Mapping data elements to terminological resources for integrating biomedical data sources

Background

Data integration is a crucial task in the biomedical domain and integrating data sources is one approach to integrating data. Data elements (DEs) in particular play an important role in data integration. We combine schema- and instance-based approaches to mapping DEs to terminological resources in order to facilitate data sources integration.

Methods

We extracted DEs from eleven disparate biomedical sources. We compared these DEs to concepts and/or terms in biomedical controlled vocabularies and to reference DEs. We also exploited DE values to disambiguate underspecified DEs and to identify additional mappings.

Results

82.5% of the 474 DEs studied are mapped to entries of a terminological resource and 74.7% of the whole set can be associated with reference DEs. Only 6.6% of the DEs had values that could be semantically typed.

Conclusion

Our study suggests that the integration of biomedical sources can be achieved automatically with limited precision and largely facilitated by mapping DEs to terminological resources.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

PROPER: Performance visualization for optimizing and comparing ranking classifiers in MATLAB

Background

One of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research.

Results

To meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in the MATLAB environment.

Conclusion

PROPER is an efficient tool for optimization and comparison of ranking classifiers, providing over 20 different two- and three-dimensional performance curves.

     

Interactive processing and visualization of image data for biomedical and life science applications

Background

Applications in biomedical science and life science produce large data sets using increasingly powerful imaging devices and computer simulations. It is becoming increasingly difficult for scientists to explore and analyze these data using traditional tools. Interactive data processing and visualization tools can support scientists to overcome these limitations.

Results

We show that new data processing tools and visualization systems can be used successfully in biomedical and life science applications. We present an adaptive high-resolution display system suitable for biomedical image data, algorithms for analyzing and visualization protein surfaces and retinal optical coherence tomography data, and visualization tools for 3D gene expression data.

Conclusion

We demonstrated that interactive processing and visualization methods and systems can support scientists in a variety of biomedical and life science application areas concerned with massive data analysis.

     

NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics

Introduction

Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.

Objectives

The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.

Methods

NMRProcFlow was developed to be a graphical and interactive 1D NMR (¹H & ¹³C) spectra processing tool.

Results

NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.

Conclusion

Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

     

Comparison of named entity recognition methodologies in biomedical documents

Background

Biomedical named entity recognition (Bio-NER) is a fundamental task in handling biomedical text terms, such as RNA, protein, cell type, cell line, and DNA. Bio-NER is one of the most elementary and core tasks in biomedical knowledge discovery from texts. The system described here is developed by using the BioNLP/NLPBA 2004 shared task. Experiments are conducted on a training and evaluation set provided by the task organizers.

Results

Our results show that, compared with a baseline having a 70.09% F1 score, the RNN Jordan- and Elman-type algorithms have F1 scores of approximately 60.53% and 58.80%, respectively. When we use CRF as a machine learning algorithm, CCA, GloVe, and Word2Vec have F1 scores of 72.73%, 72.74%, and 72.82%, respectively.

Conclusions

By using the word embedding constructed through the unsupervised learning, the time and cost required to construct the learning data can be saved.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts

Background

Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.

Methods

We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer.

Results

Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%.

Conclusion

A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

     

Lexical adaptation of link grammar to the biomedical sublanguage: a comparative evaluation of three approaches

Background

We study the adaptation of Link Grammar Parser to the biomedical sublanguage with a focus on domain terms not found in a general parser lexicon. Using two biomedical corpora, we implement and evaluate three approaches to addressing unknown words: automatic lexicon expansion, the use of morphological clues, and disambiguation using a part-of-speech tagger. We evaluate each approach separately for its effect on parsing performance and consider combinations of these approaches.

Results

In addition to a 45% increase in parsing efficiency, we find that the best approach, incorporating information from a domain part-of-speech tagger, offers a statistically significant 10% relative decrease in error.

Conclusion

When available, a high-quality domain part-of-speech tagger is the best solution to unknown word issues in the domain adaptation of a general parser. In the absence of such a resource, surface clues can provide remarkably good coverage and performance when tuned to the domain. The adapted parser is available under an open-source license.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Variability in adherence to clinical practice guidelines and recommendations in COPD outpatients: a multi-level,cross-sectional analysis of the EPOCONSUL study

Background

Clinical audits have reported considerable variability in COPD medical care and frequent inconsistencies with recommendations. The objectives of this study were to identify factors associated with a better adherence to clinical practice guidelines and to explore determinants of this variability at the the hospital level.

