bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge
Authors: Sundin Iiris , Peltola Tomi , Micallef Luana , Afrabandpey Homayun , Soare Marta , Mamun Majumder Muntasir , Daee Pedram , He Chen , Serim Baris , Havulinna Aki , Heckman Caroline , Jacucci Giulio , Marttinen Pekka , Kaski Samuel 
Published: April 2018

AbstractMotivationPrecision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large.ResultsWe introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach.Availability and implementationSource code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine.Supplementary informationSupplementary data are available at Bioinformatics online.
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
Authors: Smaili Fatima Zohra , Gao Xin , Hoehndorf Robert 
Published: April 2018

Abstract

Motivation
Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications.

Results
We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein–protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved.

Availability and implementation
https://github.com/bio-ontology-research-group/onto2vec

Supplementary information
Supplementary data are available at Bioinformatics online.
Versatile genome assembly evaluation with QUAST-LG
Authors: Mikheenko Alla , Prjibelski Andrey , Saveliev Vladislav , Antipov Dmitry , Gurevich Alexey 
Published: April 2018

Abstract

Motivation
The emergence of high-throughput sequencing technologies revolutionized genomics in early 2000s. The next revolution came with the era of long-read sequencing. These technological advances along with novel computational approaches became the next step towards the automatic pipelines capable to assemble nearly complete mammalian-size genomes.

Results
In this manuscript, we demonstrate performance of the state-of-the-art genome assembly software on six eukaryotic datasets sequenced using different technologies. To evaluate the results, we developed QUAST-LG—a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference.

Availability and implementation
http://cab.spbu.ru/software/quast-lg

Supplementary information
Supplementary data are available at Bioinformatics online.
DisruPPI: structure-based computational redesign algorithm for protein binding disruption
Authors: Choi Yoonjoo , Furlon Jacob M , Amos Ryan B , Griswold Karl E , Bailey-Kellogg Chris 
Published: April 2018

Abstract

Motivation
Disruption of protein–protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein–Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.

Results
Two existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins.

Availability and implementation
DisruPPI is implemented in the EpiSweep package, freely available under an academic use license.

Supplementary information
Supplementary data are available at Bioinformatics online.
A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains
Authors: Her Hsuan-Lin , Wu Yu-Wei 
Published: April 2018

Abstract

Motivation
Antimicrobial resistance (AMR) is becoming a huge problem in both developed and developing countries, and identifying strains resistant or susceptible to certain antibiotics is essential in fighting against antibiotic-resistant pathogens. Whole-genome sequences have been collected for different microbial strains in order to identify crucial characteristics that allow certain strains to become resistant to antibiotics; however, a global inspection of the gene content responsible for AMR activities remains to be done.

Results
We propose a pan-genome-based approach to characterize antibiotic-resistant microbial strains and test this approach on the bacterial model organism Escherichia coli. By identifying core and accessory gene clusters and predicting AMR genes for the E. coli pan-genome, we not only showed that certain classes of genes are unevenly distributed between the core and accessory parts of the pan-genome but also demonstrated that only a portion of the identified AMR genes belong to the accessory genome. Application of machine learning algorithms to predict whether specific strains were resistant to antibiotic drugs yielded the best prediction accuracy for the set of AMR genes within the accessory part of the pan-genome, suggesting that these gene clusters were most crucial to AMR activities in E. coli. Selecting subsets of AMR genes for different antibiotic drugs based on a genetic algorithm (GA) achieved better prediction performances than the gene sets established in the literature, hinting that the gene sets selected by the GA may warrant further analysis in investigating more details about how E. coli fight against antibiotics.

Supplementary information
Supplementary data are available at Bioinformatics online.
CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling
Authors: Peng Hui , Zheng Yi , Blumenstein Michael , Tao Dacheng , Li Jinyan 
Published: April 2018

AbstractMotivationCRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single-guide RNAs (sgRNAs), such that its cleavage efficiency is high meanwhile the off-target effect is low.ResultsThis work proposed a two-step averaging method (TSAM) for the regression of cleavage efficiencies of a set of sgRNAs by averaging the predicted efficiency scores of a boosting algorithm and those by a support vector machine (SVM). We also proposed to use profiled Markov properties as novel features to capture the global characteristics of sgRNAs. These new features are combined with the outstanding features ranked by the boosting algorithm for the training of the SVM regressor. TSAM improved the mean Spearman correlation coefficiencies comparing with the state-of-the-art performance on benchmark datasets containing thousands of human, mouse and zebrafish sgRNAs. Our method can be also converted to make binary distinctions between efficient and inefficient sgRNAs with superior performance to the existing methods. The analysis reveals that highly efficient sgRNAs have lower melting temperature at the middle of the spacer, cut at 5’-end closer parts of the genome and contain more ‘A’ but less ‘G’ comparing with inefficient ones. Comprehensive further analysis also demonstrates that our tool can predict an sgRNA’s cutting efficiency with consistently good performance no matter it is expressed from an U6 promoter in cells or from a T7 promoter in vitro.Availability and implementationOnline tool is available at http://www.aai-bioinfo.com/CRISPR/. Python and Matlab source codes are freely available at https://github.com/penn-hui/TSAM.Supplementary informationSupplementary data are available at Bioinformatics online.
GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models
Authors: Clarke Thomas H , Brinkac Lauren M , Sutton Granger , Fouts Derrick E 
Published: April 2018

Abstract

Motivation
The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes.

