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Journal articles on the topic 'Online annotations'
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Gil de Gómez Pérez, David, and Roman Bednarik. "POnline: An Online Pupil Annotation Tool Employing Crowd-sourcing and Engagement Mechanisms." Human Computation 6 (December 10, 2019): 176–91. http://dx.doi.org/10.15346/hc.v6i1.99.
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Pupil center and pupil contour are two of the most important features in the eye-image used for video-based eye-tracking. Well annotated databases are needed in order to allow benchmarking of the available- and new pupil detection and gaze estimation algorithms. Unfortunately, creation of such a data set is costly and requires a lot of efforts, including manual work of the annotators. In addition, reliability of manual annotations is hard to establish with a low number of annotators. In order to facilitate progress of the gaze tracking algorithm research, we created an online pupil annotation tool that engages many users to interact through gamification and allows utilization of the crowd power to create reliable annotations \cite{artstein2005bias}. We describe the tool and the mechanisms employed, and report results on the annotation of a publicly available data set. Finally, we demonstrate an example utilization of the new high-quality annotation on a comparison of two state-of-the-art pupil center algorithms.
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Dijkshoorn, Chris, Victor De Boer, Lora Aroyo, and Guus Schreiber. "Accurator: Nichesourcing for Cultural Heritage." Human Computation 6 (June 2, 2019): 12–41. http://dx.doi.org/10.15346/hc.v6i1.91.
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With the increase of cultural heritage data published online, the usefulness of data in this open context hinges on the quality and diversity of descriptions of collection objects. In many cases, existing descriptions are not sufficient for retrieval and research tasks, resulting in the need for more specific annotations. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used to execute simple annotation tasks, identifying people with the required expertise might prove troublesome for more complex and domain-specific tasks. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is fourfold: 1) a nichesourcing methodology, 2) an annotation tool for experts, 3) validation of the methodology in three case studies and 4) a dataset including the obtained annotations. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high-quality annotations in a variety of domains. A user evaluation indicates the tool is suited and usable for domain-specific annotation tasks.
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Yssel, Anna E. J., Shu-Min Kao, Yves Van de Peer, and Lieven Sterck. "ORCAE-AOCC: A Centralized Portal for the Annotation of African Orphan Crop Genomes." Genes 10, no. 12 (November 20, 2019): 950. http://dx.doi.org/10.3390/genes10120950.
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ORCAE (Online Resource for Community Annotation of Eukaryotes) is a public genome annotation curation resource. ORCAE-AOCC is a branch that is dedicated to the genomes published as part of the African Orphan Crops Consortium (AOCC). The motivation behind the development of the ORCAE platform was to create a knowledge-based website where the research-community can make contributions to improve genome annotations. All changes to any given gene-model or gene description are stored, and the entire annotation history can be retrieved. Genomes can either be set to “public” or “restricted” mode; anonymous users can browse public genomes but cannot make any changes. Aside from providing a user- friendly interface to view genome annotations, the platform also includes tools and information (such as gene expression evidence) that enables authorized users to edit and validate genome annotations. The ORCAE-AOCC platform will enable various stakeholders from around the world to coordinate their efforts to annotate and study underutilized crops.
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Karavellas, Themistoklis, Anggarda Prameswari, Oana Inel, and Victor De Boer. "Local Crowdsourcing for Annotating Audio: the Elevator Annotator platform." Human Computation 6 (June 2, 2019): 1–11. http://dx.doi.org/10.15346/hc.v6i1.100.
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Crowdsourcing and other human computation techniques have proven useful in collecting large numbers of annotations for various datasets. In the majority of cases, online platforms are used when running crowdsourcing campaigns. Local crowdsourcing is a variant where annotation is done on specific physical locations. This paper describes a local crowdsourcing concept, platform and experiment. The case setting concerns eliciting annotations for an audio archive. For the experiment, we developed a hardware platform designed to be deployed in building elevators. To evaluate the effectiveness of the platform and to test the influence of location on the annotation results, an experiment was set up in two different locations. In each location two different user interaction modalities are used. The results show that our simple local crowdsourcing setup is able to achieve acceptable accuracy levels with up to 4 annotations per hour, and that the location has a significant effect on accuracy.
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"Build Your Online Networks Using Social Annotations." Journal of Artificial Intelligence 4, no. 2 (March 15, 2011): 169. http://dx.doi.org/10.3923/jai.2011.169.169.
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Davidson, John S. "Online Annotations to Robert Boyle's Sceptical Chymist." Journal of Chemical Education 80, no. 5 (May 2003): 487. http://dx.doi.org/10.1021/ed080p487.2.
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Rydell, Christopher, and Joakim Lindblad. "CytoBrowser: a browser-based collaborative annotation platform for whole slide images." F1000Research 10 (March 22, 2021): 226. http://dx.doi.org/10.12688/f1000research.51916.1.
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We present CytoBrowser, an open-source (GPLv3) JavaScript and Node.js driven environment for fast and accessible collaborative online visualization, assessment, and annotation of very large microscopy images, including, but not limited to, z-stacks (focus stacks) of cytology or histology whole slide images. CytoBrowser provides a web-based viewer for high-resolution zoomable images and facilitates easy remote collaboration, with options for joint-view visualization and simultaneous collaborative annotation of very large datasets. It delivers a unique combination of functionalities not found in other software solutions, making it a preferred tool for large scale annotation of whole slide image data. The web browser interface is directly accessible on any modern computer or even on a mobile phone, without need for additional software. By sharing a "session", several remote users can interactively explore and jointly annotate whole slide image data, thereby reaching improved data understanding and annotation quality, effortless project scaling and distribution of resources to/from remote locations, efficient creation of "ground truth" annotations for methods' evaluation and training of machine learning-based approaches, a user-friendly learning environment for medical students, to just name a few. Rectangle and polygon region annotations complement point-based annotations, each with a selectable annotation-class as well as free-form text fields. The default setting of CytoBrowser presents an interface for the Bethesda cancer grading system, while other annotation schemes can easily be incorporated. Automatic server side storage of annotations is complemented by JSON-based import/export options facilitating easy interoperability with other tools. CytoBrowser is available here: https://mida-group.github.io/CytoBrowser/.
