bluuebunny/crossref_metadata_2025_split huggingface dataset:
A convex optimization approach for identification of human tissue-specific interactomes
Authors: Mohammadi Shahin , Grama Ananth 
Published: June 2016

AbstractMotivation: Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.Results: We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer’s and Parkinson’s diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.Availability and implementation: http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.htmlContact: mohammadi@purdue.edu
Fast metabolite identification with Input Output Kernel Regression
Authors: Brouard Céline , Shen Huibin , Dührkop Kai , d'Alché-Buc Florence , Böcker Sebastian , Rousu Juho 
Published: June 2016

AbstractMotivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space.Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods.Availability and implementation :Contact: celine.brouard@aalto.fiSupplementary information: Supplementary data are available at Bioinformatics online.
Analysis of aggregated cell–cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells
Authors: Schissler A. Grant , Li Qike , Chen James L. , Kenost Colleen , Achour Ikbel , Billheimer D. Dean , Li Haiquan , Piegorsch Walter W. , Lussier Yves A. 
Published: June 2016

AbstractMotivation: As ‘omics’ biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.Results: In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcriptome dynamics through the analysis of aggregated cell–cell statistical distances within biomolecular pathways. Cell–cell statistical distances are calculated from pathway mRNA fold changes between two cells. Within an elaborate case study of circulating tumor cells derived from prostate cancer patients, we develop analytic methods of aggregated distances to identify five differentially expressed pathways associated to therapeutic resistance. Our aggregation analyses perform comparably with Gene Set Enrichment Analysis and better than differentially expressed genes followed by gene set enrichment. However, these methods were not designed to inform on differential pathway expression for a single cell. As such, our framework culminates with the novel aggregation method, cell-centric statistics (CCS). CCS quantifies the effect size and significance of differentially expressed pathways for a single cell of interest. Improved rose plots of differentially expressed pathways in each cell highlight the utility of CCS for therapeutic decision-making.Availability and implementation: http://www.lussierlab.org/publications/CCS/Contact: yves@email.arizona.edu or piegorsch@math.arizona.eduSupplementary information: Supplementary data are available at Bioinformatics online.
BioASF: a framework for automatically generating executable pathway models specified in BioPAX
Authors: Haydarlou Reza , Jacobsen Annika , Bonzanni Nicola , Feenstra K. Anton , Abeln Sanne , Heringa Jaap 
Published: June 2016

Abstract
Motivation: Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models. However, a generic software framework for simulating BioPAX models is missing. Here, we attempt to fill this gap by introducing a generic simulation framework for BioPAX. The framework explicitly separates the execution model from the model structure as provided by BioPAX, with the advantage that the modelling process becomes more reproducible and intrinsically more modular; this ensures natural biological constraints are satisfied upon execution. The framework is based on the principles of discrete event systems and multi-agent systems, and is capable of automatically generating a hierarchical multi-agent system for a given BioPAX model.
Results: To demonstrate the applicability of the framework, we simulated two types of biological network models: a gene regulatory network modeling the haematopoietic stem cell regulators and a signal transduction network modeling the Wnt/β-catenin signaling pathway. We observed that the results of the simulations performed using our framework were entirely consistent with the simulation results reported by the researchers who developed the original models in a proprietary language.
Availability and Implementation: The framework, implemented in Java, is open source and its source code, documentation and tutorial are available at http://www.ibi.vu.nl/programs/BioASF.
Contact: j.heringa@vu.nl
Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways
Authors: Jo Kyuri , Jung Inuk , Moon Ji Hwan , Kim Sun 
Published: June 2016

