bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prevalence of chronic wet cough and protracted bacterial bronchitis in Aboriginal children
Authors: Laird Pamela , Totterdell James , Walker Roz , Chang Anne B. , Schultz André 
Published: December 2019

BackgroundChronic wet cough, the most common symptom of a disease spectrum that encompasses protracted bacterial bronchitis (PBB) and bronchiectasis, is common among Aboriginal children. In the absence of any community prevalence data, and with the high burden of respiratory disease and the European Respiratory Society task force's recommendation to identify disease burden, we determined the prevalence of chronic wet cough and PBB in young Aboriginal children in four remote communities in north Western Australia.MethodsA whole-population, prospective study was conducted. Aboriginal children aged ≤7 years were clinically assessed for chronic wet cough by paediatric respiratory clinicians between July 2018 and May 2019. Where children had a wet cough but parents reported a short or uncertain cough duration, children were followed up 1 month later. A medical record audit 6 weeks to 3 months later was used to determine those children with chronic wet cough who had PBB (based on response to antibiotics).ResultsOf the 203 children, 191 (94%; median age 3.5 years, range 0–7 years) were enrolled. At the initial visit, chronic wet cough was present in 21 (11%), absent in 143 (75%) and unknown in 27 (14%). By follow-up, the total prevalence of chronic wet cough was 13% (95% CI 8–19%) and 10% (95% CI 7–17%) for PBB. Chronic wet cough was more common in the two communities with unsealed roads (19%) compared to the two with sealed roads (7%).ConclusionGiven the relatively high prevalence, strategies to address reasons for and treatment of chronic wet cough and PBB in young Aboriginal children in remote north Western Australia are required.
Prevalence of comorbidities and impact on pulmonary rehabilitation outcomes
Authors: Butler Stacey J. , Li Lok Sze Katrina , Ellerton Lauren , Gershon Andrea S. , Goldstein Roger S. , Brooks Dina 
Published: December 2019

BackgroundThe presence of comorbid conditions could impact performance in pulmonary rehabilitation (PR) programmes. We aimed to compare the comorbidity prevalence among those with chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) and evaluate the impact on PR response.MethodsWe performed a retrospective cohort study, recording comorbidities for all patients with COPD or ILD referred to PR. Participants were classified as responders to PR if they met the minimal important difference for exercise capacity and health-related quality of life (HRQoL). The prevalence of comorbidities and impact on PR outcomes were compared by lung disease and by sex using a univariate analysis and multivariate logistic regression.ResultsThe mean number of comorbidities was similar among those with COPD (3.3±2.1, n=242) and ILD (3.2±1.9, n=66) (p>0.05). Females had a higher number of comorbidities than males in both COPD (p=0.001) and ILD (p=0.017) populations. Circulatory (64%) and endocrine/metabolic (45%) conditions were most common in COPD. In ILD, digestive (55%) and circulatory (53%) comorbidities were most prevalent. In people with ILD, those over 65 years, with musculoskeletal/connective tissue disease or circulatory disease were less likely to obtain meaningful improvements in exercise capacity. There was no impact of comorbidities on exercise capacity in COPD or on HRQoL in ILD.ConclusionsThe majority of patients with COPD or ILD enrolled in PR programmes have multiple comorbidities that may affect improvements in exercise capacity. PR programmes may be less effective for older adults with ILD and comorbid circulatory or musculoskeletal disease.
Intrinsically disordered domains deviate significantly from random sequences in mammalian proteins
Authors: Teraguchi Shunsuke , Patil Ashwini , Standley Daron M 
Published: December 2019

Abstract

Background
In order to characterize mammalian intrinsically disordered domains (IDDs) we examined the patterns in their amino acid abundance as well as overrepresented local sequence motifs. We considered IDDs from mouse proteins associated with innate immune responses as well as a set of generic human genes. These sets were compared with artificially generated random sequences with the same overall amino acid abundance and length distributions. IDDs were then clustered by amino acid abundance, and further analyzed in terms of co-occurrence of clusters with functionally characterized Pfam domains.