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Tseng, Sheng-Shiang, Hui-Chin Yeh, and Shih-hsien Yang. "Promoting different reading comprehension levels through online annotations." Computer Assisted Language Learning 28, no. 1 (September 5, 2014): 41–57. http://dx.doi.org/10.1080/09588221.2014.927366.
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Warwick Vesztrocy, Alex, and Christophe Dessimoz. "Benchmarking gene ontology function predictions using negative annotations." Bioinformatics 36, Supplement_1 (July 1, 2020): i210—i218. http://dx.doi.org/10.1093/bioinformatics/btaa466.
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Abstract Motivation With the ever-increasing number and diversity of sequenced species, the challenge to characterize genes with functional information is even more important. In most species, this characterization almost entirely relies on automated electronic methods. As such, it is critical to benchmark the various methods. The Critical Assessment of protein Function Annotation algorithms (CAFA) series of community experiments provide the most comprehensive benchmark, with a time-delayed analysis leveraging newly curated experimentally supported annotations. However, the definition of a false positive in CAFA has not fully accounted for the open world assumption (OWA), leading to a systematic underestimation of precision. The main reason for this limitation is the relative paucity of negative experimental annotations. Results This article introduces a new, OWA-compliant, benchmark based on a balanced test set of positive and negative annotations. The negative annotations are derived from expert-curated annotations of protein families on phylogenetic trees. This approach results in a large increase in the average information content of negative annotations. The benchmark has been tested using the naïve and BLAST baseline methods, as well as two orthology-based methods. This new benchmark could complement existing ones in future CAFA experiments. Availability and Implementation All data, as well as code used for analysis, is available from https://lab.dessimoz.org/20_not. Supplementary information Supplementary data are available at Bioinformatics online.
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Semenzin, Chiara, Lisa Hamrick, Amanda Seidl, Bridgette L. Kelleher, and Alejandrina Cristia. "Describing Vocalizations in Young Children: A Big Data Approach Through Citizen Science Annotation." Journal of Speech, Language, and Hearing Research 64, no. 7 (July 16, 2021): 2401–16. http://dx.doi.org/10.1044/2021_jslhr-20-00661.
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Purpose Recording young children's vocalizations through wearables is a promising method to assess language development. However, accurately and rapidly annotating these files remains challenging. Online crowdsourcing with the collaboration of citizen scientists could be a feasible solution. In this article, we assess the extent to which citizen scientists' annotations align with those gathered in the lab for recordings collected from young children. Method Segments identified by Language ENvironment Analysis as produced by the key child were extracted from one daylong recording for each of 20 participants: 10 low-risk control children and 10 children diagnosed with Angelman syndrome, a neurogenetic syndrome characterized by severe language impairments. Speech samples were annotated by trained annotators in the laboratory as well as by citizen scientists on Zooniverse. All annotators assigned one of five labels to each sample: Canonical, Noncanonical, Crying, Laughing, and Junk. This allowed the derivation of two child-level vocalization metrics: the Linguistic Proportion and the Canonical Proportion. Results At the segment level, Zooniverse classifications had moderate precision and recall. More importantly, the Linguistic Proportion and the Canonical Proportion derived from Zooniverse annotations were highly correlated with those derived from laboratory annotations. Conclusions Annotations obtained through a citizen science platform can help us overcome challenges posed by the process of annotating daylong speech recordings. Particularly when used in composites or derived metrics, such annotations can be used to investigate early markers of language delays.
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Cebrián-de-la-Serna, Manuel, María Jesús Gallego-Arrufat, and Violeta Cebrián-Robles. "Multimedia Annotations for Practical Collaborative Reasoning." Journal of New Approaches in Educational Research 10, no. 2 (July 15, 2021): 264. http://dx.doi.org/10.7821/naer.2021.7.664.
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University education requires students to be trained both at university and at external internship centres. Because of Covid-19, the availability of multimedia resources and examples of practical contexts has become vital. Multimedia annotation can help students reflect on the professional world, collaborating and interacting with colleagues online. This study aims to encourage collaborative practical thinking by using new video annotation technologies. 274 students participated in an experiment of task design focusing on the analysis of a technology-based, award-winning educational innovation project. With mixed research design, qualitative and quantitative data exported from the video annotation platform used was collected and analysed. The results show differences in the quality and quantity of the answers: in the tasks with broad Folksonomy they are more numerous but more dispersed in their analysis, and vice versa. The quality of the answers given with narrow Folksonomy is also higher in both texts and videos modes. Producing multimedia annotations is a practical way to encourage students to practise reflective reasoning about the professional reality.
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Wu, Aihua. "Ranking Biomedical Annotations with Annotator’s Semantic Relevancy." Computational and Mathematical Methods in Medicine 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/258929.
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Biomedical annotation is a common and affective artifact for researchers to discuss, show opinion, and share discoveries. It becomes increasing popular in many online research communities, and implies much useful information. Ranking biomedical annotations is a critical problem for data user to efficiently get information. As the annotator’s knowledge about the annotated entity normally determines quality of the annotations, we evaluate the knowledge, that is, semantic relationship between them, in two ways. The first is extracting relational information from credible websites by mining association rules between an annotator and a biomedical entity. The second way is frequent pattern mining from historical annotations, which reveals common features of biomedical entities that an annotator can annotate with high quality. We propose a weighted and concept-extended RDF model to represent an annotator, a biomedical entity, and their background attributes and merge information from the two ways as the context of an annotator. Based on that, we present a method to rank the annotations by evaluating their correctness according to user’s vote and the semantic relevancy between the annotator and the annotated entity. The experimental results show that the approach is applicable and efficient even when data set is large.
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Yeh, Hui-Chin, Hsiu-Ting Hung, and Yu-Hsin Chiang. "The use of online annotations in reading instruction and its impact on students’ reading progress and processes." ReCALL 29, no. 1 (April 8, 2016): 22–38. http://dx.doi.org/10.1017/s0958344016000021.