Abstract
Motivation: To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools. Most pathway analysis methods are not designed for time series data and they do not consider gene-gene influence on the time dimension.
Results: In this article, we propose a novel time-series analysis method TimeTP for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein–protein interaction network to locate TFs triggering the perturbation. TimeTP first identifies perturbed sub-pathways that propagate the expression changes along the time. Starting points of the perturbed sub-pathways are mapped into the network and the most influential TFs are determined by influence maximization technique. The analysis result is visually summarized in TF-Pathway map in time clock . TimeTP was applied to PIK3CA knock-in dataset and found significant sub-pathways and their regulators relevant to the PIP3 signaling pathway.
Availability and Implementation: TimeTP is implemented in Python and available at http://biohealth.snu.ac.kr/software/TimeTP/ .
Supplementary information: Supplementary data are available at Bioinformatics online.
Contact: sunkim.bioinfo@snu.ac.kr
RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes
Authors: Srivastava Avi , Sarkar Hirak , Gupta Nitish , Patro Rob 
Published: June 2016

Abstract
Motivation: The alignment of sequencing reads to a transcriptome is a common and important step in many RNA-seq analysis tasks. When aligning RNA-seq reads directly to a transcriptome (as is common in the de novo setting or when a trusted reference annotation is available), care must be taken to report the potentially large number of multi-mapping locations per read. This can pose a substantial computational burden for existing aligners, and can considerably slow downstream analysis.
Results: We introduce a novel concept, quasi-mapping, and an efficient algorithm implementing this approach for mapping sequencing reads to a transcriptome. By attempting only to report the potential loci of origin of a sequencing read, and not the base-to-base alignment by which it derives from the reference, RapMap—our tool implementing quasi-mapping—is capable of mapping sequencing reads to a target transcriptome substantially faster than existing alignment tools. The algorithm we use to implement quasi-mapping uses several efficient data structures and takes advantage of the special structure of shared sequence prevalent in transcriptomes to rapidly provide highly-accurate mapping information. We demonstrate how quasi-mapping can be successfully applied to the problems of transcript-level quantification from RNA-seq reads and the clustering of contigs from de novo assembled transcriptomes into biologically meaningful groups.
Availability and implementation: RapMap is implemented in C ++11 and is available as open-source software, under GPL v3, at https://github.com/COMBINE-lab/RapMap .
Contact: rob.patro@cs.stonybrook.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
A novel algorithm for calling mRNA m
6
A peaks by modeling biological variances in MeRIP-seq data
Authors: Cui Xiaodong , Meng Jia , Zhang Shaowu , Chen Yidong , Huang Yufei 
Published: June 2016

Abstract
Motivation: N 6 -methyl-adenosine (m 6 A) is the most prevalent mRNA methylation but precise prediction of its mRNA location is important for understanding its function. A recent sequencing technology, known as Methylated RNA Immunoprecipitation Sequencing technology (MeRIP-seq), has been developed for transcriptome-wide profiling of m 6 A. We previously developed a peak calling algorithm called exomePeak. However, exomePeak over-simplifies data characteristics and ignores the reads’ variances among replicates or reads dependency across a site region. To further improve the performance, new model is needed to address these important issues of MeRIP-seq data.
Results: We propose a novel, graphical model-based peak calling method, MeTPeak, for transcriptome-wide detection of m 6 A sites from MeRIP-seq data. MeTPeak explicitly models read count of an m 6 A site and introduces a hierarchical layer of Beta variables to capture the variances and a Hidden Markov model to characterize the reads dependency across a site. In addition, we developed a constrained Newton’s method and designed a log-barrier function to compute analytically intractable, positively constrained Beta parameters. We applied our algorithm to simulated and real biological datasets and demonstrated significant improvement in detection performance and robustness over exomePeak. Prediction results on publicly available MeRIP-seq datasets are also validated and shown to be able to recapitulate the known patterns of m 6 A, further validating the improved performance of MeTPeak.
Availability and implementation: The package ‘MeTPeak’ is implemented in R and C ++, and additional details are available at https://github.com/compgenomics/MeTPeak
Contact: yufei.huang@utsa.edu or xdchoi@gmail.com
Supplementary information: Supplementary data are available at Bioinformatics online.
On tests of general relativity with binary radio pulsars
Authors: Del Pozzo W. , Vecchio A. 
Published: June 2016

