scholarly journals Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

2018 ◽  
Author(s):  
Jake Lever ◽  
Martin R Jones ◽  
Arpad M Danos ◽  
Kilannin Krysiak ◽  
Melika Bonakdar ◽  
...  
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Full Text ◽  
Cancer Genomics ◽  
Improve Patient Care ◽  
Biomedical Literature ◽  
Precision Oncology ◽  
Manual Curation ◽  
Cancer Types ◽  
Improve Patient

Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated biomarkers and their clinical associations discussed in 800 sentences and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase (http://bionlp.bcgsc.ca/civicmine/) extracting 128,857 relevant sentences from PubMed abstracts and Pubmed Central Open Access full text papers. CIViCmine contains over 90,992 biomarkers associated with 7,866 genes, 402 drugs and 557 cancer types, representing 29,153 abstracts and 40,551 full-text publications. Through integration with CIVIC, we provide a prioritised list of curatable biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general.

scholarly journals Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jake Lever ◽  
Martin R. Jones ◽  
Arpad M. Danos ◽  
Kilannin Krysiak ◽  
Melika Bonakdar ◽  
...  
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Full Text ◽  
Cancer Genomics ◽  
Improve Patient Care ◽  
Cancer Biomarkers ◽  
Biomedical Literature ◽  
Precision Oncology ◽  
Creative Commons ◽  
Manual Curation

Abstract Background Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. Methods To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase. Results We extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications. Conclusions Through integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http://bionlp.bcgsc.ca/civicmine/.

scholarly journals Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language

2019 ◽  
Author(s):  
Charles Tapley Hoyt ◽  
Daniel Domingo-Fernández ◽  
Rana Aldisi ◽  
Lingling Xu ◽  
Kristian Kolpeja ◽  
...  
Keyword(s):  
Text Mining ◽  
Full Text ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Pubmed Central ◽  
Link Type ◽  
Information Density ◽  
Manual Curation ◽  
Rapid Accumulation ◽  
Knowledge Graphs

AbstractThe rapid accumulation of new biomedical literature not only causes curated knowledge graphs to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich knowledge graphs.We have developed two workflows: one for re-curating a given knowledge graph to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the knowledge graphs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.Database URLhttps://github.com/bel-enrichment/results

Accelerating Clinical Innovation in Biomedical Engineering Education by Using a Digital Portal for Collaboration

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Joel L. Berry ◽  
Kristen Noles ◽  
Alan Eberhardt ◽  
Nancy Wingo
Keyword(s):  
Open Access ◽  
Biomedical Engineering ◽  
Improve Patient Care ◽  
Digital Tool ◽  
Fluid Exchange ◽  
Continuous Innovation ◽  
Clinical Innovation ◽  
Stem Students ◽  
Improve Patient ◽  
Internal Funding

Abstract The rapidly changing healthcare landscape requires continuous innovation by clinicians, yet generating ideas to improve patient care is often problematic. This paper describes the development of a digital tool used in an interprofessional program designed to enhance collaborations between clinicians, undergraduate, and graduate STEM students, particularly biomedical engineering (BME). The program founders began by connecting clinicians and students through a course portal in a learning management system (LMS). They eventually secured internal funding to create an open access tool for posting and viewing problems, allowing interprofessional teams to rally around healthcare challenges and create prototypes for solving them. Results after three years of the program's inception have been encouraging, as teams have created devices and processes that have led to intellectual property disclosures, provisional patents, grant funding, and other productive interprofessional relationships. The open access tool has given clinicians and STEM students an outlet for convenient team formation around unsolved clinical problems and allowed a fluid exchange of ideas between participants across a variety of clinical disciplines.

scholarly journals ConsensusDriver improves upon individual algorithms for predicting driver alterations in different cancer types and individual patients – a toolbox for precision oncology

2017 ◽  
Author(s):  
Denis Bertrand ◽  
Sibyl Drissler ◽  
Burton Chia ◽  
Jia Yu Koh ◽  
Li Chenhao ◽  
...  
Keyword(s):  
Cancer Genomics ◽  
Clinical Phenotype ◽  
Genetic Alterations ◽  
Driver Gene ◽  
Prediction Methods ◽  
Precision Oncology ◽  
Cancer Driver ◽  
Recurrent Mutations ◽  
Cancer Types ◽  
Patient Subgroups

AbstractBackgroundIn recent years, several large-scale cancer genomics studies have helped generate detailed molecular profiling datasets for many cancer types and thousands of patients. These datasets provide a unique resource for studying cancer driver prediction methods and their utility for precision oncology, both to predict driver genetic alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.MethodsWe performed the most comprehensive assessment to date of 18 driver gene prediction methods, on more than 3,400 tumour samples, from 15 cancer types, to determine their suitability in guiding precision medicine efforts. These methods have diverse approaches, which can be classified into five categories:functionalimpact on proteins in general (FI) or specific tocancer (FIC),cohort-basedanalysis for recurrent mutations (CBA),mutations withexpressioncorrelation (MEC) and methods that use geneinteractionnetwork-basedanalysis (INA).ResultsThe performance of driver prediction methods varies considerably, with concordance with a gold-standard varying from 9% to 68%. FI methods show relatively poor performance (concordance <22%) while CBA methods provide conservative results, but require large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provide the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of drivers, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity). This tool can be applied to predict driver alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.ConclusionExisting cancer driver prediction methods are based on very different assumptions and each of them can only detect a particular subset of driver events. Consensus-based methods, like ConsensusDriver, are thus a promising approach to harness the strengths of different driver prediction paradigms.

scholarly journals ProPheno 1.0: An online dataset for accelerating the complete characterization of the human protein-phenotype landscape in biomedical literature