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AbstractStudies suggest that the incorporation of online annotations in reading instruction can improve students’ reading comprehension. However, little research has addressed how students use online annotations in their reading processes and how such use may lead to their improvement. This study thus adopted Reciprocal Teaching (RT) as an instructional framework to support students’ reading comprehension progress and processes, facilitated by the use of online annotations. A total of 54 English language learners at a university were recruited to read with online annotations based on the RT procedure, namely predicting, clarifying, questioning, and summarizing. The data collected included the students’ scores on pre- and post-tests and their participation records in a collaborative learning environment. The results reveal that the students enhanced their reading comprehension after the intervention. Their reading processes were also analyzed, and the major differences between the groups making more and less progress were identified. Those who made more progress not only frequently reviewed their previously generated predictions, clarifications, questions, and summaries but also actively provided feedback to their peers in a reciprocal manner. Pedagogical implications and recommendations are discussed.
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Letunic, Ivica, and Peer Bork. "Interactive Tree Of Life (iTOL) v4: recent updates and new developments." Nucleic Acids Research 47, W1 (April 1, 2019): W256—W259. http://dx.doi.org/10.1093/nar/gkz239.
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Abstract The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. The current version introduces four new dataset types, together with numerous new features. Annotation options have been expanded and new control options added for many display elements. An interactive spreadsheet-like editor has been implemented, providing dataset creation and editing directly in the web interface. Font support has been rewritten with full support for UTF-8 character encoding throughout the user interface. Google Web Fonts are now fully supported in the tree text labels. iTOL v4 is the first tool which supports direct visualization of Qiime 2 trees and associated annotations. The user account system has been streamlined and expanded with new navigation options, and currently handles >700 000 trees from more than 40 000 individual users. Full batch access has been implemented allowing programmatic upload and export of trees and annotations.
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Timonen, Oskari, Mikko Särkkä, Tibor Fülöp, Anton Mattsson, Juha Kekäläinen, and Jussi Paananen. "Varanto: variant enrichment analysis and annotation." Bioinformatics 35, no. 17 (January 24, 2019): 3154–56. http://dx.doi.org/10.1093/bioinformatics/btz046.
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Abstract Summary Genome-wide association studies (GWAS) aim to identify associations of genetic variations such as single-nucleotide polymorphisms (SNPs) to a specific trait or a disease. Identifying common themes such as pathways, biological processes and diseases associations is needed to further explore and interpret these results. Varanto is a novel web tool for annotating, visualizing and analyzing human genetic variations using diverse data sources. Varanto can be used to query a set of input variations, retrieve their associated variation and gene level annotations, perform annotation enrichment analysis and visualize the results. Availability and implementation Varanto web app is developed with R and implemented as Shiny app with PostgreSQL database and is freely available at http://bioinformatics.uef.fi/varanto. Source code for the tool is available at https://github.com/oqe/varanto. Supplementary information Supplementary data are available at Bioinformatics online.
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Li, Kang, Lequan Yu, Shujun Wang, and Pheng-Ann Heng. "Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 775–83. http://dx.doi.org/10.1609/aaai.v34i01.5421.
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The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usually expensive and time-consuming to be obtained. Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e.g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity. To alleviate the learning difficulties caused by modality-specific appearance discrepancy, we first present an Image Alignment Module (IAM) to narrow the appearance gap between assistant and target modality data. We then propose a novel Mutual Knowledge Distillation (MKD) scheme to thoroughly exploit the modality-shared knowledge to facilitate the target-modality segmentation. To be specific, we formulate our framework as an integration of two individual segmentors. Each segmentor not only explicitly extracts one modality knowledge from corresponding annotations, but also implicitly explores another modality knowledge from its counterpart in mutual-guided manner. The ensemble of two segmentors would further integrate the knowledge from both modalities and generate reliable segmentation results on target modality. Experimental results on the public multi-class cardiac segmentation data, i.e., MM-WHS 2017, show that our method achieves large improvements on CT segmentation by utilizing additional MRI data and outperforms other state-of-the-art multi-modality learning methods.
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Cheplygina, Veronika, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, and Marleen de Bruijne. "Crowdsourcing airway annotations in chest computed tomography images." PLOS ONE 16, no. 4 (April 22, 2021): e0249580. http://dx.doi.org/10.1371/journal.pone.0249580.
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Measuring airways in chest computed tomography (CT) scans is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated scans for good performance. We investigate whether crowdsourcing can be used to gather airway annotations. We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall. After combining multiple crowd workers, we compare the measurements to those made by the experts in the original scans. Similar to our preliminary study, a large portion of the annotations were excluded, possibly due to workers misunderstanding the instructions. After excluding such annotations, moderate to strong correlations with the expert can be observed, although these correlations are slightly lower than inter-expert correlations. Furthermore, the results across subjects in this study are quite variable. Although the crowd has potential in annotating airways, further development is needed for it to be robust enough for gathering annotations in practice. For reproducibility, data and code are available online: http://github.com/adriapr/crowdairway.git.
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Liu, Zhaowen, Edmund T. Rolls, Zhi Liu, Kai Zhang, Ming Yang, Jingnan Du, Weikang Gong, et al. "Brain annotation toolbox: exploring the functional and genetic associations of neuroimaging results." Bioinformatics 35, no. 19 (March 11, 2019): 3771–78. http://dx.doi.org/10.1093/bioinformatics/btz128.
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Abstract Motivation Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. Results We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results. Availability and implementation BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat. Supplementary information Supplementary data are available at Bioinformatics online.
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Wei, Chih-Hsuan, Alexis Allot, Robert Leaman, and Zhiyong Lu. "PubTator central: automated concept annotation for biomedical full text articles." Nucleic Acids Research 47, W1 (May 22, 2019): W587—W593. http://dx.doi.org/10.1093/nar/gkz389.
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AbstractPubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.
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Kiesling, Scott F., Umashanthi Pavalanathan, Jim Fitzpatrick, Xiaochuang Han, and Jacob Eisenstein. "Interactional Stancetaking in Online Forums." Computational Linguistics 44, no. 4 (December 2018): 683–718. http://dx.doi.org/10.1162/coli_a_00334.
