bluuebunny/crossref_metadata_2025_split huggingface dataset:
LocTree2 predicts localization for all domains of life
Authors: Goldberg Tatyana , Hamp Tobias , Rost Burkhard 
Published: September 2012

Abstract
Motivation: Subcellular localization is one aspect of protein function. Despite advances in high-throughput imaging, localization maps remain incomplete. Several methods accurately predict localization, but many challenges remain to be tackled.
Results: In this study, we introduced a framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data.
Availability: Online through PredictProtein (predictprotein.org); as standalone version at http://www.rostlab.org/services/loctree2.
Contact: localization@rostlab.org
Supplementary Information: Supplementary data are available at Bioinformatics online.
ReLiance: a machine learning and literature-based prioritization of receptor—ligand pairings
Authors: Iacucci Ernesto , Tranchevent Léon-Charles , Popovic Dusan , Pavlopoulos Georgios A. , De Moor Bart , Schneider Reinhard , Moreau Yves 
Published: September 2012

Abstract
Motivation: The prediction of receptor—ligand pairings is an important area of research as intercellular communications are mediated by the successful interaction of these key proteins. As the exhaustive assaying of receptor—ligand pairs is impractical, a computational approach to predict pairings is necessary. We propose a workflow to carry out this interaction prediction task, using a text mining approach in conjunction with a state of the art prediction method, as well as a widely accessible and comprehensive dataset.
Among several modern classifiers, random forests have been found to be the best at this prediction task. The training of this classifier was carried out using an experimentally validated dataset of Database of Ligand-Receptor Partners (DLRP) receptor—ligand pairs. New examples, co-cited with the training receptors and ligands, are then classified using the trained classifier. After applying our method, we find that we are able to successfully predict receptor—ligand pairs within the GPCR family with a balanced accuracy of 0.96. Upon further inspection, we find several supported interactions that were not present in the Database of Interacting Proteins (DIPdatabase).
We have measured the balanced accuracy of our method resulting in high quality predictions stored in the available database ReLiance.
Availability: http://homes.esat.kuleuven.be/~bioiuser/ReLianceDB/index.php
Contact: yves.moreau@esat.kuleuven.be; ernesto.iacucci@gmail.com
Supplementary information: Supplementary data are available at Bioinformatics online.
Fractionation, rearrangement and subgenome dominance
Authors: Sankoff David , Zheng Chunfang 
Published: September 2012

Abstract
Motivation: Fractionation is arguably the greatest cause of gene order disruption following whole genome duplication, causing severe biases in chromosome rearrangement-based estimates of evolutionary divergence.
Results: We show how to correct for this bias almost entirely by means of a ‘consolidation’ algorithm for detecting and suitably transforming identifiable regions of fractionation. We characterize the process of fractionation and the performance of the algorithm through realistic simulations. We apply our method to a number of core eudicot genomes, we and by studying the fractionation regions detected, are able to address topical issues in polyploid evolution.
Availability and implementation: Code for the consolidation algorithm, and sample data, is available at: http://137.122.149.195/Software/Fractionation/fractionation.html
Contact: sankoff@uottawa.ca
An accurate paired sample test for count data
Authors: Pham Thang V. , Jimenez Connie R. 
Published: September 2012