Results
Overall, IDDs were very different from randomly generated sequences. The deviation from random distributions was at least as great as that for ordered domains, for which the deviation can be rationalized in terms of strong evolutionary pressure for structure and function. The co-occurrence of certain Pfam domains with specific IDD clusters was found to be significant (p-value < 0.01). Local sequence motifs that were over-represented in the innate immune set consisted mostly of low complexity fragments, primarily characterized by amino acid repeats, and could not be assigned an obvious functional role.

Conclusions
Our results suggest that IDDs are constrained within a narrow subset of possible sequences. This is most likely a result of biophysical restraints that have yet to be elucidated. More detailed examination of the functional relationship between the IDDs and associated Pfam domains is one possible avenue of investigation.
Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition
Authors: Huang Hui-Ling , Charoenkwan Phasit , Kao Te-Fen , Lee Hua-Chin , Chang Fang-Lin , Huang Wen-Lin , Ho Shinn-Jang , Shu Li-Sun , Chen Wen-Liang , Ho Shinn-Ying 
Published: December 2019

Abstract

Background
Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods.

Results
This study proposes a novel scoring card method (SCM) by using dipeptide composition only to estimate solubility scores of sequences for predicting protein solubility. SCM calculates the propensities of 400 individual dipeptides to be soluble using statistic discrimination between soluble and insoluble proteins of a training data set. Consequently, the propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The solubility score of a sequence is determined by the weighted sum of all propensity scores and dipeptide composition. To evaluate SCM by performance comparisons, four data sets with different sizes and variation degrees of experimental conditions were used. The results show that the simple method SCM with interpretable propensities of dipeptides has promising performance, compared with existing SVM-based ensemble methods with a number of feature types. Furthermore, the propensities of dipeptides and solubility scores of sequences can provide insights to protein solubility. For example, the analysis of dipeptide scores shows high propensity of α-helix structure and thermophilic proteins to be soluble.

Conclusions
The propensities of individual dipeptides to be soluble are varied for proteins under altered experimental conditions. For accurately predicting protein solubility using SCM, it is better to customize the score card of dipeptide propensities by using a training data set under the same specified experimental conditions. The proposed method SCM with solubility scores and dipeptide propensities can be easily applied to the protein function prediction problems that dipeptide composition features play an important role.

Availability
The used datasets, source codes of SCM, and supplementary files are available at http://iclab.life.nctu.edu.tw/SCM/.
ChemEx: information extraction system for chemical data curation
Authors: Tharatipyakul Atima , Numnark Somrak , Wichadakul Duangdao , Ingsriswang Supawadee 
Published: December 2019

Abstract

Background
Manual chemical data curation from publications is error-prone, time consuming, and hard to maintain up-to-date data sets. Automatic information extraction can be used as a tool to reduce these problems. Since chemical structures usually described in images, information extraction needs to combine structure image recognition and text mining together.

Results
We have developed ChemEx, a chemical information extraction system. ChemEx processes both text and images in publications. Text annotator is able to extract compound, organism, and assay entities from text content while structure image recognition enables translation of chemical raster images to machine readable format. A user can view annotated text along with summarized information of compounds, organism that produces those compounds, and assay tests.

Conclusions
ChemEx facilitates and speeds up chemical data curation by extracting compounds, organisms, and assays from a large collection of publications. The software and corpus can be downloaded from http://www.biotec.or.th/isl/ChemEx.
Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data
Authors: Xiang Yang , Zhang Cun-Quan , Huang Kun 
Published: December 2019

Abstract

Background
Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted gene co-expression network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of gene co-expression networks which are related to GBM prognosis.

Methods
We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-network in WGCN. Each sub-network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of genes are carried out to determine the thresholds for log-rank test p-values for network selection. Sub-networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using gene ontology enrichment analysis.

Results
Using the eQCM algorithm, we identified 8,124 sub-networks in the WGCN, out of which 170 sub-networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters.

