scholarly journals Ontoclick: a Chrome web browser extension to facilitate biomedical knowledge curation

2021 ◽  
Author(s):  
Anthony Xu ◽  
Aravind Venkateswaran ◽  
Lianguizi Zhou ◽  
Andreas Zankl
Keyword(s):  
Source Code ◽  
Biomedical Literature ◽  
Biomedical Knowledge ◽  
Web Browser ◽  
Ontology Term ◽  
Browser Extension ◽  
Widespread Adoption ◽  
User Friendly

Knowledge curation from the biomedical literature is very valuable but can be a repetitive and laborious process. The paucity of user-friendly tools is one of the reasons for the lack of widespread adoption of good biomedical knowledge curation practices. Here we present Ontoclick, a Chrome web browser extension that streamlines the process of annotating a text span with a relevant ontology term. We hope this tool will make biocuration more accessible to a wider audience of biomedical researchers. Ontoclick is freely available under the GPL-3.0 license on the Chrome Web Store. Source code and documentation are available at: https://github.com/azankl/Ontoclick Contact: [email protected]

scholarly journals LocusExplorer: a user-friendly tool for integrated visualization of human genetic association data and biological annotations

2015 ◽  
Vol 32 (6) ◽  
pp. 949-951 ◽  
Author(s):  
Tokhir Dadaev ◽  
Daniel A. Leongamornlert ◽  
Edward J. Saunders ◽  
Rosalind Eeles ◽  
Zsofia Kote-Jarai
Keyword(s):  
Genetic Association ◽  
Source Code ◽  
Biological Data ◽  
Single Image ◽  
Web Browser ◽  
File Formats ◽  
Exploration Tool ◽  
Association Data ◽  
User Friendly ◽  
Publication Quality

Abstract Summary: In this article, we present LocusExplorer, a data visualization and exploration tool for genetic association data. LocusExplorer is written in R using the Shiny library, providing access to powerful R-based functions through a simple user interface. LocusExplorer allows users to simultaneously display genetic, statistical and biological data for humans in a single image and allows dynamic zooming and customization of the plot features. Publication quality plots may then be produced in a variety of file formats. Availability and implementation: LocusExplorer is open source and runs through R and a web browser. It is available at www.oncogenetics.icr.ac.uk/LocusExplorer/ or can be installed locally and the source code accessed from https://github.com/oncogenetics/LocusExplorer. Contact: [email protected]

Semi-Automatic Online Tagging with K-Medoid Clustering

2014 ◽  
Vol 24 (08) ◽  
pp. 1115-1130 ◽  
Author(s):  
He Hu ◽  
Xiaoyong Du
Keyword(s):  
Clustering Algorithm ◽  
Prototype System ◽  
Web Pages ◽  
Web Page ◽  
Web Browser ◽  
User Input ◽  
Browser Extension ◽  
Annotation Process ◽  
Efficiency And Effectiveness ◽  
Automatic Mechanism

Online tagging is crucial for the acquisition and organization of web knowledge. We present TYG (Tag-as-You-Go) in this paper, a web browser extension for online tagging of personal knowledge on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can automatically group similar HTML DOM nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios.

Semantic-Based Indexing Approaches for Medical Document Clustering Using Cognitive Search

2019 ◽  
pp. 41-64
Author(s):  
Logeswari Shanmugam ◽  
Premalatha K.
Keyword(s):  
Text Processing ◽  
Relevant Information ◽  
Biomedical Literature ◽  
Data Sets ◽  
Original Text ◽  
Biomedical Knowledge ◽  
Text Data ◽  
Natural Language Text ◽  
Diverse Data ◽  
Medical Document

Biomedical literature is the primary repository of biomedical knowledge in which PubMed is the most absolute database for collecting, organizing and analyzing textual knowledge. The high dimensionality of the natural language text makes the text data quite noisy and sparse in the vector space. Hence, the data preprocessing and feature selection are important processes for the text processing issues. Ontologies select the meaningful terms semantically associated with the concepts from a document to reduce the dimensionality of the original text. In this chapter, semantic-based indexing approaches are proposed with cognitive search which makes use of domain ontology to extract relevant information from big and diverse data sets for users.

