Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Bioinformatics
Title: Biotea-2-Bioschemas, facilitating structured markup for semantically annotated scholarly publications
Abstract: The total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata. Schema.org supports adding structured metadata to web pages via markup, making it easier for data providers but also for search engines to provide the right search results. Bioschemas is based on the standards of schema.org, providing new types, properties and guidelines for metadata, i.e., providing metadata profiles tailored to the Life Sciences domain. Here we present our proposed contribution to Bioschemas (from the project “Biotea”), which supports metadata contributions for scholarly publications via profiles and web components. Biotea comprises a semantic model to represent publications together with annotated elements recognized from the scientific text; our Biotea model has been mapped to schema.org following Bioschemas standards.
Extracted statement — Related Resource: Biotea
Research field: Bioinformatics
Title: LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions
Abstract: In order to access and filter content of life-science databases, full text search is a widely applied query interface. But its high flexibility and intuitiveness is paid for with potentially imprecise and incomplete query results. To reduce this drawback, query assistance systems suggest those combinations of keywords with the highest potential to match most of the relevant data records. Widespread approaches are syntactic query corrections that avoid misspelling and support expansion of words by suffixes and prefixes. Synonym expansion approaches apply thesauri, ontologies, and query logs. All need laborious curation and maintenance. Furthermore, access to query logs is in general restricted. Approaches that infer related queries by their query profile like research field, geographic location, co-authorship, affiliation etc. require user’s registration and its public accessibility that contradict privacy concerns. To overcome these drawbacks, we implemented LAILAPS-QSM, a machine learning approach that reconstruct possible linguistic contexts of a given keyword query. The context is referred from the text records that are stored in the databases that are going to be queried or extracted for a general purpose query suggestion from PubMed abstracts and UniProt data. The supplied tool suite enables the pre-processing of these text records and the further computation of customized distributed word vectors. The latter are used to suggest alternative keyword queries. An evaluated of the query suggestion quality was done for plant science use cases. Locally present experts enable a cost-efficient quality assessment in the categories trait, biological entity, taxonomy, affiliation, and metabolic function which has been performed using ontology term similarities. LAILAPS-QSM mean information content similarity for 15 representative queries is 0.70, whereas 34% have a score above 0.80. In comparison, the information content similarity for human expert made query suggestions is 0.90. The software is either available as tool set to build and train dedicated query suggestion services or as already trained general purpose RESTful web service. The service uses open interfaces to be seamless embeddable into database frontends. The JAVA implementation uses highly optimized data structures and streamlined code to provide fast and scalable response for web service calls. The source code of LAILAPS-QSM is available under GNU General Public License version 2 in Bitbucket GIT repository: https://bitbucket.org/ipk_bit_team/bioescorte-suggestion
Extracted statement — uses: Bitbucket
Research field: Bioinformatics
Title: Distinctive Gut Microbiota of Honey Bees Assessed Using Deep Sampling from Individual Worker Bees
Abstract: Surveys of 16S rDNA sequences from the honey bee, Apis mellifera, have revealed the presence of eight distinctive bacterial phylotypes in intestinal tracts of adult worker bees. Because previous studies have been limited to relatively few sequences from samples pooled from multiple hosts, the extent of variation in this microbiota among individuals within and between colonies and locations has been unclear. We surveyed the gut microbiota of 40 individual workers from two sites, Arizona and Maryland USA, sampling four colonies per site. Universal primers were used to amplify regions of 16S ribosomal RNA genes, and amplicons were sequenced using 454 pyrotag methods, enabling analysis of about 330,000 bacterial reads. Over 99% of these sequences belonged to clusters for which the first blastn hits in GenBank were members of the known bee phylotypes. Four phylotypes, one within Gammaproteobacteria (corresponding to “Candidatus Gilliamella apicola”) one within Betaproteobacteria (“Candidatus Snodgrassella alvi”), and two within Lactobacillus, were present in every bee, though their frequencies varied. The same typical bacterial phylotypes were present in all colonies and at both sites. Community profiles differed significantly among colonies and between sites, mostly due to the presence in some Arizona colonies of two species of Enterobacteriaceae not retrieved previously from bees. Analysis of Sanger sequences of rRNA of the Snodgrassella and Gilliamella phylotypes revealed that single bees contain numerous distinct strains of each phylotype. Strains showed some differentiation between localities, especially for the Snodgrassella phylotype.