Methods

EPOCONSUL is a Spanish nationwide clinical audit that evaluates the outpatient management of COPD. Multilevel logistic regression with two levels was performed to assess the relationships between individual and disease-related factors, as well as hospital characteristics.

Results

A total of 4508 clinical records of COPD patients from 59 Spanish hospitals were evaluated. High variability was observed among hospitals in terms of medical care. Some of the patient’s characteristics (airflow obstruction, degree of dyspnea, exacerbation risk, presence of comorbidities), the hospital factors (size and respiratory nurses available) and treatment at a specialized COPD outpatient clinic were identified as factors associated with a better adherence to recommendations, although this only explains a small proportion of the total variance.

Conclusion

To be treated at a specialized COPD outpatient clinic and some intrinsic patient characteristics were factors associated with a better adherence to guideline recommendations, although these variables were only explaining part of the high variability observed among hospitals in terms of COPD medical care.

     

Collagen and fibronectin surface modification of nanoporous anodic alumina and macroporous silicon for endothelial cell cultures

Background

The ability to direct the cellular response by means of biomaterial surface topography is important for biomedical applications. Substrate surface topography has been shown to be an effective cue for the regulation of cellular response. Here, the response of human aortic endothelial cells to nanoporous anodic alumina and macroporous silicon with collagen and fibronectin functionalization has been studied.

Methods

Confocal microscopy and scanning electron microscopy were employed to analyse the effects of the material and the porosity on the adhesion, morphology, and proliferation of the cells. Cell spreading and filopodia formation on macro- and nanoporous material was characterized by atomic force microscopy. We have also studied the influence of the protein on the adhesion.

Results

It was obtained the best results when the material is functionalized with fibronectin, regarding cells adhesion, morphology, and proliferation.

Conclusion

These results permit to obtain chemical modified 3D structures for several biotechnology applications such as tissue engineering, organ-on-chip or regenerative medicine.

     

Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN

Background

Imbalanced data classification is an inevitable problem in medical intelligent diagnosis. Most of real-world biomedical datasets are usually along with limited samples and high-dimensional feature. This seriously affects the classification performance of the model and causes erroneous guidance for the diagnosis of diseases. Exploring an effective classification method for imbalanced and limited biomedical dataset is a challenging task.

Methods

In this paper, we propose a novel multilayer extreme learning machine (ELM) classification model combined with dynamic generative adversarial net (GAN) to tackle limited and imbalanced biomedical data. Firstly, principal component analysis is utilized to remove irrelevant and redundant features. Meanwhile, more meaningful pathological features are extracted. After that, dynamic GAN is designed to generate the realistic-looking minority class samples, thereby balancing the class distribution and avoiding overfitting effectively. Finally, a self-adaptive multilayer ELM is proposed to classify the balanced dataset. The analytic expression for the numbers of hidden layer and node is determined by quantitatively establishing the relationship between the change of imbalance ratio and the hyper-parameters of the model. Reducing interactive parameters adjustment makes the classification model more robust.

Results

To evaluate the classification performance of the proposed method, numerical experiments are conducted on four real-world biomedical datasets. The proposed method can generate authentic minority class samples and self-adaptively select the optimal parameters of learning model. By comparing with W-ELM, SMOTE-ELM, and H-ELM methods, the quantitative experimental results demonstrate that our method can achieve better classification performance and higher computational efficiency in terms of ROC, AUC, G-mean, and F-measure metrics.

Conclusions

Our study provides an effective solution for imbalanced biomedical data classification under the condition of limited samples and high-dimensional feature. The proposed method could offer a theoretical basis for computer-aided diagnosis. It has the potential to be applied in biomedical clinical practice.

     

Pathway networks generated from human disease phenome

Background

Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. In this paper, we propose a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases.