Conclusions
We identified 16 gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epigenetic events in GBM development and prognosis.
Bayesian probabilistic network modeling from multiple independent replicates
Authors: Patton Kristopher L , John David J , Norris James L 
Published: December 2019

AbstractOften protein (or gene) time-course data are collected for multiple replicates. Each replicate generally has sparse data with the number of time points being less than the number of proteins. Usually each replicate is modeled separately. However, here all the information in each of the replicates is used to make a composite inference about signal networks. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the networks. In particular, when the edge's partial correlation between two proteins is at least moderate, then the composite's posterior probability is large.
SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS
Authors: Merelli Ivan , Calabria Andrea , Cozzi Paolo , Viti Federica , Mosca Ettore , Milanesi Luciano 
Published: December 2019

AbstractBackgroundThe capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.ResultsWe propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using agene-centricdatabase design, (2) on the evaluation of a set of static biomolecular annotations, defined asfeatures, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool calledSNPranker 2.0(http://www.itb.cnr.it/snpranker), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores.ConclusionsDifferent databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.
Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles
Authors: Zhu Jie , Qin Yufang , Liu Taigang , Wang Jun , Zheng Xiaoqi 
Published: December 2019

Abstract

Background
Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance.

Results
In this paper, a new diffusion-based method was proposed to prioritize candidate disease genes. Diffusion profile of a disease was defined as the stationary distribution of candidate genes given a random walk with restart where similarities between phenotypes are incorporated. Then, candidate disease genes are prioritized by comparing their diffusion profiles with that of the disease. Finally, the effectiveness of our method was demonstrated through the leave-one-out cross-validation against control genes from artificial linkage intervals and randomly chosen genes. Comparative study showed that our method achieves improved performance compared to some classical diffusion-based methods. To further illustrate our method, we used our algorithm to predict new causing genes of 16 multifactorial diseases including Prostate cancer and Alzheimer's disease, and the top predictions were in good consistent with literature reports.

Conclusions
Our study indicates that integration of multiple information sources, especially the phenotype similarity profile data, and introduction of global similarity measure between disease and gene diffusion profiles are helpful for prioritizing candidate disease genes.

Availability
Programs and data are available upon request.
Advances in translational bioinformatics and population genomics in the Asia-Pacific
Authors: Ranganathan Shoba , Tongsima Sissades , Chan Jonathan , Tan Tin Wee , Schönbach Christian 
Published: December 2019

Abstract
The theme of the 2012 International Conference on Bioinformatics (InCoB) in Bangkok, Thailand was "From Biological Data to Knowledge to Technological Breakthroughs." Besides providing a forum for life scientists and bioinformatics researchers in the Asia-Pacific region to meet and interact, the conference also hosted thematic sessions on the Pan-Asian Pacific Genome Initiative and immunoinformatics. Over the seven years of conference papers published in BMC Bioinformatics and four years in BMC Genomics, we note that there is increasing interest in the applications of -omics technologies to the understanding of diseases, as a forerunner to personalized genomic medicine.
Detecting the borders between coding and non-coding DNA regions in prokaryotes based on recursive segmentation and nucleotide doublets statistics
Authors: Deng Suping , Shi Yixiang , Yuan Liyun , Li Yixue , Ding Guohui 
Published: December 2019

Abstract

Background
Detecting the borders between coding and non-coding regions is an essential step in the genome annotation. And information entropy measures are useful for describing the signals in genome sequence. However, the accuracies of previous methods of finding borders based on entropy segmentation method still need to be improved.

Methods
In this study, we first applied a new recursive entropic segmentation method on DNA sequences to get preliminary significant cuts. A 22-symbol alphabet is used to capture the differential composition of nucleotide doublets and stop codon patterns along three phases in both DNA strands. This process requires no prior training datasets.

Results
Comparing with the previous segmentation methods, the experimental results on three bacteria genomes, Rickettsia prowazekii, Borrelia burgdorferi and E.coli, show that our approach improves the accuracy for finding the borders between coding and non-coding regions in DNA sequences.