2019 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Indika Kahanda
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Predictive Models ◽  
Complete Characterization ◽  
Biomedical Literature ◽  
Human Protein ◽  
Pubmed Central ◽  
Mining Tool ◽  
Text Mining Tool

Identifying protein-phenotype relations is of paramount importance for applications such as uncovering rare and complex diseases. One of the best resources that captures the protein-phenotype relationships is the biomedical literature. In this work, we introduce ProPheno, a comprehensive online dataset composed of human protein/phenotype mentions extracted from the complete corpora of Medline and PubMed Central Open Access. Moreover, it includes co-occurrences of protein-phenotype pairs within different spans of text such as sentences and paragraphs. We use ProPheno for completely characterizing the human protein-phenotype landscape in biomedical literature. ProPheno, the reported findings and the gained insight has implications for (1) biocurators for expediting their curation efforts, (2) researches for quickly finding relevant articles, and (3) text mining tool developers for training their predictive models. The RESTful API of ProPheno is freely available at http://propheno.cs.montana.edu.

scholarly journals ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed

2021 ◽  
Vol 8 ◽  
Author(s):  
Paola Turina ◽  
Piero Fariselli ◽  
Emidio Capriotti
Keyword(s):  
Text Mining ◽  
Protein Stability ◽  
Full Text ◽  
Thermodynamic Data ◽  
Experimental Studies ◽  
Biomedical Literature ◽  
Automatic Identification ◽  
Thermodynamic Variables ◽  
Automatic Tool ◽  
The Impact

During the last years, the increasing number of DNA sequencing and protein mutagenesis studies has generated a large amount of variation data published in the biomedical literature. The collection of such data has been essential for the development and assessment of tools predicting the impact of protein variants at functional and structural levels. Nevertheless, the collection of manually curated data from literature is a highly time consuming and costly process that requires domain experts. In particular, the development of methods for predicting the effect of amino acid variants on protein stability relies on the thermodynamic data extracted from literature. In the past, such data were deposited in the ProTherm database, which however is no longer maintained since 2013. For facilitating the collection of protein thermodynamic data from literature, we developed the semi-automatic tool ThermoScan. ThermoScan is a text mining approach for the identification of relevant thermodynamic data on protein stability from full-text articles. The method relies on a regular expression searching for groups of words, including the most common conceptual words appearing in experimental studies on protein stability, several thermodynamic variables, and their units of measure. ThermoScan analyzes full-text articles from the PubMed Central Open Access subset and calculates an empiric score that allows the identification of manuscripts reporting thermodynamic data on protein stability. The method was optimized on a set of publications included in the ProTherm database, and tested on a new curated set of articles, manually selected for presence of thermodynamic data. The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts.Availability: The ThermoScan server is freely accessible online at https://folding.biofold.org/thermoscan. The ThermoScan python code and the Google Chrome extension for submitting visualized PMC web pages to the ThermoScan server are available at https://github.com/biofold/ThermoScan.

scholarly journals ProPheno 1.0: An online dataset for accelerating the complete characterization of the human protein-phenotype landscape in biomedical literature

2019 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Indika Kahanda
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Predictive Models ◽  
Complete Characterization ◽  
Biomedical Literature ◽  
Human Protein ◽  
Pubmed Central ◽  
Mining Tool ◽  
Text Mining Tool

Identifying protein-phenotype relations is of paramount importance for applications such as uncovering rare and complex diseases. One of the best resources that captures the protein-phenotype relationships is the biomedical literature. In this work, we introduce ProPheno, a comprehensive online dataset composed of human protein/phenotype mentions extracted from the complete corpora of Medline and PubMed Central Open Access. Moreover, it includes co-occurrences of protein-phenotype pairs within different spans of text such as sentences and paragraphs. We use ProPheno for completely characterizing the human protein-phenotype landscape in biomedical literature. ProPheno, the reported findings and the gained insight has implications for (1) biocurators for expediting their curation efforts, (2) researches for quickly finding relevant articles, and (3) text mining tool developers for training their predictive models. The RESTful API of ProPheno is freely available at http://propheno.cs.montana.edu.

scholarly journals Words Algorithm Collection - finding closely related open access books using text mining techniques

2021 ◽  
Vol 31 (1) ◽  
pp. 1-22
Author(s):  
Ronald Snijder
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Recommender Systems ◽  
Full Text ◽  
Book Publishing ◽  
Open Access Journal ◽  
Use Cases ◽  
Journal Articles ◽  
Everyday Language

Open access platforms and retail websites are both trying to present the most relevant offerings to their patrons. Retail websites deploy recommender systems that collect data about their customers. These systems are successful but intrude on privacy. As an alternative, this paper presents an algorithm that uses text mining techniques to find the most important themes of an open access book or chapter. By locating other publications that share one or more of these themes, it is possible to recommend closely related books or chapters. The algorithm splits the full text in trigrams. It removes all trigrams containing words that are commonly used in everyday language and in (open access) book publishing. The most occurring remaining trigrams are distinctive to the publication and indicate the themes of the book. The next step is finding publications that share one or more of the trigrams. The strength of the connection can be measured by counting – and ranking – the number of shared trigrams. The algorithm was used to find connections between 10,997 titles: 67% in English, 29% in German and 6% in Dutch or a combination of languages. The algorithm is able to find connected books across languages. It is possible use the algorithm for several use cases, not just recommender systems. Creating benchmarks for publishers or creating a collection of connected titles for libraries are other possibilities. Apart from the OAPEN Library, the algorithm can be applied to other collections of open access books or even open access journal articles. Combining the results across multiple collections will enhance its effectiveness.

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