scholarly journals COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database

2020 ◽  
Author(s):  
Daniel Korn ◽  
Tesia Bobrowski ◽  
Michael Li ◽  
Yaphet Kebede ◽  
Patrick Wang ◽  
...  
Keyword(s):  
Biomedical Literature ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Drug Candidates ◽  
Clinical Drug

<p>In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing ROBOKOP biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to test new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19. COVID-KOP is freely accessible at <a href="https://covidkop.renci.org/">https://covidkop.renci.org/</a>. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.</p>

scholarly journals COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database

2020 ◽  
Author(s):  
Daniel Korn ◽  
Tesia Bobrowski ◽  
Michael Li ◽  
Yaphet Kebede ◽  
Patrick Wang ◽  
...  
Keyword(s):  
Drug Action ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Drug Candidates ◽  
Clinical Drug

Abstract Summary In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to generate new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19 by establishing respective confirmatory pathways of drug action. Availability and implementation COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

scholarly journals COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database

2020 ◽  
Author(s):  
Daniel Korn ◽  
Tesia Bobrowski ◽  
Michael Li ◽  
Yaphet Kebede ◽  
Patrick Wang ◽  
...  
Keyword(s):  
Biomedical Literature ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Drug Candidates ◽  
Clinical Drug

<p>In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing ROBOKOP biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to test new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19. COVID-KOP is freely accessible at <a href="https://covidkop.renci.org/">https://covidkop.renci.org/</a>. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.</p>

scholarly journals Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19

2021 ◽  
Vol 12 ◽  
Author(s):  
Daniel P. Smith ◽  
Olly Oechsle ◽  
Michael J. Rawling ◽  
Ed Savory ◽  
Alix M.B. Lacoste ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Drug Repurposing ◽  
Standard Of Care ◽  
Biomedical Literature ◽  
Biological Research ◽  
Knowledge Graph ◽  
Neglected Diseases ◽  
Biomedical Knowledge ◽  
Computational Tools ◽  
Approved Drugs

The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.

scholarly journals A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development (Preprint)

2020 ◽  
Author(s):  
Jian Du ◽  
Xiaoying Li
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Clinical Practice ◽  
Practice Guidelines ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Combination Therapies ◽  
Combination Drug ◽  
Automated Knowledge ◽  
Semantic Predications

BACKGROUND Combination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders. OBJECTIVE This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines. METHODS Based on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S<sub>1</sub>-P-O and S<sub>i</sub>-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, “treat”) and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as “pharmacologic actions” and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation. RESULTS We retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching “antineoplastic agents” for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms “conclusion*” and “conclude*” ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers (“combin*,” “coadministration,” “co-administered,” and “regimen”) to identify potential combination therapies to enable development of a machine learning algorithm. CONCLUSIONS Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.

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.

Biomedical Knowledge Graph Embeddings for Personalized Medicine

2021 ◽  
pp. 584-595
Author(s):  
Joana Vilela ◽  
Muhammad Asif ◽  
Ana Rita Marques ◽  
João Xavier Santos ◽  
Célia Rasga ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Biomedical Knowledge

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 Tight binding enantiomers of pre-clinical drug candidates

2015 ◽  
Vol 23 (17) ◽  
pp. 5326-5333 ◽  
Author(s):  
Gary B. Evans ◽  
Scott A. Cameron ◽  
Andreas Luxenburger ◽  
Rong Guan ◽  
Javier Suarez ◽  
...  
Keyword(s):  
Tight Binding ◽  
Drug Candidates ◽  
Clinical Drug

scholarly journals A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development

10.2196/18323 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e18323
Author(s):  
Jian Du ◽  
Xiaoying Li
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Clinical Practice ◽  
Practice Guidelines ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Combination Therapies ◽  
Combination Drug ◽  
Automated Knowledge ◽  
Semantic Predications

Background Combination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders. Objective This paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines. Methods Based on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S1-P-O and Si-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, “treat”) and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as “pharmacologic actions” and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation. Results We retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching “antineoplastic agents” for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms “conclusion*” and “conclude*” ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers (“combin*,” “coadministration,” “co-administered,” and “regimen”) to identify potential combination therapies to enable development of a machine learning algorithm. Conclusions Semantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.

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