: Shabo A. 
Published: March 2018

Summary

Objectives:
This paper pursues the challenge of sustaining lifetime electronic health records (EHRs) based on a comprehensive socio-economic-medico-legal model. The notion of a lifetime EHR extends the emerging concept of a longitudinal and cross-institutional EHR and is invaluable information for increasing patient safety and quality of care.

Methods:
The challenge is how to compile and sustain a coherent EHR across the lifetime of an individual. Several existing and hypothetical models are described, analyzed and compared in an attempt to suggest a preferred approach.

Results:
The vision is that lifetime EHRs should be sustained by new players in the healthcare arena, who will function as independent health record banks (IHRBs). Multiple competing IHRBs would be established and regulated following preemptive legislation. They should be neither owned by healthcare providers nor by health insurer/payers or government agencies. The new legislation should also stipulate that the records located in these banks be considered the medico-legal copies of an individual’s records, and that healthcare providers no longer serve as the legal record keepers.

Conclusions:
The proposed model is not centered on any of the current players in the field; instead, it is focussed on the objective service of sustaining individual EHRs, much like financial banks maintain and manage financial assets. This revolutionary structure provides two main benefits: 1) Healthcare organizations will be able to cut the costs of long-term record keeping, and 2) healthcare providers will be able to provide better care based on the availability of a lifelong EHR of their new patients.
A Closed Queueing Network Approach to the Analysis of Patient Flow in Health Care Systems
Authors: Xie H. , Millard P. , Chaussalet T. J. 
Published: March 2018

Summary

Objectives:
To model patient flow in health care systems with bed capacity constraints in order to provide a useful decision aid for health service managers.

Methods:
We model the patient flow of health care systems using a closed queueing network framework with the assumption that the system is always full. Key performance measures of the health care system are also derived.

Results:
Using parameters taken from a study of a geriatric department in the UK, we show that the model is useful in helping service managers to gain better understanding of the behaviour of the system. In addition, we demonstrate that the model could help improving decision-making by allowing managers to explore different options and evaluate their impacts on performance. Our findings highlight the importance of policy makers taking into account the interactions between different phases of care.

Conclusions:
We have developed a novel approach to modelling the flow of patients through health care systems with constrained bed capacity.
The Common Data Elements for Cancer Research: Remarks on Functions and Structure
Authors: Brandt C. A. , Nadkarni P. M. 
Published: March 2018

Summary

Objectives:
The National Cancer Institute (NCI) has developed the Common Data Elements (CDE) to serve as a controlled vocabulary of data descriptors for cancer research, to facilitate data interchange and inter-oper-ability between cancer research centers. We evaluated CDE’s structure to see whether it could represent the elements necessary to support its intended purpose, and whether it could prevent errors and inconsistencies from being accidentally introduced. We also performed automated checks for certain types of content errors that provided a rough measure of curation quality.

Methods:
Evaluation was performed on CDE content downloaded via the NCI’s CDE Browser, and transformed into relational database form. Evaluation was performed under three categories: 1) compatibility with the ISO/IEC 11179 metadata model, on which CDE structure is based, 2) features necessary for controlled vocabulary support, and 3) support for a stated NCI goal, set up of data collection forms for cancer research.

Results:
Various limitations were identified both with respect to content (inconsistency, insufficient definition of elements, redundancy) as well as structure – particularly the need for term and relationship support, as well as the need for metadata supporting the explicit representation of electronic forms that utilize sets of common data elements.

Conclusions:
While there are numerous positive aspects to the CDE effort, there is considerable opportunity for improvement. Our recommendations include review of existing content by diverse experts in the cancer community; integration with the NCI thesaurus to take advantage of the latter’s links to nationally used controlled vocabularies, and various schema enhancements required for electronic form support.
A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis
Authors: Tzallas A. T. , Karvelis P. S. , Katsis C. D. , Giannopoulos S. , Konitsiotis S. , Fotiadis D. I. 
Published: March 2018

Summary

Objectives:
The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method.

Methods:
A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity.