Results
The Gaussian Genome Representative Selector with Prioritization (GGRaSP) R-package described below generates a reduced subset of genomes that prioritizes maintaining genomes of interest to the user as well as minimizing the loss of genetic variation. The package also allows for unsupervised clustering by modeling the genomic relationships using a Gaussian mixture model to select an appropriate cluster threshold. We demonstrate the capabilities of GGRaSP by generating a reduced list of 315 genomes from a genomic dataset of 4600 Escherichia coli genomes, prioritizing selection by type strain and by genome completeness.

Availability and implementaion
GGRaSP is available at https://github.com/JCVenterInstitute/ggrasp/.

Supplementary information
Supplementary data are available at Bioinformatics online.
DeepEfflux: a 2D convolutional neural network model for identifying families of efflux proteins in transporters
Authors: Taju Semmy Wellem , Nguyen Trinh-Trung-Duong , Le Nguyen-Quoc-Khanh , Kusuma Rosdyana Mangir Irawan , Ou Yu-Yen 
Published: April 2018

Abstract

Motivation
Efflux protein plays a key role in pumping xenobiotics out of the cells. The prediction of efflux family proteins involved in transport process of compounds is crucial for understanding family structures, functions and energy dependencies. Many methods have been proposed to classify efflux pump transporters without considerations of any pump specific of efflux protein families. In other words, efflux proteins protect cells from extrusion of foreign chemicals. Moreover, almost all efflux protein families have the same structure based on the analysis of significant motifs. The motif sequences consisting of the same amount of residues will have high degrees of residue similarity and thus will affect the classification process. Consequently, it is challenging but vital to recognize the structures and determine energy dependencies of efflux protein families. In order to efficiently identify efflux protein families with considering about pump specific, we developed a 2 D convolutional neural network (2 D CNN) model called DeepEfflux. DeepEfflux tried to capture the motifs of sequences around hidden target residues to use as hidden features of families. In addition, the 2 D CNN model uses a position-specific scoring matrix (PSSM) as an input. Three different datasets, each for one family of efflux protein, was fed into DeepEfflux, and then a 5-fold cross validation approach was used to evaluate the training performance.

Results
The model evaluation results show that DeepEfflux outperforms traditional machine learning algorithms. Furthermore, the accuracy of 96.02%, 94.89% and 90.34% for classes A, B and C, respectively, in the independent test results show that our model can perform well and can be used as a reliable tool for identifying families of efflux proteins in transporters.

Availability and implementation
The online version of deepefflux is available at http://deepefflux.irit.fr. The source code of deepefflux is available both on the deepefflux website and at http://140.138.155.216/deepefflux/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Predicting Clinical Outcomes Following Surgical Correction of Adult Spinal Deformity
Authors: Sharma Akshay , Tanenbaum Joseph E , Hogue Olivia , Mehdi Syed , Vallabh Sagar , Hu Emily , Benzel Edward C , Steinmetz Michael P , Savage Jason W 
Published: April 2018

Abstract

BACKGROUND
Deformity reconstruction surgery has been shown to improve quality of life (QOL) in cases of adult spinal deformity (ASD) but is associated with significant morbidity.

OBJECTIVE
To create a preoperative predictive nomogram to help risk-stratify patients and determine which would likely benefit from corrective surgery for ASD as measured by patient-reported health-related quality of life (HRQoL).

METHODS
All patients aged 25-yr and older with radiographic evidence of ASD and QOL data that underwent thoracolumbar fusion between 2008 and 2014 were identified. Demographic and clinical parameters were obtained. The EuroQol 5 dimensions questionnaire (EQ-5D) was used to measure HRQoL preoperatively and at 12-mo postoperative follow-up. Logistic regression of preoperative variables was used to create the prognostic nomogram.

RESULTS
Our sample included data from 191 patients. Fifty-one percent of patients experienced clinically relevant postoperative improvement in HRQoL. Seven variables were included in the final model: preoperative EQ-5D score, sex, preoperative diagnosis (degenerative, idiopathic, or iatrogenic), previous spinal surgical history, obesity, and a sex-by-obesity interaction term. Preoperative EQ-5D score independently predicted the outcome. Sex interacted with obesity: obese men were at disproportionately higher odds of improving than nonobese men, but obesity did not affect odds of the outcome among women. Model discrimination was good, with an optimism-adjusted c-statistic of 0.739.