Abstract
The timing of radio pulsars in binary systems provides a superb testing ground of general relativity. Here we propose a Bayesian approach to carry out these tests, and a relevant efficient numerical implementation, that has several conceptual and practical advantages with respect to traditional methods based on least-squares fit that have been used so far: (i) it accounts for the actual structure of the likelihood function – and it is not predicated on the Laplace approximation which is implicitly built in least-squares fit that can potentially bias the inference – (ii) it provides the ratio of the evidences of any two models under consideration as the statistical quantity to compare different theories, and (iii) it allows us to put joint constraints from the monitoring of multiple systems, that can be expressed in terms of ratio of evidences or probability intervals of global (thus not system-dependent) parameters of the theory, if any exists. Our proposed approach optimally exploits the progress in timing of radio pulsars and the increase in the number of observed systems. We demonstrate the power of this framework using simulated data sets that are representative of current observations.
Hard X-ray luminosity function of tidal disruption events: First results from the MAXI extragalactic survey
Authors: Kawamuro Taiki , Ueda Yoshihiro , Shidatsu Megumi , Hori Takafumi , Kawai Nobuyuki , Negoro Hitoshi , Mihara Tatehiro 
Published: June 2016

Abstract
We derive the first hard X-ray luminosity function (XLF) of stellar tidal disruption events (TDEs) by supermassive black holes (SMBHs), which gives an occurrence rate of TDEs per unit volume as a function of peak luminosity and redshift, utilizing an unbiased sample observed by the Monitor of All-sky X-ray Image (MAXI). On the basis of the light curves characterized by a power-law decay with an index of −5/3, a systematic search using the MAXI data detected four TDEs in the first 37 months of observations, all of which have been found in the literature. To formulate the TDE XLF, we consider the mass function of SMBHs, that of disrupted stars, the specific TDE rate as a function of SMBH mass, and the fraction of TDEs with relativistic jets. We perform an unbinned maximum likelihood fit to the MAXI TDE list and check the consistency with the observed TDE rate in the ROSAT all-sky survey. The results suggest that the intrinsic fraction of the jet-accompanying events is 0.0007%–34%. We confirm that at z ≲ 1.5 the contamination of the hard X-ray luminosity functions of active galactic nuclei by TDEs is not significant and hence that their contribution to the growth of SMBHs is negligible at the redshifts.
A History of Psychosis in Bipolar Disorder is Associated With Gray Matter Volume Reduction
Authors: Ekman Carl Johan , Petrovic Predrag , Johansson Anette G. M. , Sellgren Carl , Ingvar Martin , Landén Mikael 
Published: June 2016

Abstract
Psychotic symptoms are prevalent in schizophrenia, bipolar disorder, and other psychiatric and neurological disorders, yet the neurobiological underpinnings of psychosis remain obscure. In the last decade, a large number of magnetic resonance imaging studies have shown differences in local gray matter volume between patients with different psychiatric syndromes and healthy controls. Few studies have focused on the symptoms, which these syndromes are constituted of. Here, we test the association between psychosis and gray matter volume by using a sample of 167 subjects with bipolar disorder, with and without a history of psychosis, and 102 healthy controls. Magnetic resonance images were analyzed on group level using a voxel-wise mass univariate analysis (Voxel-Based Morphometry). We found that patients with a history of psychosis had smaller gray matter volume in left fusiform gyrus, the right rostral dorsolateral prefrontal cortex, and the left inferior frontal gyrus compared with patients without psychosis and with healthy controls. There was no volume difference in these areas between the no-psychosis group and healthy controls. These areas have previously been structurally and functionally coupled to delusions and hallucinations. Our finding adds further evidence to the probability of these regions as key areas in the development of psychotic symptoms.
New paradigms in the diagnosis and management of endometriosis
Authors: Kavoussi Shahryar K. , Lim Courtney S. , Skinner Bethany D. , Lebovic Dan I. , As-Sanie Sawsan 
Published: June 2016

Purpose of review
Endometriosis is a common gynecologic condition estimated to affect 10–15% of reproductive-aged women, 30% of women with subfertility, and 80% of women with chronic pelvic pain. Although mainstays of diagnosis and treatment are still commonly applied, there have been various advances in the modalities of diagnosis and management of this complex condition. This article provides an updated review of novel findings regarding the diagnosis and management of this challenging disease.