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Language is shaped by the relationships between the speaker/writer and the audience, the object of discussion, and the talk itself. In turn, language is used to reshape these relationships over the course of an interaction. Computational researchers have succeeded in operationalizing sentiment, formality, and politeness, but each of these constructs captures only some aspects of social and relational meaning. Theories of interactional stancetaking have been put forward as holistic accounts, but until now, these theories have been applied only through detailed qualitative analysis of (portions of) a few individual conversations. In this article, we propose a new computational operationalization of interpersonal stancetaking. We begin with annotations of three linked stance dimensions—affect, investment, and alignment—on 68 conversation threads from the online platform Reddit. Using these annotations, we investigate thread structure and linguistic properties of stancetaking in online conversations. We identify lexical features that characterize the extremes along each stancetaking dimension, and show that these stancetaking properties can be predicted with moderate accuracy from bag-of-words features, even with a relatively small labeled training set. These quantitative analyses are supplemented by extensive qualitative analysis, highlighting the compatibility of computational and qualitative methods in synthesizing evidence about the creation of interactional meaning.
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Ziem, Alexander, Johanna Flick, and Phillip Sandkühler. "The German Constructicon Project: Framework, methodology, resources." Lexicographica 35, no. 1 (December 1, 2019): 15–40. http://dx.doi.org/10.1515/lex-2019-0003.
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Abstract The paper introduces aims and efforts of the German Constructicon Project (GCP) (www.german-constructicon.de) hosted at the University of Düsseldorf. The ultimate goal of the project is not only to identify and describe oftentimes overlooked grammatical constructions, but also to offer comprehensive descriptions of them in a dictionary-like online repository. Each construction entry is intended to provide all relevant grammatical information necessary to understand and correctly use the respective grammatical construction. Based on constructicographical analyses of authentic corpus examples, the paper reports on the methodological foundations of GCP, most importantly the annotation categories used, as well as on the computational work routine yielding construction entries. The routine includes five consecutive steps, (i) subcorporation and preliminary analysis, (ii) parsing pipeline (automatic annotations of part of speech, phrase type and grammatical function), (iii) semantic annotations, (iv) semi-automatic analyses of the annotations and, finally, (v) compilation of construction entries. For illustration, we present the geschweige denn (‘let alone’) construction in its habitat of the negating_connector construction family. The paper concludes with outlining a full-fledged construction entry for geschweige denn in the German Constructicon.
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Wei, Xiaoqiong, Chengxin Zhang, Peter L. Freddolino, and Yang Zhang. "Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons." Bioinformatics 36, no. 16 (May 29, 2020): 4383–88. http://dx.doi.org/10.1093/bioinformatics/btaa548.
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Abstract Motivation Many protein function databases are built on automated or semi-automated curations and can contain various annotation errors. The correction of such misannotations is critical to improving the accuracy and reliability of the databases. Results We proposed a new approach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of annotation rates (RAR) for the same GO term across different taxonomic groups, where those with a relatively low RAR usually correspond to incorrect annotations. As an illustration, we applied the approach to 20 commonly studied species in two recent UniProt-GOA releases and identified 250 potential misannotations in the 2018-11-6 release, where only 25% of them were corrected in the 2019-6-3 release. Importantly, 56% of the misannotations are ‘Inferred from Biological aspect of Ancestor (IBA)’ which is in contradiction with previous observations that attributed misannotations mainly to ‘Inferred from Sequence or structural Similarity (ISS)’, probably reflecting an error source shift due to the new developments of function annotation databases. The results demonstrated a simple but efficient misannotation detection approach that is useful for large-scale comparative protein function studies. Availability and implementation https://zhanglab.ccmb.med.umich.edu/RAR. Supplementary information Supplementary data are available at Bioinformatics online.
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Langlotz, Tobias, Daniel Wagner, Alessandro Mulloni, and Dieter Schmalstieg. "Online Creation of Panoramic Augmented Reality Annotations on Mobile Phones." IEEE Pervasive Computing 11, no. 2 (February 2012): 56–63. http://dx.doi.org/10.1109/mprv.2010.69.
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Yeh, Shiou-Wen, and Jia-Jiunn Lo. "Using online annotations to support error correction and corrective feedback." Computers & Education 52, no. 4 (May 2009): 882–92. http://dx.doi.org/10.1016/j.compedu.2008.12.014.
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Segura, Joan, Ruben Sanchez-Garcia, C. O. S. Sorzano, and J. M. Carazo. "3DBIONOTES v3.0: crossing molecular and structural biology data with genomic variations." Bioinformatics 35, no. 18 (February 15, 2019): 3512–13. http://dx.doi.org/10.1093/bioinformatics/btz118.
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Abstract Motivation Many diseases are associated to single nucleotide polymorphisms that affect critical regions of proteins as binding sites or post translational modifications. Therefore, analysing genomic variants with structural and molecular biology data is a powerful framework in order to elucidate the potential causes of such diseases. Results A new version of our web framework 3DBIONOTES is presented. This version offers new tools to analyse and visualize protein annotations and genomic variants, including a contingency analysis of variants and amino acid features by means of a Fisher exact test, the integration of a gene annotation viewer to highlight protein features on gene sequences and a protein–protein interaction viewer to display protein annotations at network level. Availability and implementation The web server is available at https://3dbionotes.cnb.csic.es Supplementary information Supplementary data are available at Bioinformatics online. Contact Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, C/ Darwin nº 3, Campus of Cantoblanco, 28049 Madrid, Spain.
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Nikolaichik, Yevgeny, and Aliaksandr U. Damienikan. "SigmoID: a user-friendly tool for improving bacterial genome annotation through analysis of transcription control signals." PeerJ 4 (May 24, 2016): e2056. http://dx.doi.org/10.7717/peerj.2056.