Methods

We used a compilation of over 80,000 human genetic variants with known disease associations from databases including the Online Mendelian Inheritance in Man (OMIM), Clinical Variance database (ClinVar), Universal Protein Resource (UniProt), and Human Gene Mutation Database (HGMD). Furthermore, we used the Unified Medical Language System (UMLS) to normalize variant phenotype terminologies, mapping 87% of unique genetic variants to phenotypic disorder concepts. Lastly, variants were grouped by UMLS Medical Subject Heading (MeSH) identifiers to determine pathway enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

Results

By linking KEGG pathways through underlying variant associations, we elucidated connections between the human genetic variant-based disease phenome and metabolic pathways, finding novel disease connections not otherwise detected through gene-level analysis. When looking at broader disease categories, our network analysis showed that large complex diseases, such as cancers, are highly linked by their common pathways. In addition, we found Cardiovascular Diseases and Skin and Connective Tissue Diseases to have the highest number of common pathways, among 35 significant main disease category (MeSH) pairings.

Conclusions

This study constitutes an important contribution to extending disease-variant connections and new molecular links between diseases. Novel disease connections were made by disease-pathway associations not otherwise detected through single-gene analysis. For instance, we found that mutations in different genes associated to Noonan Syndrome and Essential Hypertension share a common pathway. This analysis also provides the foundation to build novel disease-drug networks through their underlying common metabolic pathways, thus enabling new diagnostic and therapeutic interventions.

     

A novel approach to minimize false discovery rate in genome-wide data analysis

Background

High-throughput technologies, such as DNA microarray, have significantly advanced biological and biomedical research by enabling researchers to carry out genome-wide screens. One critical task in analyzing genome-wide datasets is to control the false discovery rate (FDR) so that the proportion of false positive features among those called significant is restrained. Recently a number of FDR control methods have been proposed and widely practiced, such as the Benjamini-Hochberg approach, the Storey approach and Significant Analysis of Microarrays (SAM).

Methods

This paper presents a straight-forward yet powerful FDR control method termed miFDR, which aims to minimize FDR when calling a fixed number of significant features. We theoretically proved that the strategy used by miFDR is able to find the optimal number of significant features when the desired FDR is fixed.

Results

We compared miFDR with the BH approach, the Storey approach and SAM on both simulated datasets and public DNA microarray datasets. The results demonstrated that miFDR outperforms others by identifying more significant features under the same FDR cut-offs. Literature search showed that many genes called only by miFDR are indeed relevant to the underlying biology of interest.

Conclusions

FDR has been widely applied to analyzing high-throughput datasets allowed for rapid discoveries. Under the same FDR threshold, miFDR is capable to identify more significant features than its competitors at a compatible level of complexity. Therefore, it can potentially generate great impacts on biological and biomedical research.

Availability

If interested, please contact the authors for getting miFDR.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Energetic performance analysis of staged palliative surgery in tricuspid atresia using vector flow mapping

Background

Staged palliative surgery markedly shifts the balance of volume load on a single ventricle and pulmonary vascular bed. Blalock-Taussig shunt necessitates a single ventricle eject blood to both the systemic and pulmonary circulation. On the contrary, bidirectional cavopulmonary shunt release the single ventricle from pulmonary circulation.

Case presentation

We report a case of tricuspid atresia patient who underwent first palliative surgery and second palliative surgery. Volume loading condition was assessed by energetic parameters (energy loss, kinetic energy) intraoperatively using vector flow mapping. These energetic parameters can simply indicate the volume loading condition.

Conclusion

Vector flow mapping was useful tool for monitoring volume loading condition in congenital heart disease surgery.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Medical students and physical education students as CPR instructors: an appropriate solution to the CPR-instructor shortage in secondary schools?

Background

Integrating cardiopulmonary resuscitation (CPR) training in secondary schools will increase the number of potential CPR providers. However, currently too few certified instructors are available for this purpose. Training medical students and physical education student teachers to become CPR instructors could decrease this shortage.

Aim

Examine whether medical students and physical education student teachers can provide CPR training for secondary school pupils as well as (i.?e., non-inferior to) registered nurses.

Methods

A total of 144 secondary school pupils were randomly assigned to CPR training by a registered nurse (n = 12), a  medical student (n = 17) or a physical education student teacher (n = 15). CPR performance was assessed after training and after eight weeks in a simulated cardiac arrest scenario on a resuscitation manikin, using manikin software and video recordings.

Results

No significant differences were found between the groups on the overall Cardiff Test scores and the correctness of the CPR techniques during the post-training and retention test. All pupils showed sufficient CPR competence, even after eight weeks.

Conclusion

Training by medical students or physical education student teachers is non-inferior to training by a registered nurse, suggesting that school teachers, student teachers and medical students can be recruited for CPR training in secondary schools.