Results:
The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases.

Conclusions:
The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.
Recurrent Neural Networks for Predicting Outcomes after Liver Transplantation: Representing Temporal Sequence of Clinical Observations
Authors: Doyle H. R. , Parmanto B. 
Published: March 2018

Summary

Objectives:
This paper investigates a version of recurrent neural network with the backpropagation through time (BPTT) algorithm for predicting liver transplant graft failure based on a time series sequence of clinical observations. The objective is to improve upon the current approaches to liver transplant outcome prediction by developing a more complete model that takes into account not only the preoperative risk assessment, but also the early postoperative history.

Methods:
A 6-fold cross-validation procedure was used to measure the performance of the networks. The data set was divided into a learning set and a test set by maintaining the same proportion of positive and negative cases in the original set. The effects of network complexity on overfitting were investigated by constructing two types of networks with different numbers of hidden units. For each type of network, 10 individual networks were trained on the learning set and used to form a committee. The performance of the networks was measured exhaustively with respect to both the entire training and test sets.

Results:
The networks were capable of learning the time series problem and achieved good performances of 90% correct classification on the learning set and 78% on the test set. The prediction accuracy increases as more information becomes progressively available after the operation with the daily improvement of 10% on the learning set and 5% on the test set.

Conclusions:
Recurrent neural networks trained with BPTT algorithm are capable of learning to represent temporal behavior of the time series prediction task. This model is an improvement upon the current model that does not take into account postoperative temporal information.
The CardioOP-Data Clas (CDC)
Authors: Klas W. , Westermann U. , Rose T. , Tremper J. , Stracke S. , Gödje O. , Hannekum A. , Preisack M. B. , Friedl R. 
Published: March 2018

Summary
Objectives: Self-directed and customized medical education programs are gaining importance in health care instruction. We prototypically implemented a repository-driven online computer system (CardioOP) for teleteaching in Heart Surgery. It supports authoring and multiple re-use of multimedia data for different user groups in different instructional applications and therefore requires a process of content management.
Methods: We defined objectives for a terminological system to support semantic, cross-media type annotation and retrieval of learning objects: domain completeness, German (natural) language processing, multi-user concepts, extensibility and maintenance, content based annotation and technical implementation. Existing terminologies (ICD10, READ V3, Snomed III, UMLS 1997, MESH) have been analysed according to these objectives.
Results: We found that the analysed terminologies did not meet our criteria sufficiently. Therefore, we developed a domain-specific thesaurus, the CardioOPDataClas (CDC). The application of the CDC within a database-driven authoring process using specifically developed tools is reported.
Conclusions: Metadata play an important role in the effective discovery and search, access, integration and management of educational multimedia data in medicine but so far, there is no terminology to support content management for instructional multimedia. We prototypically designed and applied a thesaurus for the CardioOP educational system. Additional work is needed to evaluate the system in terms of user-friendliness, concept coverage and information retrieval performance.
On the Design of a Generic and Scalable Multilayer Software Architecture for Data Flow Management in the Intensive Care Unit
Authors: De Turck F. , Vanhaste S. , Vandermeulen F. , Demeester P. , de Moor G. , Decruyenaere J. 
Published: March 2018

Summary
Objectives: The current Intensive Care Information Systems (IC-ISs) collect and store monitoring data in an automated way and can replace all paper forms by an electronic equivalent, resulting in a paperless ICU. Future development of IC-ISs will now have to focus on bedside clinical decision support. The current IC-ISs are data-driven systems, with a two-layer software architecture. This software architecture is hardly maintainable and probably not the most optimal architecture to make the transition towards future systems with decision support. The aim of this research was to address the design of an alternative software architecture based on new paradigms.
Methods: State-of-the art component, middleware and agent technology were deployed to design and implement a software architecture for ICU data flow management.
Results: An advanced multi-layer architecture for efficient data flow management in the ICU has been designed. The architecture is both generic and scalable, which means that it neither depends on a particular ICU nor on the deployed monitoring devices. Automatic device detection and Graphical User Interface generation are taken into account. Furthermore, a demonstrator has been developed as a proof that the proposed conceptual software architecture is feasible in practice. The core of the new architecture consists of Bed Decision Agents (BDAs). The introduction of BDAs, who perform specific dedicated tasks, improves the adaptability and maintainability of the future very complex IC-ISs.
Conclusions: A software architecture, based on component, middleware and agent technology, is feasible and offers important advantages over the currently used two-layer software architecture.
The Micro-Macro Spectrum of Medical Informatics Challenges: From Molecular Medicine to Transforming Health Care in a Globalizing Society
Authors: Kulikowski C. A. 
Published: March 2018