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The majority of bacterial genome annotations are currently automated and based on a ‘gene by gene’ approach. Regulatory signals and operon structures are rarely taken into account which often results in incomplete and even incorrect gene function assignments. Here we present SigmoID, a cross-platform (OS X, Linux and Windows) open-source application aiming at simplifying the identification of transcription regulatory sites (promoters, transcription factor binding sites and terminators) in bacterial genomes and providing assistance in correcting annotations in accordance with regulatory information. SigmoID combines a user-friendly graphical interface to well known command line tools with a genome browser for visualising regulatory elements in genomic context. Integrated access to online databases with regulatory information (RegPrecise and RegulonDB) and web-based search engines speeds up genome analysis and simplifies correction of genome annotation. We demonstrate some features of SigmoID by constructing a series of regulatory protein binding site profiles for two groups of bacteria: Soft RotEnterobacteriaceae(PectobacteriumandDickeyaspp.) andPseudomonasspp. Furthermore, we inferred over 900 transcription factor binding sites and alternative sigma factor promoters in the annotated genome ofPectobacterium atrosepticum. These regulatory signals control putative transcription units covering about 40% of theP. atrosepticumchromosome. Reviewing the annotation in cases where it didn’t fit with regulatory information allowed us to correct product and gene names for over 300 loci.
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Tschöpe, Okka, Lutz Suhrbier, Anton Güntsch, and Walter Berendsohn. "AnnoSys – an online tool for sharing annotations to enhance data quality." Proceedings of TDWG 1 (August 15, 2017): e20315. http://dx.doi.org/10.3897/tdwgproceedings.1.20315.
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Wong, Nathan, and Xiaowei Wang. "miRDB: an online resource for microRNA target prediction and functional annotations." Nucleic Acids Research 43, no. D1 (November 5, 2014): D146—D152. http://dx.doi.org/10.1093/nar/gku1104.
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Zdobnov, Evgeny M., Dmitry Kuznetsov, Fredrik Tegenfeldt, Mosè Manni, Matthew Berkeley, and Evgenia V. Kriventseva. "OrthoDB in 2020: evolutionary and functional annotations of orthologs." Nucleic Acids Research 49, no. D1 (November 16, 2020): D389—D393. http://dx.doi.org/10.1093/nar/gkaa1009.
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Abstract OrthoDB provides evolutionary and functional annotations of orthologs, inferred for a vast number of available organisms. OrthoDB is leading in the coverage and genomic diversity sampling of Eukaryotes, Prokaryotes and Viruses, and the sampling of Bacteria is further set to increase three-fold. The user interface has been enhanced in response to the massive growth in data. OrthoDB provides three views on the data: (i) a list of orthologous groups related to a user query, which are now arranged to visualize their hierarchical relations, (ii) a detailed view of an orthologous group, now featuring a Sankey diagram to facilitate navigation between the levels of orthology, from more finely-resolved to more general groups of orthologs, as well as an arrangement of orthologs into an interactive organism taxonomy structure, and (iii) we added a gene-centric view, showing the gene functional annotations and the pair-wise orthologs in example species. The OrthoDB standalone software for delineation of orthologs, Orthologer, is freely available. Online BUSCO assessments and mapping to OrthoDB of user-uploaded data enable interactive exploration of related annotations and generation of comparative charts. OrthoDB strives to predict orthologs from the broadest coverage of species, as well as to extensively collate available functional annotations, and to compute evolutionary annotations such as evolutionary rate and phyletic profile. OrthoDB data can be assessed via SPARQL RDF, REST API, downloaded or browsed online from https://orthodb.org.
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Amgad, Mohamed, Habiba Elfandy, Hagar Hussein, Lamees A. Atteya, Mai A. T. Elsebaie, Lamia S. Abo Elnasr, Rokia A. Sakr, et al. "Structured crowdsourcing enables convolutional segmentation of histology images." Bioinformatics 35, no. 18 (February 6, 2019): 3461–67. http://dx.doi.org/10.1093/bioinformatics/btz083.
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Abstract Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. Results We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. Availability and Implementation Dataset is freely available at: https://goo.gl/cNM4EL. Supplementary information Supplementary data are available at Bioinformatics online.
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Ruble, Julie E., and Barbara Lom. "Online Protocol Annotation: A Method to Enhance Undergraduate Laboratory Research Skills." CBE—Life Sciences Education 7, no. 3 (September 2008): 296–301. http://dx.doi.org/10.1187/cbe.08-02-0007.
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A well-constructed, step-by-step protocol is a critical starting point for teaching undergraduates new techniques, an important record of a lab's standard procedures, and a useful mechanism for sharing techniques between labs. Many research labs use websites to archive and share their protocols for these purposes. Here we describe our experiences developing and using a protocol website for the additional purpose of enhancing undergraduate research training. We created our lab's protocol website in a message board format that allows undergraduates to post comments on protocols describing the lessons they learned, questions that arose, and/or insights they gained while learning to execute specific research protocols. Encouraging and expecting students to comment on the protocols they are learning to execute is beneficial for both the student and for the lab in which they are training. For the student, annotations encourage active reflection on their execution of techniques and emphasize the important message that attending to and understanding details of a protocol is a critical factor in producing reliable data. For the lab, annotations capture valuable insights for future generations of researchers by describing missing details, hints, and common hurdles for newcomers.
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Sharov, Alexei A., David Schlessinger, and Minoru S. H. Ko. "ExAtlas: An interactive online tool for meta-analysis of gene expression data." Journal of Bioinformatics and Computational Biology 13, no. 06 (December 2015): 1550019. http://dx.doi.org/10.1142/s0219720015500195.
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We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users’ own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher’s methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein–protein interaction) are pre-loaded and can be used for functional annotations.
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Wiesner, David, David Svoboda, Martin Maška, and Michal Kozubek. "CytoPacq: a web-interface for simulating multi-dimensional cell imaging." Bioinformatics 35, no. 21 (May 22, 2019): 4531–33. http://dx.doi.org/10.1093/bioinformatics/btz417.