CONCLUSION
The predictive nomogram that we developed using these data can improve preoperative risk counseling and patient selection for deformity correction surgery.
Associations Between Engagement and Outcomes in the SmokefreeTXT Program: A Growth Mixture Modeling Analysis
Authors: Coa Kisha I , Wiseman Kara P , Higgins Bryan , Augustson Erik 
Published: April 2018

Abstract

Introduction
Smoking continues to be a leading cause of preventable death. Mobile health (mHealth) can extend the reach of smoking cessation programs; however, user dropout, especially in real-world implementations of these programs, limit their potential effectiveness. Research is needed to understand patterns of engagement in mHealth cessation programs.

Methods
SmokefreeTXT (SFTXT) is the National Cancer Institute’s 6–8 week smoking cessation text-messaging intervention. Latent growth mixture modeling was used to identify unique classes of engagement among SFTXT users using real-world program data from 7090 SFTXT users. Survival analysis was conducted to model program dropout over time by class, and multilevel modeling was used to explore differences in abstinence over time.

Results
We identified four unique patterns of engagement groups. The largest percentage of users (61.6%) were in the low-engagers declining group; these users started off with low level of engagement and their engagement decreased over time. Users in this group were more likely to drop out from the program and less likely to be abstinent than users in the other groups. Users in the high engagers–maintaining group (ie, the smallest but most engaged group) were less likely to be daily smokers at baseline and were slightly older than those in the other groups. They were most likely to complete the program and report being abstinent.

Conclusions
Our findings show the importance of maintaining active engagement in text-based cessation programs. Future research is needed to elucidate predictors of the various levels of engagement, and to assess whether strategies aimed at increasing engagement result in higher abstinence rates.

Implications
The current study enabled us to investigate differing engagement patterns in non-incentivized program participants, which can help inform program modifications in real-world settings. Lack of engagement and dropout continue to impede the potential effectiveness of mHealth interventions, and understanding patterns and predictors of engagement can enhance the impact of these programs.
A Plague of Kinyounism: The Caricatures of Bacteriology in 1900 San Francisco
Authors: Engelmann Lukas 
Published: April 2018

Abstract
The arrival of bubonic plague in San Francisco in 1900 has become a pivotal case study in the history of American public health. The presence of plague remained contested for months as the evidence provided by the federal bacteriologist Joseph Kinyoun of the Marine Hospital Service was rejected, his laboratory methods disputed and his person ridiculed. Before the disease diagnosis became widely accepted, Kinyoun had been subjected to public caricature; his expensive and disruptive pragmatics for containing the epidemic were ridiculed as a plague of ‘Kinyounism’. Not only does this history offer insight into the difficult and contradictory ways in which bacteriology became an established science, it also provides an early twentieth-century example of ‘politicised science’. This paper revisits the controversy around Kinyoun and his bacteriological practice through the lens of caricature to sharpen the historical understanding of the shifting and shifty relationships between science, medicine, public health and politics.
Performance and Reproducibility of the Continuous Scale Physical Functional Performance 10 Test in Patients With Chronic Obstructive Pulmonary Disease
Authors: Stanzani Vera , José Anderson , de Oliveira Cristiane Helga Yamane , Boldorini Jacqueline Cristina , de Cordoba Lanza Fernanda , Dal Corso Simone , Malaguti Carla 
Published: April 2018

Purpose:
To compare the performances of patients with chronic obstructive pulmonary disease (COPD) and healthy individuals on the Continuous Scale Physical Functional Performance 10 (PFP10) test and, second, to test the reproducibility of the PFP10 test in these participants.

Methods:
In a prospective, cross-sectional study, 28 patients with COPD (67 ± 9 y, FEV1: 44.3 ± 16.9% pred) and 12 healthy volunteers (healthy group) matched by age, body mass index, and sex were included. The participants performed 2 PFP10 tests 5 d apart. Main outcome measure was performance on the PFP10 test and included total score, score for specific domains, and total time to perform the PFP10 test.

Results:
The functional performance of patients with COPD was worse than that of the healthy group on PFP10 total scores (43.9 ± 12.6 vs 63.5 ± 9.4, respectively; P < .001; effect size: 1.78) and time to complete the tasks (207.9 ± 36.1 sec vs 153.2 ± 12.1 sec, respectively; P < .001, effect size: 2.27). In addition, the COPD group had significantly worse performance in the domains of upper-body strength, lower-body strength, balance and coordination, and endurance. The test was highly reproducible (ICC: 0.98; 95% CI, 0.96-0.99 for both groups), and the mean difference (upper and lower limits of agreement) of the total scores was −3 (−9 to 3).

Conclusions:
The PFP10 test is a suitable and reproducible test for determining decreases in the functional performance of patients with COPD compared with healthy individuals.