Extracted statement — uses: blastn
Research field: Bioinformatics
Title: Distinctive Gut Microbiota of Honey Bees Assessed Using Deep Sampling from Individual Worker Bees
Abstract: Surveys of 16S rDNA sequences from the honey bee, Apis mellifera, have revealed the presence of eight distinctive bacterial phylotypes in intestinal tracts of adult worker bees. Because previous studies have been limited to relatively few sequences from samples pooled from multiple hosts, the extent of variation in this microbiota among individuals within and between colonies and locations has been unclear. We surveyed the gut microbiota of 40 individual workers from two sites, Arizona and Maryland USA, sampling four colonies per site. Universal primers were used to amplify regions of 16S ribosomal RNA genes, and amplicons were sequenced using 454 pyrotag methods, enabling analysis of about 330,000 bacterial reads. Over 99% of these sequences belonged to clusters for which the first blastn hits in GenBank were members of the known bee phylotypes. Four phylotypes, one within Gammaproteobacteria (corresponding to “Candidatus Gilliamella apicola”) one within Betaproteobacteria (“Candidatus Snodgrassella alvi”), and two within Lactobacillus, were present in every bee, though their frequencies varied. The same typical bacterial phylotypes were present in all colonies and at both sites. Community profiles differed significantly among colonies and between sites, mostly due to the presence in some Arizona colonies of two species of Enterobacteriaceae not retrieved previously from bees. Analysis of Sanger sequences of rRNA of the Snodgrassella and Gilliamella phylotypes revealed that single bees contain numerous distinct strains of each phylotype. Strains showed some differentiation between localities, especially for the Snodgrassella phylotype.
Extracted statement — uses: Blastn
Research field: Bioinformatics
Title: Ecological Genetics of Chinese Rhesus Macaque in Response to Mountain Building: All Things Are Not Equal
Abstract: Background Pliocene uplifting of the Qinghai-Tibetan Plateau (QTP) and Quaternary glaciation may have impacted the Asian biota more than any other events. Little is documented with respect to how the geological and climatological events influenced speciation as well as spatial and genetic structuring, especially in vertebrate endotherms. Macaca mulatta is the most widely distributed non-human primate. It may be the most suitable model to test hypotheses regarding the genetic consequences of orogenesis on an endotherm. Methodology and Principal Findings Using a large dataset of maternally inherited mitochondrial DNA gene sequences and nuclear microsatellite DNA data, we discovered two maternal super-haplogroups exist, one in western China and the other in eastern China. M. mulatta formed around 2.31 Ma (1.51–3.15, 95%), and divergence of the two major matrilines was estimated at 1.15 Ma (0.78–1.55, 95%). The western super-haplogroup exhibits significant geographic structure. In contrast, the eastern super-haplogroup has far greater haplotypic variability with little structure based on analyses of six variable microsatellite loci using Structure and Geneland. Analysis using Migrate detected greater gene flow from WEST to EAST than vice versa. We did not detect signals of bottlenecking in most populations. Conclusions Analyses of the nuclear and mitochondrial datasets obtained large differences in genetic patterns for M. mulatta. The difference likely reflects inheritance mechanisms of the maternally inherited mtDNA genome versus nuclear biparentally inherited STRs and male-mediated gene flow. Dramatic environmental changes may be responsible for shaping the matrilineal history of macaques. The timing of events, the formation of M. mulatta, and the divergence of the super-haplogroups, corresponds to both the uplifting of the QTP and Quaternary climatic oscillations. Orogenesis likely drove divergence of western populations in China, and Pleistocene glaciations are likely responsible for genetic structuring in the eastern super-haplogroup via geographic isolation and secondary contact.
Extracted statement — uses: Bottleneck
Research field: Bioinformatics
Title: CellProfiler 3.0: Next-generation image processing for biology
Abstract: CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.
Extracted statement — creates: CellProfiler
Research field: Bioinformatics
Title: CellProfiler 3.0: Next-generation image processing for biology
Abstract: CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.