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Abstract Motivation Objective assessment of bioimage analysis methods is an essential step towards understanding their robustness and parameter sensitivity, calling for the availability of heterogeneous bioimage datasets accompanied by their reference annotations. Because manual annotations are known to be arduous, highly subjective and barely reproducible, numerous simulators have emerged over past decades, generating synthetic bioimage datasets complemented with inherent reference annotations. However, the installation and configuration of these tools generally constitutes a barrier to their widespread use. Results We present a modern, modular web-interface, CytoPacq, to facilitate the generation of synthetic benchmark datasets relevant for multi-dimensional cell imaging. CytoPacq poses a user-friendly graphical interface with contextual tooltips and currently allows a comfortable access to various cell simulation systems of fluorescence microscopy, which have already been recognized and used by the scientific community, in a straightforward and self-contained form. Availability and implementation CytoPacq is a publicly available online service running at https://cbia.fi.muni.cz/simulator. More information about it as well as examples of generated bioimage datasets are available directly through the web-interface. Supplementary information Supplementary data are available at Bioinformatics online.
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Cary, Michael, Katie Podshivalova, and Cynthia Kenyon. "Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans’ Gene Expression Data." G3: Genes|Genomes|Genetics 10, no. 10 (August 5, 2020): 3623–38. http://dx.doi.org/10.1534/g3.120.401270.
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Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans. These modules represent responses to changes in the environment (e.g., starvation, exposure to xenobiotics), genes regulated by transcriptions factors (e.g., ATFS-1, DAF-16), genes specific to tissues (e.g., neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g., GO term). Analysis of “module-weighted annotations” improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource with tutorials at http://genemodules.org/, in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own gene expression data (generated using any platform) or gene sets of interest.
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Ylla, Guillem, Tianyuan Liu, and Ana Conesa. "MirCure: a tool for quality control, filter and curation of microRNAs of animals and plants." Bioinformatics 36, Supplement_2 (December 2020): i618—i624. http://dx.doi.org/10.1093/bioinformatics/btaa889.
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Abstract Motivation microRNAs (miRNAs) are essential components of gene expression regulation at the post-transcriptional level. miRNAs have a well-defined molecular structure and this has facilitated the development of computational and high-throughput approaches to predict miRNAs genes. However, due to their short size, miRNAs have often been incorrectly annotated in both plants and animals. Consequently, published miRNA annotations and miRNA databases are enriched for false miRNAs, jeopardizing their utility as molecular information resources. To address this problem, we developed MirCure, a new software for quality control, filtering and curation of miRNA candidates. MirCure is an easy-to-use tool with a graphical interface that allows both scoring of miRNA reliability and browsing of supporting evidence by manual curators. Results Given a list of miRNA candidates, MirCure evaluates a number of miRNA-specific features based on gene expression, biogenesis and conservation data, and generates a score that can be used to discard poorly supported miRNA annotations. MirCure can also curate and adjust the annotation of the 5p and 3p arms based on user-provided small RNA-seq data. We evaluated MirCure on a set of manually curated animal and plant miRNAs and demonstrated great accuracy. Moreover, we show that MirCure can be used to revisit previous bona fide miRNAs annotations to improve miRNA databases. Availability and implementation The MirCure software and all the additional scripts used in this project are publicly available at https://github.com/ConesaLab/MirCure. A Docker image of MirCure is available at https://hub.docker.com/r/conesalab/mircure. Supplementary information Supplementary data are available at Bioinformatics online.
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Chen, Zhanlin, Jing Zhang, Jason Liu, Yi Dai, Donghoon Lee, Martin Renqiang Min, Min Xu, and Mark Gerstein. "DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays." Bioinformatics 37, Supplement_1 (July 1, 2021): i280—i288. http://dx.doi.org/10.1093/bioinformatics/btab283.
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Abstract Motivation Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping. Results Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization. Availability and implementation DECODE source code and pre-processing scripts are available at decode.gersteinlab.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Ming, Jingsi, Tao Wang, and Can Yang. "LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations." Bioinformatics 36, no. 8 (December 20, 2019): 2506–14. http://dx.doi.org/10.1093/bioinformatics/btz947.
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Abstract Motivation Much effort has been made toward understanding the genetic architecture of complex traits and diseases. In the past decade, fruitful GWAS findings have highlighted the important role of regulatory variants and pervasive pleiotropy. Because of the accumulation of GWAS data on a wide range of phenotypes and high-quality functional annotations in different cell types, it is timely to develop a statistical framework to explore the genetic architecture of human complex traits by integrating rich data resources. Results In this study, we propose a unified statistical approach, aiming to characterize relationship among complex traits, and prioritize risk variants by leveraging regulatory information collected in functional annotations. Specifically, we consider a latent probit model (LPM) to integrate summary-level GWAS data and functional annotations. The developed computational framework not only makes LPM scalable to hundreds of annotations and phenotypes but also ensures its statistically guaranteed accuracy. Through comprehensive simulation studies, we evaluated LPM’s performance and compared it with related methods. Then, we applied it to analyze 44 GWASs with 9 genic category annotations and 127 cell-type specific functional annotations. The results demonstrate the benefits of LPM and gain insights of genetic architecture of complex traits. Availability and implementation The LPM package, all simulation codes and real datasets in this study are available at https://github.com/mingjingsi/LPM. Supplementary information Supplementary data are available at Bioinformatics online.
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Graveline, Laura. "Sources: Guide to Reference: Essential General Reference and Library Science Sources." Reference & User Services Quarterly 54, no. 4 (June 19, 2015): 72. http://dx.doi.org/10.5860/rusq.54n4.72b.
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ALA's classic print publication, Guide to Reference Books, was replaced in 2009 with the online Guide to Reference. As the online introduction states, the web subscription version of the Guide to Reference serves as a gateway with interactive features that the former print guide never could have replicated. Indeed, because print publications are by their nature hampered by size limitations, the online guide simply can provide more cross referencing, offer more extensive comparative evaluations and annotations of sources, and become more global in coverage.
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Senan, Oriol, Antoni Aguilar-Mogas, Miriam Navarro, Jordi Capellades, Luke Noon, Deborah Burks, Oscar Yanes, Roger Guimerà, and Marta Sales-Pardo. "CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network." Bioinformatics 35, no. 20 (March 23, 2019): 4089–97. http://dx.doi.org/10.1093/bioinformatics/btz207.