AbstractMotivation: Recent technology platforms in proteomics and genomics produce count data for quantitative analysis. Previous works on statistical significance analysis for count data have mainly focused on the independent sample setting, which does not cover the case where pairs of measurements are taken from individual patients before and after treatment. This experimental setting requires paired sample testing such as the paired t-test often used for continuous measurements. A state-of-the-art method uses a negative binomial distribution in a generalized linear model framework for paired sample testing. A paired sample design assumes that the relative change within each pair is constant across biological samples. This model can be used as an approximation to the true model in cases of heterogeneity of response in complex biological systems. We aim to specify the variation in response explicitly in combination with the inherent technical variation.Results: We formulate the problem of paired sample test for count data in a framework of statistical combination of multiple contingency tables. In particular, we specify explicitly a random distribution for the effect with an inverted beta model. The technical variation can be modeled by either a standard Poisson distribution or an exponentiated Poisson distribution, depending on the reproducibility of the acquisition workflow. The new statistical test is evaluated on both proteomics and genomics datasets, showing a comparable performance to the state-of-the-art method in general, and in several cases where the two methods differ, the proposed test returns more reasonable p-values.Availability: Available for download at http://www.oncoproteomics.nl/.Contact: t.pham@vumc.nl
Telescoper: de novo assembly of highly repetitive regions
Authors: Bresler Ma'ayan , Sheehan Sara , Chan Andrew H. , Song Yun S. 
Published: September 2012

Abstract
Motivation: With advances in sequencing technology, it has become faster and cheaper to obtain short-read data from which to assemble genomes. Although there has been considerable progress in the field of genome assembly, producing high-quality de novo assemblies from short-reads remains challenging, primarily because of the complex repeat structures found in the genomes of most higher organisms. The telomeric regions of many genomes are particularly difficult to assemble, though much could be gained from the study of these regions, as their evolution has not been fully characterized and they have been linked to aging.
Results: In this article, we tackle the problem of assembling highly repetitive regions by developing a novel algorithm that iteratively extends long paths through a series of read-overlap graphs and evaluates them based on a statistical framework. Our algorithm, Telescoper, uses short- and long-insert libraries in an integrated way throughout the assembly process. Results on real and simulated data demonstrate that our approach can effectively resolve much of the complex repeat structures found in the telomeres of yeast genomes, especially when longer long-insert libraries are used.
Availability: Telescoper is publicly available for download at sourceforge.net/p/telescoper.
Contact: yss@eecs.berkeley.edu
Supplementary Information: Supplementary data are available at Bioinformatics online.
Accurate estimation of short read mapping quality for next-generation genome sequencing
Authors: Ruffalo Matthew , Koyutürk Mehmet , Ray Soumya , LaFramboise Thomas 
Published: September 2012

Abstract
Motivation: Several software tools specialize in the alignment of short next-generation sequencing reads to a reference sequence. Some of these tools report a mapping quality score for each alignment—in principle, this quality score tells researchers the likelihood that the alignment is correct. However, the reported mapping quality often correlates weakly with actual accuracy and the qualities of many mappings are underestimated, encouraging the researchers to discard correct mappings. Further, these low-quality mappings tend to correlate with variations in the genome (both single nucleotide and structural), and such mappings are important in accurately identifying genomic variants.
Approach: We develop a machine learning tool, LoQuM (LOgistic regression tool for calibrating the Quality of short read mappings, to assign reliable mapping quality scores to mappings of Illumina reads returned by any alignment tool. LoQuM uses statistics on the read (base quality scores reported by the sequencer) and the alignment (number of matches, mismatches and deletions, mapping quality score returned by the alignment tool, if available, and number of mappings) as features for classification and uses simulated reads to learn a logistic regression model that relates these features to actual mapping quality.
Results: We test the predictions of LoQuM on an independent dataset generated by the ART short read simulation software and observe that LoQuM can ‘resurrect’ many mappings that are assigned zero quality scores by the alignment tools and are therefore likely to be discarded by researchers. We also observe that the recalibration of mapping quality scores greatly enhances the precision of called single nucleotide polymorphisms.
Availability: LoQuM is available as open source at http://compbio.case.edu/loqum/.
Contact: matthew.ruffalo@case.edu.
Drug target prediction using adverse event report systems: a pharmacogenomic approach
Authors: Takarabe Masataka , Kotera Masaaki , Nishimura Yosuke , Goto Susumu , Yamanishi Yoshihiro 
Published: September 2012