Extracted statement — uses: CellProfiler
Research field: Bioinformatics
Title: Intrinsic Functional Connectivity in Salience and Default Mode Networks and Aberrant Social Processes in Youth at Ultra-High Risk for Psychosis
Abstract: Social processes are key to navigating the world, and investigating their underlying mechanisms and cognitive architecture can aid in understanding disease states such as schizophrenia, where social processes are highly impacted. Evidence suggests that social processes are impaired in individuals at ultra high-risk for the development of psychosis (UHR). Understanding these phenomena in UHR youth may clarify disease etiology and social processes in a period that is characterized by significantly fewer confounds than schizophrenia. Furthermore, understanding social processing deficits in this population will help explain these processes in healthy individuals. The current study examined resting state connectivity of the salience (SN) and default mode networks (DMN) in association with facial emotion recognition (FER), an integral aspect of social processes, as well as broader social functioning (SF) in UHR individuals and healthy controls. Consistent with the existing literature, UHR youth were impaired in FER and SF when compared with controls. In the UHR group, we found increased connectivity between the SN and the medial prefrontal cortex, an area of the DMN relative to controls. In UHR youth, the DMN exhibited both positive and negative correlations with the somatosensory cortex/cerebellum and precuneus, respectively, which was linked with better FER performance. For SF, results showed that sensory processing links with the SN might be important in allowing for better SF for both groups, but especially in controls where sensory processing is likely to be unimpaired. These findings clarify how social processing deficits may manifest in psychosis, and underscore the importance of SN and DMN connectivity for social processing more generally.
Extracted statement — uses: conn
Research field: Bioinformatics
Title: dcGOR: An R Package for Analysing Ontologies and Protein Domain Annotations
Abstract: I introduce an open-source R package ‘dcGOR’ to provide the bioinformatics community with the ease to analyse ontologies and protein domain annotations, particularly those in the dcGO database. The dcGO is a comprehensive resource for protein domain annotations using a panel of ontologies including Gene Ontology. Although increasing in popularity, this database needs statistical and graphical support to meet its full potential. Moreover, there are no bioinformatics tools specifically designed for domain ontology analysis. As an add-on package built in the R software environment, dcGOR offers a basic infrastructure with great flexibility and functionality. It implements new data structure to represent domains, ontologies, annotations, and all analytical outputs as well. For each ontology, it provides various mining facilities, including: (i) domain-based enrichment analysis and visualisation; (ii) construction of a domain (semantic similarity) network according to ontology annotations; and (iii) significance analysis for estimating a contact (statistical significance) network. To reduce runtime, most analyses support high-performance parallel computing. Taking as inputs a list of protein domains of interest, the package is able to easily carry out in-depth analyses in terms of functional, phenotypic and diseased relevance, and network-level understanding. More importantly, dcGOR is designed to allow users to import and analyse their own ontologies and annotations on domains (taken from SCOP, Pfam and InterPro) and RNAs (from Rfam) as well. The package is freely available at CRAN for easy installation, and also at GitHub for version control. The dedicated website with reproducible demos can be found at http://supfam.org/dcGOR.
Extracted statement — deposits: dcGOR
Research field: Bioinformatics
Title: Classifying semantic relations in bioscience texts
Abstract: A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities "treatment" and "disease" in bioscience text, and the problem of identifying such entities. We compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy.
Extracted statement — Concept types: Disease
Research field: Bioinformatics
Title: Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale
Abstract: The rapidly expanding body of available genomic and protein structural data provides a rich resource for understanding protein dynamics with biomolecular simulation. While computational infrastructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because software infrastructure to enable simulations at this scale has not kept pace. It should now be possible to study protein dynamics across entire (super)families, exploiting both available structural biology data and conformational similarities across homologous proteins. Here, we present a new tool for enabling high-throughput simulation in the genomics era. Ensembler takes any set of sequences - from a single sequence to an entire superfamily - and shepherds them through various stages of modeling and refinement to produce simulation-ready structures. This includes comparative modeling to all relevant PDB structures (which may span multiple conformational states of interest), reconstruction of missing loops, addition of missing atoms, culling of nearly identical structures, assignment of appropriate protonation states, solvation in explicit solvent, and refinement and filtering with molecular simulation to ensure stable simulation. The output of this pipeline is an ensemble of structures ready for subsequent molecular simulations using computer clusters, supercomputers, or distributed computing projects like Folding@home. Ensembler thus automates much of the time-consuming process of preparing protein models suitable for simulation, while allowing scalability up to entire superfamilies. A particular advantage of this approach can be found in the construction of kinetic models of conformational dynamics - such as Markov state models (MSMs) - which benefit from a diverse array of initial configurations that span the accessible conformational states to aid sampling. We demonstrate the power of this approach by constructing models for all catalytic domains in the human tyrosine kinase family, using all available kinase catalytic domain structures from any organism as structural templates. Ensembler is free and open source software licensed under the GNU General Public License (GPL) v2. It is compatible with Linux and OS X. The latest release can be installed via the conda package manager, and the latest source can be downloaded from https://github.com/choderalab/ensembler.