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Abstract Motivation The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample. Results Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools. Availability and implementation https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS. Supplementary information Supplementary data are available at Bioinformatics online.
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Shen, Chengchao, Xinchao Wang, Jie Song, Li Sun, and Mingli Song. "Amalgamating Knowledge towards Comprehensive Classification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3068–75. http://dx.doi.org/10.1609/aaai.v33i01.33013068.
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With the rapid development of deep learning, there have been an unprecedentedly large number of trained deep network models available online. Reusing such trained models can significantly reduce the cost of training the new models from scratch, if not infeasible at all as the annotations used for the training original networks are often unavailable to public. We propose in this paper to study a new model-reusing task, which we term as knowledge amalgamation. Given multiple trained teacher networks, each of which specializes in a different classification problem, the goal of knowledge amalgamation is to learn a lightweight student model capable of handling the comprehensive classification. We assume no other annotations except the outputs from the teacher models are available, and thus focus on extracting and amalgamating knowledge from the multiple teachers. To this end, we propose a pilot two-step strategy to tackle the knowledge amalgamation task, by learning first the compact feature representations from teachers and then the network parameters in a layer-wise manner so as to build the student model. We apply this approach to four public datasets and obtain very encouraging results: even without any human annotation, the obtained student model is competent to handle the comprehensive classification task and in most cases outperforms the teachers in individual sub-tasks.
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Debras, Camille. "How to prepare the video component of the Diachronic Corpus of Political Speeches for multimodal analysis." Research in Corpus Linguistics 9, no. 1 (2021): 132–51. http://dx.doi.org/10.32714/ricl.09.01.08.
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The Diachronic Corpus of Political Speeches (DCPS) is a collection of 1,500 full-length political speeches in English. It includes speeches delivered in countries where English is an official language (the US, Britain, Canada, Ireland) by English-speaking politicians in various settings from 1800 up to the present time. Enriched with semi-automatic morphosyntactic annotations and with discourse-pragmatic manual annotations, the DCPS is designed to achieve maximum representativeness and balance for political English speeches from major national English varieties in time, preserve detailed metadata, and enable corpus-based studies of syntactic, semantic and discourse-pragmatic variation and change on political corpora. For speeches given from 1950 onwards, video-recordings of the original delivery are often retrievable online. This opens up avenues of research in multimodal linguistics, in which studies on the integration of speech and gesture in the construction of meaning can include analyses of recurrent gestures and of multimodal constructions. This article discusses the issues at stake in preparing the video-recorded component of the DCPS for linguistic multimodal analysis, namely the exploitability of recordings, the segmentation and alignment of transcriptions, the annotation of gesture forms and functions in the software ELAN and the quantity of available gesture data.
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Katumba, S., and Sj Coetzee. "Enhancing the online discovery of geospatial data through taxonomy, folksonomy and semantic annotations." South African Journal of Geomatics 4, no. 3 (August 13, 2015): 339. http://dx.doi.org/10.4314/sajg.v4i3.14.
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Branavan, S. R. K., H. Chen, J. Eisenstein, and R. Barzilay. "Learning Document-Level Semantic Properties from Free-Text Annotations." Journal of Artificial Intelligence Research 34 (April 23, 2009): 569–603. http://dx.doi.org/10.1613/jair.2633.
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This paper presents a new method for inferring the semantic properties of documents by leveraging free-text keyphrase annotations. Such annotations are becoming increasingly abundant due to the recent dramatic growth in semi-structured, user-generated online content. One especially relevant domain is product reviews, which are often annotated by their authors with pros/cons keyphrases such as ``a real bargain'' or ``good value.'' These annotations are representative of the underlying semantic properties; however, unlike expert annotations, they are noisy: lay authors may use different labels to denote the same property, and some labels may be missing. To learn using such noisy annotations, we find a hidden paraphrase structure which clusters the keyphrases. The paraphrase structure is linked with a latent topic model of the review texts, enabling the system to predict the properties of unannotated documents and to effectively aggregate the semantic properties of multiple reviews. Our approach is implemented as a hierarchical Bayesian model with joint inference. We find that joint inference increases the robustness of the keyphrase clustering and encourages the latent topics to correlate with semantically meaningful properties. Multiple evaluations demonstrate that our model substantially outperforms alternative approaches for summarizing single and multiple documents into a set of semantically salient keyphrases.
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Dérozier, Sandra, Pierre Nicolas, Ulrike Mäder, and Cyprien Guérin. "Genoscapist: online exploration of quantitative profiles along genomes via interactively customized graphical representations." Bioinformatics 37, no. 17 (February 3, 2021): 2747–49. http://dx.doi.org/10.1093/bioinformatics/btab079.
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Abstract Summary Genoscapist is a tool to design web interfaces generating high-quality images for interactive visualization of hundreds of quantitative profiles along a reference genome together with various annotations. Relevance is demonstrated by deployment of two websites dedicated to large condition-dependent transcriptome datasets available for Bacillus subtilis and Staphylococcus aureus. Availability and implementation Websites and source code freely accessible at https://genoscapist.migale.inrae.fr
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Ruan, Peifeng, Ya Wang, Ronglai Shen, and Shuang Wang. "Using association signal annotations to boost similarity network fusion." Bioinformatics 35, no. 19 (February 19, 2019): 3718–26. http://dx.doi.org/10.1093/bioinformatics/btz124.
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Abstract Motivation Recent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data. Results In this article, we proposed an association-signal-annotation boosted similarity network fusion (ab-SNF) method, adding feature-level association signal annotations as weights aiming to up-weight signal features and down-weight noise features when constructing subject similarity networks to boost the performance in disease subtyping. In various simulation studies, the proposed ab-SNF outperforms the original SNF approach without weights. Most importantly, the improvement in the subtyping performance due to association-signal-annotation weights is amplified in the integration process. Applications to somatic mutation data, DNA methylation data and gene expression data of three cancer types from The Cancer Genome Atlas project suggest that the proposed ab-SNF method consistently identifies new subtypes in each cancer that more accurately predict patient survival and are more biologically meaningful. Availability and implementation The R package abSNF is freely available for downloading from https://github.com/pfruan/abSNF. Supplementary information Supplementary data are available at Bioinformatics online.