Abstract
Motivation: Unexpected drug activities derived from off-targets are usually undesired and harmful; however, they can occasionally be beneficial for different therapeutic indications. There are many uncharacterized drugs whose target proteins (including the primary target and off-targets) remain unknown. The identification of all potential drug targets has become an important issue in drug repositioning to reuse known drugs for new therapeutic indications.
Results: We defined pharmacological similarity for all possible drugs using the US Food and Drug Administration's (FDA's) adverse event reporting system (AERS) and developed a new method to predict unknown drug–target interactions on a large scale from the integration of pharmacological similarity of drugs and genomic sequence similarity of target proteins in the framework of a pharmacogenomic approach. The proposed method was applicable to a large number of drugs and it was useful especially for predicting unknown drug–target interactions that could not be expected from drug chemical structures. We made a comprehensive prediction for potential off-targets of 1874 drugs with known targets and potential target profiles of 2519 drugs without known targets, which suggests many potential drug–target interactions that were not predicted by previous chemogenomic or pharmacogenomic approaches.
Availability: Softwares are available upon request.
Contact: yamanishi@bioreg.kyushu-u.ac.jp
Supplementary Information: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/aers/.
MaConDa: a publicly accessible mass spectrometry contaminants database
Authors: Weber Ralf J. M. , Li Eva , Bruty Jonathan , He Shan , Viant Mark R. 
Published: September 2012

Abstract
Mass spectrometry is widely used in bioanalysis, including the fields of metabolomics and proteomics, to simultaneously measure large numbers of molecules in complex biological samples. Contaminants routinely occur within these samples, for example, originating from the solvents or plasticware. Identification of these contaminants is crucial to enable their removal before data analysis, in particular to maintain the validity of conclusions drawn from uni- and multivariate statistical analyses. Although efforts have been made to report contaminants within mass spectra, this information is fragmented and its accessibility is relatively limited. In response to the needs of the bioanalytical community, here we report the creation of an extensive manually well-annotated database of currently known small molecule contaminants.
Availability: The Mass spectrometry Contaminants Database (MaConDa) is freely available and accessible through all major browsers or by using the MaConDa web service http://www.maconda.bham.ac.uk.
Contact: m.viant@bham.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text
Authors: French Leon , Lane Suzanne , Xu Lydia , Siu Celia , Kwok Cathy , Chen Yiqi , Krebs Claudia , Pavlidis Paul 
Published: September 2012

Abstract
Motivation: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large-scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching and integration of the reports. We annotated a set of 1377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features.
Results: Co-occurrence based methods provided high recall with weak precision. The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision. Owing to its speed and simplicity, we applied the shallow linguistic kernel to a large set of new abstracts. To evaluate the results, we compared 2688 extracted connections with the Brain Architecture Management System (an existing database of rat connectivity). The extracted connections were connected in the Brain Architecture Management System at a rate of 63.5%, compared with 51.1% for co-occurring brain region pairs. We found that precision increases with the recency and frequency of the extracted relationships.
Availability and implementation: The source code, evaluations, documentation and other supplementary materials are available at http://www.chibi.ubc.ca/WhiteText.
Contact: paul@chibi.ubc.ca
Supplementary information: Supplementary data are available at Bioinformatics Online.
Classical to quantum in large-number limit
Authors: Modi Kavan , Fazio Rosario , Pascazio Saverio , Vedral Vlatko , Yuasa Kazuya 
Published: September 2012