scholarly journals OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

2020 ◽  
Vol 36 (13) ◽  
pp. 4097-4098 ◽  
Author(s):  
Anna Breit ◽  
Simon Ott ◽  
Asan Agibetov ◽  
Matthias Samwald
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Source Code ◽  
Machine Learning Algorithms ◽  
Knowledge Networks ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Biomedical Knowledge ◽  
High Quality ◽  
Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Theodosios Theodosiou ◽  
Nikolaos Papanikolaou ◽  
Maria Savvaki ◽  
Giulia Bonetto ◽  
Stella Maxouri ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Computational Prediction ◽  
Biomedical Literature ◽  
Protein Network ◽  
Protein Protein Interactions ◽  
High Coverage ◽  
Depth Study ◽  
Experimental Approaches ◽  
Wet Lab ◽  
User Friendly

Abstract The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

scholarly journals Chemical–protein interaction extraction via Gaussian probability distribution and external biomedical knowledge

2020 ◽  
Vol 36 (15) ◽  
pp. 4323-4330 ◽  
Author(s):  
Cong Sun ◽  
Zhihao Yang ◽  
Leilei Su ◽  
Lei Wang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Protein Interactions ◽  
Local Structure ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Supplementary Information ◽  
Biomedical Knowledge ◽  
Proposed Model ◽  
Gaussian Probability Distribution ◽  
Extraction Performance

Abstract Motivation The biomedical literature contains a wealth of chemical–protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identify these CPIs. However, the local structure of sentences and external biomedical knowledge also contain valuable information. Effective use of such information may improve the performance of CPI extraction. Results In this article, we propose a novel neural network-based approach to improve CPI extraction. Specifically, the approach first employs BERT to generate high-quality contextual representations of the title sequence, instance sequence and knowledge sequence. Then, the Gaussian probability distribution is introduced to capture the local structure of the instance. Meanwhile, the attention mechanism is applied to fuse the title information and biomedical knowledge, respectively. Finally, the related representations are concatenated and fed into the softmax function to extract CPIs. We evaluate our proposed model on the CHEMPROT corpus. Our proposed model is superior in performance as compared with other state-of-the-art models. The experimental results show that the Gaussian probability distribution and external knowledge are complementary to each other. Integrating them can effectively improve the CPI extraction performance. Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks. Availability and implementation Data and code are available at https://github.com/CongSun-dlut/CPI_extraction. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Secureweb: Protecting sensitive information through the web browser extension with a security token

2018 ◽  
Vol 23 (5) ◽  
pp. 526-538 ◽  
Author(s):  
Shuang Liang ◽  
Yue Zhang ◽  
Bo Li ◽  
Xiaojie Guo ◽  
Chunfu Jia ◽  
...  
Keyword(s):  
Sensitive Information ◽  
Web Browser ◽  
Browser Extension ◽  
The Web

scholarly journals TeamTat: a collaborative text annotation tool

2020 ◽  
Vol 48 (W1) ◽  
pp. W5-W11
Author(s):  
Rezarta Islamaj ◽  
Dongseop Kwon ◽  
Sun Kim ◽  
Zhiyong Lu
Keyword(s):  
Project Managers ◽  
Biomedical Literature ◽  
Task Completion ◽  
Annotation Tool ◽  
Web Based ◽  
Domain Experts ◽  
Plain Text ◽  
Text Annotation ◽  
User Friendly ◽  
Input Format

Abstract Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.

scholarly journals Temporal network alignment via GoT-WAVE

2019 ◽  
Vol 35 (18) ◽  
pp. 3527-3529 ◽  
Author(s):  
David Aparício ◽  
Pedro Ribeiro ◽  
Tijana Milenković ◽  
Fernando Silva
Keyword(s):  
User Interface ◽  
State Of The Art ◽  
Source Code ◽  
Network Alignment ◽  
Supplementary Information ◽  
Temporal Network ◽  
Temporal Networks ◽  
Supplementary Data ◽  
Node Similarity ◽  
User Friendly

Abstract Motivation Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results On synthetic networks, GoT-WAVE improves DynaWAVE’s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.

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