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Mahjoub, Mahiar, and Daphne Ezer. "PAFway: pairwise associations between functional annotations in biological networks and pathways." Bioinformatics 36, no. 19 (July 17, 2020): 4963–64. http://dx.doi.org/10.1093/bioinformatics/btaa639.
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Abstract Motivation Large gene networks can be dense and difficult to interpret in a biologically meaningful way. Results Here, we introduce PAFway, which estimates pairwise associations between functional annotations in biological networks and pathways. It answers the biological question: do genes that have a specific function tend to regulate genes that have a different specific function? The results can be visualized as a heatmap or a network of biological functions. We apply this package to reveal associations between functional annotations in an Arabidopsis thaliana gene network. Availability and implementation PAFway is submitted to CRAN. Currently available here: https://github.com/ezer/PAFway. Supplementary information Supplementary data are available at Bioinformatics online.
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Anderson, Warren D., Fabiana M. Duarte, Mete Civelek, and Michael J. Guertin. "Defining data-driven primary transcript annotations with primaryTranscriptAnnotation in R." Bioinformatics 36, no. 9 (January 9, 2020): 2926–28. http://dx.doi.org/10.1093/bioinformatics/btaa011.
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Abstract Summary Nascent transcript measurements derived from run-on sequencing experiments are critical for the investigation of transcriptional mechanisms and regulatory networks. However, conventional mRNA gene annotations significantly differ from the boundaries of primary transcripts. New primary transcript annotations are needed to accurately interpret run-on data. We developed the primaryTranscriptAnnotation R package to infer the transcriptional start and termination sites of primary transcripts from genomic run-on data. We then used these inferred coordinates to annotate transcriptional units identified de novo. This package provides the novel utility to integrate data-driven primary transcript annotations with transcriptional unit coordinates identified in an unbiased manner. Highlighting the importance of using accurate primary transcript coordinates, we demonstrate that this new methodology increases the detection of differentially expressed transcripts and provides more accurate quantification of RNA polymerase pause indices. Availability and implementation https://github.com/WarrenDavidAnderson/genomicsRpackage/tree/master/primaryTranscriptAnnotation. Supplementary information Supplementary data are available at Bioinformatics online.
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Lo, Jia-Jiunn, Shiou-Wen Yeh, and Chao-Shien Sung. "Learning paragraph structure with online annotations: An interactive approach to enhancing EFL reading comprehension." System 41, no. 2 (June 2013): 413–27. http://dx.doi.org/10.1016/j.system.2013.03.003.
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Yu, Guoxian, Keyao Wang, Carlotta Domeniconi, Maozu Guo, and Jun Wang. "Isoform function prediction based on bi-random walks on a heterogeneous network." Bioinformatics 36, no. 1 (June 28, 2019): 303–10. http://dx.doi.org/10.1093/bioinformatics/btz535.
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Abstract Motivation Alternative splicing contributes to the functional diversity of protein species and the proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions. Computationally predicting the functions of genes has been studied for decades. However, how to distinguish the functional annotations of isoforms, whose annotations are essential for understanding developmental abnormalities and cancers, is rarely explored. The main bottleneck is that functional annotations of isoforms are generally unavailable and functional genomic databases universally store the functional annotations at the gene level. Results We propose IsoFun to accomplish Isoform Function prediction based on bi-random walks on a heterogeneous network. IsoFun firstly constructs an isoform functional association network based on the expression profiles of isoforms derived from multiple RNA-seq datasets. Next, IsoFun uses the available Gene Ontology annotations of genes, gene–gene interactions and the relations between genes and isoforms to construct a heterogeneous network. After this, IsoFun performs a tailored bi-random walk on the heterogeneous network to predict the association between GO terms and isoforms, thus accomplishing the prediction of GO annotations of isoforms. Experimental results show that IsoFun significantly outperforms the state-of-the-art algorithms and improves the area under the receiver-operating curve (AUROC) and the area under the precision-recall curve (AUPRC) by 17% and 44% at the gene-level, respectively. We further validated the performance of IsoFun on the genes ADAM15 and BCL2L1. IsoFun accurately differentiates the functions of respective isoforms of these two genes. Availability and implementation The code of IsoFun is available at http://mlda.swu.edu.cn/codes.php? name=IsoFun. Supplementary information Supplementary data are available at Bioinformatics online.
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Sulimov, Pavel, Anastasia Voronkova, and Attila Kertész-Farkas. "Annotation of tandem mass spectrometry data using stochastic neural networks in shotgun proteomics." Bioinformatics 36, no. 12 (March 24, 2020): 3781–87. http://dx.doi.org/10.1093/bioinformatics/btaa206.
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Abstract Motivation The discrimination ability of score functions to separate correct from incorrect peptide-spectrum-matches in database-searching-based spectrum identification is hindered by many superfluous peaks belonging to unexpected fragmentation ions or by the lacking peaks of anticipated fragmentation ions. Results Here, we present a new method, called BoltzMatch, to learn score functions using a particular stochastic neural networks, called restricted Boltzmann machines, in order to enhance their discrimination ability. BoltzMatch learns chemically explainable patterns among peak pairs in the spectrum data, and it can augment peaks depending on their semantic context or even reconstruct lacking peaks of expected ions during its internal scoring mechanism. As a result, BoltzMatch achieved 50% and 33% more annotations on high- and low-resolution MS2 data than XCorr at a 0.1% false discovery rate in our benchmark; conversely, XCorr yielded the same number of spectrum annotations as BoltzMatch, albeit with 4–6 times more errors. In addition, BoltzMatch alone does yield 14% more annotations than Prosit (which runs with Percolator), and BoltzMatch with Percolator yields 32% more annotations than Prosit at 0.1% FDR level in our benchmark. Availability and implementation BoltzMatch is freely available at: https://github.com/kfattila/BoltzMatch. Contact akerteszfarkas@hse.ru Supporting information Supplementary data are available at Bioinformatics online.