Conclusions
This paper presents a new segmentation method in prokaryotes based on Jensen-Rényi divergence with a 22-symbol alphabet. For three bacteria genomes, comparing to A12_JR method, our method raised the accuracy of finding the borders between protein coding and non-coding regions in DNA sequences.
Analysis of high-throughput RNAi screening data in identifying genes mediating sensitivity to chemotherapeutic drugs: statistical approaches and perspectives
Authors: Ye Fei , Bauer Joshua A , Pietenpol Jennifer A , Shyr Yu 
Published: December 2019

Abstract

Background
High-throughput RNA interference (RNAi) screens have been used to find genes that, when silenced, result in sensitivity to certain chemotherapy drugs. Researchers therefore can further identify drug-sensitive targets and novel drug combinations that sensitize cancer cells to chemotherapeutic drugs. Considerable uncertainty exists about the efficiency and accuracy of statistical approaches used for RNAi hit selection in drug sensitivity studies. Researchers require statistical methods suitable for analyzing high-throughput RNAi screening data that will reduce false-positive and false-negative rates.

Results
In this study, we carried out a simulation study to evaluate four types of statistical approaches (fold-change/ratio, parametric tests/statistics, sensitivity index, and linear models) with different scenarios of RNAi screenings for drug sensitivity studies. With the simulated datasets, the linear model resulted in significantly lower false-negative and false-positive rates. Based on the results of the simulation study, we then make recommendations of statistical analysis methods for high-throughput RNAi screening data in different scenarios. We assessed promising methods using real data from a loss-of-function RNAi screen to identify hits that modulate paclitaxel sensitivity in breast cancer cells. High-confidence hits with specific inhibitors were further analyzed for their ability to inhibit breast cancer cell growth. Our analysis identified a number of gene targets with inhibitors known to enhance paclitaxel sensitivity, suggesting other genes identified may merit further investigation.

Conclusions
RNAi screening can identify druggable targets and novel drug combinations that can sensitize cancer cells to chemotherapeutic drugs. However, applying an inappropriate statistical method or model to the RNAi screening data will result in decreased power to detect the true hits and increase false positive and false negative rates, leading researchers to draw incorrect conclusions. In this paper, we make recommendations to enable more objective selection of statistical analysis methods for high-throughput RNAi screening data.
Redistribution of H3K4me2 on neural tissue specific genes during mouse brain development
Authors: Zhang Jie , Parvin Jeffrey , Huang Kun 
Published: December 2019

Abstract

Background
Histone modification plays an important role in cell differentiation and tissue development. A recent study has shown that the dimethylation of lysine 4 residue on histone 3 (H3K4me2) marks the gene body area of tissue specific genes in the human CD4+ T cells and neural cells. However, little is known of the H3k4me2 distribution dynamics through the cell differentiation and tissue development.

Results
We applied several clustering methods including K-means, hierarchical and principle component analysis on H3K4me2 ChIP-seq data from embryonic stem cell, neural progenitor cell and whole brain of mouse, trying to identify genes with the H3K4me2 binding on the gene body region in different cell development stage and study their redistribution in different tissue development stages.
A cluster of 356 genes with heavy H3K4me2 labeling in the gene body region was identified in the mouse whole brain tissue using K-means clustering. They are highly enriched with neural system related functions and pathways, and are involved in several central neural system diseases. The distribution of H3K4me2 on neural function related genes follows three distinctive patterns: a group of genes contain constant heavy H3K4me2 marks in the gene body from embryonic stem cell stage through neural progenitor stage to matured brain tissue stage; another group of gene have little H3K4me2 marks until cells mature into brain cells; the majority of the genes acquired H3K4me2 marks in the neural progenitor cell stage, and gain heavy labeling in the matured brain cell stage. Gene ontology enrichment analysis also revealed corresponding gene ontology terms that fit in the scenario of each cell developmental stages.

Conclusions
We investigated the process of the H3K4me2 mark redistribution during tissue specificity development for mouse brain tissue. Our analysis confirmed the previous report that heavy labeling of H3K4me2 in the downstream of TSS marks tissue specific genes. These genes show remarkable enrichment in central neural system related diseases. Furthermore, we have shown that H3K4me2 labeling can be acquired as early as the embryonic stem cell stage, and its distribution is dynamic and progressive throughout cell differentiation and tissue development.