We construct a quantumness witness following the work of Alicki & van Ryn (AvR). We reformulate the AvR test by defining it for quantum states rather than for observables. This allows us to identify the necessary quantities and resources to detect quantumness for any given system. The first quantity turns out to be the purity of the system. When applying the witness to a system with even moderate mixedness, the protocol is unable to reveal any quantumness. We then show that having many copies of the system leads the witness to reveal quantumness. This seems contrary to the Bohr correspondence, which asserts that, in the large-number limit, quantum systems become classical, whereas the witness shows quantumness when several non-quantum systems, as determined by the witness, are considered together. However, the resources required to detect the quantumness increase dramatically with the number of systems. We apply the quantumness witness for systems that are highly mixed but in the large-number limit that resembles nuclear magnetic resonance (NMR) systems. We make several conclusions about detecting quantumness in NMR-like systems.
Environmental and demographic risk factors for egg allergy in a population‐based study of infants
Authors: Koplin J. J. , Dharmage S. C. , Ponsonby A.‐L. , Tang M. L. K. , Lowe A. J. , Gurrin L. C. , Osborne N. J. , Martin P. E. , Robinson M. N. , Wake M. , Hill D. J. , Allen K. J. 
Published: September 2012

AbstractBackgroundAlthough egg allergy is the most common food allergy in infants and young children, risk factors for egg allergy remain largely unknown. This study examined the relationship between environmental and demographic factors and egg allergy in a population‐based infant cohort.MethodsIn a study of 5276 infants (HealthNuts), infants underwent skin prick testing (SPT) to egg white at 12 months of age. Questionnaire data on relevant exposures were obtained. 699/873 (80%) infants eligible for oral food challenge (detectable wheal on SPT) attended for formal assessment of egg allergy status; 453 had confirmed egg allergy (positive challenge and SPT ≥ 2 mm). Associations between environmental and demographic factors and egg allergy were investigated using multivariable logistic regression.ResultsChildren with older siblings and those with a pet dog at home were less likely to develop egg allergy by 1 year of age (adjusted OR [aOR], 0.72; 95% CI, 0.62, 0.83 per sibling; and aOR, 0.72; 95% CI, 0.52, 0.99, respectively). Caesarean section delivery, antibiotic use in infancy, childcare attendance and maternal age were not associated with egg allergy. History of allergic disease in an immediate family member and having parents born in East Asia were strong risk factors for infantile egg allergy (aOR, 1.82; 95% CI, 1.40, 2.36; and aOR, 3.30; 95% CI, 2.45, 4.45, respectively).ConclusionsExposure in the first year of life to siblings and dogs may decrease the risk of subsequent egg allergy. Infants with a family history of allergy and those with parents born in East Asia are at increased risk of egg allergy.
Magnetic resonance angiography‐defined intracranial vasculopathy is associated with silent cerebral infarcts and glucose‐6‐phosphate dehydrogenase mutation in children with sickle cell anaemia
Authors: Thangarajh Mathula , Yang Genyan , Fuchs Dana , Ponisio Maria R. , McKinstry Robert C. , Jaju Alok , Noetzel Michael J. , Casella James F. , Barron‐Casella Emily , Hooper W. Craig , Boulet Sheree L. , Bean Christopher J. , Pyle Meredith E. , Payne Amanda B. , Driggers Jennifer , Trau Heidi A. , Vendt Bruce A. , Rodeghier Mark , DeBaun Michael R. 
Published: September 2012

SummarySilent cerebral infarct (SCI) is the most commonly recognized cause of neurological injury in sickle cell anaemia (SCA). We tested the hypothesis that magnetic resonance angiography (MRA)‐defined vasculopathy is associated with SCI. Furthermore, we examined genetic variations in glucose‐6‐phosphate dehydrogenase (G6PD) and HBA (α‐globin) genes to determine their association with intracranial vasculopathy in children with SCA. Magnetic resonance imaging (MRI) of the brain and MRA of the cerebral vasculature were available in 516 paediatric patients with SCA, enrolled in the Silent Infarct Transfusion (SIT) Trial. All patients were screened for G6PD mutations and HBA deletions. SCI were present in 41·5% (214 of 516) of SIT Trial children. The frequency of intracranial vasculopathy with and without SCI was 15·9% and 6·3%, respectively (P < 0·001). Using a multivariable logistic regression model, only the presence of a SCI was associated with increased odds of vasculopathy (P = 0·0007, odds ratio (OR) 2·84; 95% Confidence Interval (CI) = 1·55–5·21). Among male children with SCA, G6PD status was associated with vasculopathy (P = 0·04, OR 2·78; 95% CI = 1·04–7·42), while no significant association was noted for HBA deletions. Intracranial vasculopathy was observed in a minority of children with SCA, and when present, was associated with G6PD status in males and SCI.
Clinical phase I study of elpamotide, a peptide vaccine for vascular endothelial growth factor receptor 2, in patients with advanced solid tumors
Authors: Okamoto Isamu , Arao Tokuzo , Miyazaki Masaki , Satoh Taroh , Okamoto Kunio , Tsunoda Takuya , Nishio Kazuto , Nakagawa Kazuhiko 
Published: September 2012