Recent findings
Despite an abundance of studies on noninvasive diagnostic markers for endometriosis, there is no single imaging study, biomarker or panel of biomarkers that has been validated for clinical diagnosis. New technologies, such as use of indocyanine green and fluorescence, which visualize neovascularization often associated with endometriosis may improve diagnostic detection of endometriosis at the time surgery, but have not been demonstrated to improve pain outcomes after surgery. Hormone suppression remains the mainstay therapy prior to and following surgery. Although most methods demonstrate similar efficacy in reducing endometriosis-associated pain, newer pharmacologic agents that may prove advantageous include oral gonadotropin receptor antagonists, selective progesterone receptor modulators, and angiogenesis inhibitors.

Summary
Although there have been some advances in the study of noninvasive imaging and biomarkers, more investigation into effective modalities are being conducted and are needed.
Patient experience and hospital profitability: Is there a link?
Authors: Richter Jason P. , Muhlestein David B. 
Published: June 2016

Background:
Patient experience has had a direct financial impact on hospitals since value-based purchasing was instituted by the Centers for Medicare & Medicaid Services in 2013 as a method to reward or punish hospitals based on performance on various measures, including patient experience. Although other industries have shown an indirect impact of customer experience on overall profitability, that link has not been well established in the health care industry. Return-to-provider rate and perceptions of health quality have been associated with profitability in the health care industry.

Purpose:
Our aims were to assess whether, independent of a direct financial impact, a more positive patient experience is associated with increased profitability and whether a more negative patient experience is associated with decreased profitability.

Methodology/Approach:
We used a sample of 19,792 observations from 3767 hospitals over the 6-year period 2007–2012. The data were sourced from Centers for Medicare & Medicaid Services and Hospital Consumer Assessment of Healthcare Providers and Systems. Using generalized estimating equations to account for repeated measures, we fit four separate models for three dependent variables: net patient revenue, net income, and operating margin. Each model included one of the following independent variables of interest: percentage of patients who definitely recommend the hospital, percentage of patients who definitely would not recommend the hospital, percentage of patients who rated the hospital 9 or 10, and percentage of patients who rated the hospital 6 or lower.

Findings:
We identified that a positive patient experience is associated with increased profitability and a negative patient experience is even more strongly associated with decreased profitability.

Practice Implications:
Management should have greater justification for incurring costs associated with bolstering patient experience programs. Improvements in training, technology, and staffing can be justified as a way to improve not only quality but now profitability as well.
RESOLUTION OF TREATMENT-RESISTANT SUBRETINAL FLUID IN A PATIENT WITH EXUDATIVE AGE-RELATED MACULAR DEGENERATION FOLLOWING ENDOPHTHALMITIS
Authors: Kally Peter M. , Sidikaro Yossi , McCannel Colin A. 
Published: June 2016

Purpose:
This is the first report to the best of the authors' knowledge to show resolution of subretinal fluid and treatment requirement in a case of exudative age-related macular degeneration (eAMD) with persistent fluid despite treatment that resolved following an episode of culture-positive bacterial endophthalmitis.

Methods:
A 73-year-old man with history of eAMD of the right eye presented with acute postinjection bacterial endophthalmitis 3 days after injection. He had a history of only partially treatment-responsive eAMD that had been treated over a period of 8 years.

Results:
After tap-and-inject treatment of endophthalmitis with ceftazidime, vancomycin, and dexamethasone, the patient returned for follow-up with visual improvement and resolution of the subretinal fluid. The previously treatment-resistant eAMD remains quiescent without further treatment after 10 months of follow-up.

Conclusion:
It is possible that some aspect of the infection, inflammation, or treatment of endophthalmitis had a disease-modifying impact on the eAMD. Further research into the components of endophthalmitis and its treatment may result in the discovery of new treatment approaches or treatment targets.