Summary
Background: Medical informatics has always encompassed a very broad spectrum of techniques for clinical and biomedical research, education and practice. There has been a concomitant variety of depth of specialization, ranging from the routine application of information processing methods to cutting-edge research on fundamental problems of computer-based systems and their relations to cognition and perception in biomedicine.
Objectives: Challenges for the field can be placed in perspective by considering the scale of each – from the highly detailed scientific problems in bioinformatics and emerging molecular medicine to the broad and complex social problems of introducing medical informatics into web-related global settings. Methods: The scale of an informatics problem is not only determined by the inherent physical space in which it exists, but also by the conceptual complexity that it involves, reinforcing the need to investigate the semantic web within which medical informatics is defined.
Results and Conclusion: Bioinformatics, biomedical imaging and language understanding provide examples that anchor research and practice in biomedical informatics at the detailed, scientific end of the spectrum. Traditional concerns of medical informatics in the clinical arena make up the broad mid-range of the spectrum, while novel social interaction models of competition and cooperation will be needed to understand the implications of distributed health information technology for individual and societal change in an increasingly interconnected world.
Challenges in Medical Informatics: Perspectives of an International Medical Informatics Organization
Authors: Lun K. C. 
Published: March 2018

Summary
Objective: As an international organization with the missions to promote informatics in health care and biomedical research, advance international cooperation, stimulate research, development and education, and disseminate and exchange information, the International Medical Informatics Association (IMIA) must be constantly cognizant of new developments in medical informatics and address the challenges to the discipline. From an international organization standpoint, it perceives three major challenges viz. the Identity, Organizational and Leadership challenges.
Method: This paper attempts to identify and discuss these challenges and to offer ways to overcome them through the activities of an international organization for medical informatics.
Results and Conclusion: From an international organization standpoint, IMIA can help overcome these organizational challenges by ensuring strong leadership throughout its echelon, actively promoting its goals and objectives worldwide through its national and institutional members as well as its regional groups and encouraging strategic partnerships between its many Working Groups and Special Interest Group on Nursing with other international organizations and industry to further promote the awareness and the perception of the relevance of medical informatics to health and medicine by the international community.
Terminology Tools: State of the Art and Practical Lessons
Authors: Cimino J. J. 
Published: March 2018

Summary

Objectives:
As controlled medical terminologies evolve from simple code-name-hierarchy arrangements, into rich, knowledge-based ontologies of medical concepts, increased demands are placed on both the developers and users of the terminologies. In response, researchers have begun developing tools to address their needs. The aims of this article are to review previous work done to develop these tools and then to describe work done at Columbia University and New York Presbyterian Hospital (NYPH).

Methods:
Researchers working with the Systematized Nomenclature of Medicine (SNOMED), the Unified Medical Language System (UMLS), and NYPH’s Medical Entities Dictionary (MED) have created a wide variety of terminology browsers, editors and servers to facilitate creation, maintenance and use of these terminologies.

Results:
Although much work has been done, no generally available tools have yet emerged. Consensus on requirement for tool functions, especially terminology servers is emerging. Tools at NYPH have been used successfully to support the integration of clinical applications and the merger of health care institutions.

Conclusions:
Significant advancement has occurred over the past fifteen years in the development of sophisticated controlled terminologies and the tools to support them. The tool set at NYPH provides a case study to demonstrate one feasible architecture.