Targeting of tumor angiogenesis with vaccines is a potentially valuable approach to cancer treatment. Elpamotide is an immunogenic peptide derived from vascular endothelial growth factor receptor 2, which is expressed at a high level in vascular endothelial cells. We have now carried out a phase I study to evaluate safety, the maximum tolerated dose, and potential pharmacodynamic biomarkers for this vaccine. Ten HLA‐A*24:02‐positive patients with advanced refractory solid tumors received elpamotide s.c. at dose levels of 0.5, 1.0, or 2.0 mg once a week on a 28‐day cycle. Five patients experienced an injection site reaction of grade 1 and 2, which was the most frequent adverse event. In the 1.0 mg cohort, one patient experienced proteinuria of grade 1 and another patient developed both hypertension and proteinuria of grade 1. No adverse events of grade 3 or higher were observed, and the maximum tolerated dose was therefore not achieved. The serum concentration of soluble vascular endothelial growth factor receptor 2 decreased significantly after elpamotide vaccination. Microarray analysis of gene expression in PBMCs indicated that several pathways related to T cell function and angiogenesis were affected by elpamotide vaccination, supporting the notion that this peptide induces an immune response that targets angiogenesis in the clinical setting. In conclusion, elpamotide is well tolerated and our biomarker analysis indicates that this anti‐angiogenic vaccine is biologically active. Clinical trial registration no. UMIN000008336.
A randomized, controlled trial of narrow‐band imaging vs high‐definition white light for adenoma detection in patients at high risk of adenomas
Authors: East J. E. , Ignjatovic A. , Suzuki N. , Guenther T. , Bassett P. , Tekkis P. P. , Saunders B. P. 
Published: September 2012

AbstractAim  The study aimed to investigate whether narrow‐band imaging (NBI) can enhance adenoma detection in patients at high risk for adenomas compared with high‐definition white‐light endoscopy (WLE). High risk was defined as three or more adenomas at last colonoscopy, history of colorectal cancer and positive faecal occult blood test.Method  Two hundred and fourteen patients were randomized 1:1 to examination with NBI or WLE. The primary outcome measure was the proportion of patients with at least one adenoma detected. Secondary outcomes included total adenomas and polyps, flat adenomas, nonadenomatous polyps, advanced adenomas and patients with three or five or more adenomas. A post hoc analysis to examine the effect of endoscopist and bowel preparation was performed.Results  There was no significant difference in the proportion of patients with at least one adenoma: NBI 73%vs WLE 66%, odds ratio 1.40 (95% CI 0.78–2.52), P = 0.26. There was no significant difference for any secondary outcome measure except for the number of flat adenomas which was significantly greater with NBI [comparison ratio 2.66 (95% CI 1.52–4.63), P = 0.001]. Post hoc analysis indicated that one of three endoscopists performed significantly better for adenoma detection with NBI than WLE [comparison ratio 1.92 (95% CI 1.07–3.44), P = 0.03]. Good bowel preparation was associated with significantly improved adenoma detection with NBI [comparison ratio 1.55 (95% CI 1.01–2.22), P = 0.04] but not with fair preparation.Conclusion  Overall NBI did not improve detection compared with WLE in a group of patients at high risk for colorectal adenomas, but specific subgroups might benefit.