Extracted statement — creates: Ensembler
Research field: Bioinformatics
Title: Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale
Abstract: The rapidly expanding body of available genomic and protein structural data provides a rich resource for understanding protein dynamics with biomolecular simulation. While computational infrastructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because software infrastructure to enable simulations at this scale has not kept pace. It should now be possible to study protein dynamics across entire (super)families, exploiting both available structural biology data and conformational similarities across homologous proteins. Here, we present a new tool for enabling high-throughput simulation in the genomics era. Ensembler takes any set of sequences - from a single sequence to an entire superfamily - and shepherds them through various stages of modeling and refinement to produce simulation-ready structures. This includes comparative modeling to all relevant PDB structures (which may span multiple conformational states of interest), reconstruction of missing loops, addition of missing atoms, culling of nearly identical structures, assignment of appropriate protonation states, solvation in explicit solvent, and refinement and filtering with molecular simulation to ensure stable simulation. The output of this pipeline is an ensemble of structures ready for subsequent molecular simulations using computer clusters, supercomputers, or distributed computing projects like Folding@home. Ensembler thus automates much of the time-consuming process of preparing protein models suitable for simulation, while allowing scalability up to entire superfamilies. A particular advantage of this approach can be found in the construction of kinetic models of conformational dynamics - such as Markov state models (MSMs) - which benefit from a diverse array of initial configurations that span the accessible conformational states to aid sampling. We demonstrate the power of this approach by constructing models for all catalytic domains in the human tyrosine kinase family, using all available kinase catalytic domain structures from any organism as structural templates. Ensembler is free and open source software licensed under the GNU General Public License (GPL) v2. It is compatible with Linux and OS X. The latest release can be installed via the conda package manager, and the latest source can be downloaded from https://github.com/choderalab/ensembler.
Extracted statement — deposits: Ensembler
Research field: Bioinformatics
Title: Malaria knowledge and its associated factors among pregnant women attending antenatal clinic of Adis Zemen Hospital, North-western Ethiopia, 2018
Abstract: Introduction In Ethiopia, the burden of malaria during pregnancy remains a public health problem. Having a good malaria knowledge leads to practicing the prevention of malaria and seeking a health care. Researches regarding pregnant women’s knowledge on malaria in Ethiopia is limited. So the aim of this study was to assess malaria knowledge and its associated factors among pregnant woman, 2018. Methods An institutional-basedcross-sectional study was conducted in Adis Zemen Hospital. Data were collected using pre-tested, an interviewer-administered structured questionnaire among 236 mothers. Women’s knowledge on malaria was measured using six malaria-related questions (cause of malaria, mode of transmission, signs and symptoms, complication and prevention of malaria). The collected data were entered using Epidata version 3.1 and exported to SPSS version 20 for analysis. Bivariate and multivariate logistic regressions were computed to identify predictor variables at 95% confidence interval. Variables having P value of <0.05 were considered as predictor variables of malaria knowledge. Result A total of 235 pregnant women participated which makes the response rate 99.6%. One hundred seventy two pregnant women (73.2%) of mothers had good knowledge on malaria.Women who were from urban (AOR; 2.4: CI; 1.8, 5.7), had better family monthly income (AOR; 3.4: CI; 2.7, 3.8), attended education (AOR; 1.8: CI; 1.4, 3.5) were more knowledgeable. Conclusion and recommendation Majority of participants had good knowledge on malaria. Educational status, household monthly income and residence werepredictors of malaria knowledge. Increasing women’s knowledge especially for those who are from rural, have no education, and have low monthly income is still needed.
Extracted statement — uses: epidata